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Treatment of HIV infection in injection drug users Tossonian, Haroutioun Krikor 2009

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TREATMENT OF HIV INFECTION IN INJECTION DRUG USERS  by Haroutioun Krikor Tossonian B.Sc., American University of Beirut, 1995 M.D., Kursk State Medical University, 1999  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE STUDIES (Pharmacology and Therapeutics)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) August 2009 © Haroutioun Krikor Tossonian, 2009  ABSTRACT  The treatment of HIV infection in injection drug users (IDUs) is limited by multiple barriers which could be addressed by using strategies based on directly observed therapy (DOT) or similar programs. This thesis evaluates a systematic approach of treatment within the context of an established methadone-based DOT program. First, we compared treatment responses with DOT relative to self-administered therapy (SAT) within a longitudinal cohort study. Higher rates of virologic suppression and retention on highly active antiretroviral therapy (HAART) were achieved with regimens taken as DOT over a period of 2 years. We also compared rates of emergence of drug resistance mutations (DRMs) with DOT relative to SAT. Although DOT did not prevent the emergence of DRMs, it did not lead to higher levels of resistance. We estimated the prevalence of primary drug resistance in our antiretroviral naive IDU cohort and found it to be relatively low (4.7%) but polymorphisms in the reverse transcriptase (RT) and protease genes were very common. Mutations at RT codon 135 (frequent in our cohort) were found to have no impact on treatment responses to non-nucleoside reverse transcriptase inhibitor (NNRTI)-based therapy. However, in patients experiencing virologic breakthrough and harboring such mutations, there was more evolution of single and less evolution of multiple NNRTI mutations. We measured the incidence of hepatotoxicity in IDUs receiving nevirapine-based HAART and compared it to that measured in non-IDUs. Hepatotoxicity was observed in 15% of participants in both IDUs and non-IDUs during the first year of therapy. Hepatitis C virus co-infection, being naive to HAART and abnormal baseline alanine aminotransferase levels were associated with higher risk of hepatotoxicity. Finally, we evaluated methadone dose adjustments and treatment responses after initiating HAART. Our results demonstrated that with nevirapine and efavirenz, moderate increases in methadone dosage were required to maintain the therapeutic benefit of opiate substitution therapy, compared to no change required in patients receiving regimens containing lopinavir or atazanavir. ii  Taken together, our data demonstrate that programmatic interventions including DOT are effective in managing HIV-infected IDUs with few if any drawbacks in terms of drug resistance, drug toxicity or interactions with other therapeutic interventions.  iii  TABLE OF CONTENTS  Abstract ..........................................................................................................................................ii Table of Contents ........................................................................................................................ iv List of Tables ................................................................................................................................vii List of Figures ............................................................................................................................ viii List of Abbreviations ....................................................................................................................ix Acknowledgements .......................................................................................................................xi Dedication .................................................................................................................................. xiii Co-authorship Statement ...........................................................................................................xiv CHAPTER I : Introduction to HIV Infection in Injection Drug Users ...................................1 1.1  Injection Drug Use and the Epidemic of HIV infection ............................................ 1  1.2  Epidemiology of HIV Infection in Injection Drug Users .......................................... 1  1.3 Natural History of HIV Infection in Injection Drug Users ...................................... 3 1.3.1 Introduction ..............................................................................................................3 1.3.2 Acute/Early Infection ...............................................................................................5 1.3.3 Chronic Infection ......................................................................................................6 1.3.4 AIDS ...........................................................................................................................8 1.4 Treatment of HIV Infection in Injection Drug Users ............................................. 10 1.4.1 Introduction ............................................................................................................10 1.4.2 Antiretroviral Drugs ...............................................................................................11 1.4.2.1 Nucleoside Reverse Transcriptase Inhibitors ................................................... 11 1.4.2.2 Non-Nucleoside Reverse Transcriptase Inhibitors .......................................... 12 1.4.2.3 Protease Inhibitors ........................................................................................... 13 1.4.2.4 Other Antiretroviral Drugs ............................................................................... 14 1.4.3 HIV Drug Resistance ..............................................................................................15 1.4.3.1 Introduction ......................................................................................................15 1.4.3.2 Resistance Testing .............................................................................................16 1.4.3.3 Resistance to Antiretroviral Drugs ...................................................................17 1.4.3.4 Primary Drug Resistance in Injection Drug Users ...........................................19 1.4.3.5 Secondary Drug Resistance in Injection Drug Users .......................................20 1.4.4 Barriers to HIV Treatment.....................................................................................21 1.4.4.1 Introduction ...................................................................................................... 21 1.4.4.2 Access to Care .................................................................................................. 21 1.4.4.3 Adherence ......................................................................................................... 22 1.4.4.4 Co-infections .................................................................................................... 23 1.4.4.5 Adverse Events ................................................................................................. 24 1.4.4.6 Drug Interactions .............................................................................................. 25 iv  1.4.5 Treatment Strategies ...............................................................................................26 1.4.5.1 Introduction ...................................................................................................... 26 1.4.5.2 Directly Observed Therapy .............................................................................. 26 1.4.5.3 Doubts about Directly Observed Therapy ........................................................ 27 1.5  Summary and Key Gaps ............................................................................................ 29  1.6  Purpose and Specific Aims......................................................................................... 31  1.7  References ................................................................................................................... 32  CHAPTER II: Treatment of HIV Infection in Injection Drug Users: Directly Observed Therapy and Self Administered Therapy..................................................................................57 2.1  Introduction ............................................................................................................... 57  2.2  Patients and Methods ................................................................................................. 58  2.3  Results ......................................................................................................................... 61  2.4  Discussion ................................................................................................................... 63  2.5  References ................................................................................................................... 74  CHAPTER III: HIV Drug Resistance in Injection Drug Users Receiving HAART within a Directly Observed Therapy Program .....................................................................................78 3.1  Introduction ............................................................................................................... 78  3.2  Patients and Methods ................................................................................................. 79  3.3  Results ......................................................................................................................... 81  3.4  Discussion ................................................................................................................... 83  3.5  References ................................................................................................................... 92  CHAPTER IV: Primary Drug Resistance in Antiretroviral Naïve Injection Drug Users ........................................................................................................................................................95 4.1  Introduction ............................................................................................................... 95  4.2  Patients and Methods ................................................................................................. 95  4.3  Results ......................................................................................................................... 97  4.4  Discussion ................................................................................................................... 99  4.5  References ................................................................................................................. 106  CHAPTER V: Clinical Implications of Mutations at Reverse Transcriptase Codon 135 on Response to NNRTI-Based Therapy...................................................................................110 5.1  Introduction ............................................................................................................. 110  5.2  Patients and Methods ............................................................................................... 111  5.3  Results ....................................................................................................................... 112  5.4  Discussion ................................................................................................................. 113  5.5  References ................................................................................................................. 121 v  CHAPTER VI: Hepatotoxicity in Injection Drug Users Receiving Nevirapine-Based HAART ……..............................................................................................................................123 6.1  Introduction ............................................................................................................. 123  6.2  Patients and Methods ............................................................................................... 123  6.3  Results ....................................................................................................................... 125  6.4  Discussion ................................................................................................................. 137  6.5  References ................................................................................................................. 136  CHAPTER VII: Methadone Dosing Strategies in HIV-Infected Injection Drug Users Enrolled in a Directly Observed Therapy Program ..............................................................138 6.1  Introduction ............................................................................................................. 138  6.2  Patients and Methods ............................................................................................... 139  6.3  Results ....................................................................................................................... 140  6.4  Discussion ................................................................................................................. 141  6.5  References ................................................................................................................. 156  CHAPTER VIII: Conclusions and Recommendations for Future Work ............................148 6.1  Introduction ............................................................................................................. 148  6.2  General Conclusions................................................................................................. 149  6.3  Overall Significance and Future Directions ........................................................... 153  6.4  Closing Remarks ...................................................................................................... 156  6.5  References …………………………………………………………...….…………..157  Appendix ....................................................................................................................................161  vi  LIST OF TABLES  Table 2.1 Baseline patient characteristics ...................................................................................67 Table 2.2 Factors associated with being on a directly observed therapy regimen ......................68 Table 2.3 Treatment outcomes at months 6, 12 and 24 ..............................................................69 Table 2.4 Causes and rates of treatment discontinuation at months 6, 12 and 24 ......................70 Table 2.5 Factors associated with virologic suppression at months 6, 12 and 24 ......................71 Table 2.6 Factors associated with retention on HAART ............................................................72 Table 3.1 Baseline patient characteristics ...................................................................................86 Table 3.2 Factors associated with being on a directly observed therapy regimen ......................87 Table 3.3 Mean rates of accumulation of total drug resistance mutations ..................................88 Table 3.4 Mean rates of accumulation of drug resistance mutations according to class ............89 Table 3.5 Factors associated with accumulation of drug resistance mutations ..........................90 Table 4.1 Baseline patient characteristics .................................................................................103 Table 4.2 Prevalence of primary genotypic and phenotypic drug resistance ............................104 Table 4.3 Patients with primary genotypic and phenotypic resistance .....................................105 Table 5.1 Baseline patient characteristics .................................................................................117 Table 5.2 Rates of drug resistance following virologic breakthrough on NNRTI-based HAART ......................................................................................................................................118 Table 6.1 Baseline patient characteristics .................................................................................129 Table 6.2 Causes and rates of treatment discontinuation at month 12 ......................................130 Table 6.3 Factors asssociated with grade 3 and 4 hepatotoxicity in a multivariable logistic regression analysis before model selection .................................................................................131 Table 6.4 Factors associated with grade 3 and 4 hepatotoxicity in a multivariable logistic regression analysis after model selection ...................................................................................132 Table 7.1 Methadone dose adjustments .....................................................................................143 Table 7.2 Antiretroviral efficacy at most recent follow-up ......................................................144  vii  LIST OF FIGURES  Figure 2.1 Flow chart of study patients and regimens ................................................................73 Figure 3.1 Flow chart of study patients and regimens.................................................................91 Figure 5.1 Median CD4 cell counts and median increases in CD4 cell counts at the latest follow-up visit in patients with and without mutations at codon 135..........................................119 Figure 5.2 Virologic suppression at the latest follow-up visit in patients with and without mutations at codon 135................................................................................................................120 Figure 6.1 Median alanine aminotransferase and aspartate aminotransferase levels during the first 12 months of treatment in injection drug users (IDUs) and non-IDUs .........................133 Figure 6.2 Alanine aminotransferase and aspartate aminotransferase elevations: Grade 3 and 4 hepatotoxicity in injection drug users (IDUs) and non-IDUs ...........................................134 Figure 6.3 Grade 3 and 4 hepatotoxicity: Alanine aminotransferase and/or aspartate aminotransferase elevations at any time point in injection drug users (IDUs) & non-IDUs ......135 Figure 7.1 Change from baseline in methadone dose by regimen type ....................................145  viii  LIST OF ABBREVIATIONS 3TC ABC AIDS ALT APV ARV AST ATV AZT BCO BID BL CCO CCO1 CCO2 CCR5 CD4 CD8 CI CTL CXCR4 D4T DAART DDI DOT DLV DNA DRM DRV EFV EIA ELISA env ETV FTC FU gag GEE HAART HBV HCV HIV HLA  Lamivudine Abacavir Acquired immune deficiency syndrome Alanine aminotransferase Fosamprenavir Antiretroviral Aspartate aminotransferase Atazanavir Zidovudine Biological cut off Twice-daily dosing Baseline Clinical cut off Lower clinical cut off Upper clinical cut off Chemokine (C-C motif) receptor 5 Cluster of differentiation 4 (cell receptor) Cluster of differentiation 8 (cell receptor) Confidence Interval Cytotoxic T lymphocyte Chemokine (CCX motif) receptor 4 Stavudine Directly administered antiretroviral therapy Didanosine Directly observed therapy Delavirdine Deoxyribonucleic acid Drug resistance mutation Darunavir Efavirenz Enzyme immunoassay Enzyme-linked immunosorbent assay Envelope Etravirine Emtricitabine Follow-up Group-specific antigen Generalized estimating equation Highly active antiretroviral therapy Hepatitis B virus Hepatitis C virus Human immunodeficiency virus Human leukocyte antigen  ix  IAS IC50 IDU IDV IQR IU LPV LPV/r MHC MSM N, n NAM NFV NNRTI NRTI NVP OR P-gp PHAC PI pol Q1 Q3 QD RNA RR RT RTV SAT SD SE SQV TAM T-cell TDF TID TPV ULN UNAIDS χ2  International AIDS Society Inhibitory concentration inhibiting replication by 50% Injection drug user; injection drug use Indinavir Interquartile range International unit Lopinavir Lopinavir/ritonavir Major histocompatibility complex Men who had sex with men Number Nucleoside analogue mutation Nelfinavir Non-nucleoside reverse transcriptase inhibitor Nucleoside reverse transcriptase inhibitor Nevirapine Odds ratio P-glycoprotein Public Health Agency of Canada Protease inhibitor Polymerase First quartile Third interquartile Once-daily dosing Ribonucleic acid Relative rate Reverse transcriptase Ritonavir Self-administered therapy Standard deviation Standard error Saquinavir Thymidine analogue mutation T-lymphocyte Tenofovir Thrice-daily dosing Tipranavir Upper limit of normal United Nations Program on HIV/AIDS Chi-squared  x  ACKNOWLEDGEMENTS My time as a graduate student has been a valuable learning experience. First, my biggest acknowledgement and thanks goes to my supervisor, Dr. Brian Conway. Through his guidance, I have developed a stronger focus and achieved considerable development as an individual and a scientist. He has provided me with opportunities that very few graduate students experience, which have significantly developed my understanding and capacity as a researcher. I owe him a great sense of gratitude and thanks for the knowledge and experiences he has shared with me and for his confidence in my abilities, his encouragement, support and never-ending patience.  Second, I must thank my friend and colleague, Mr. Jesse Raffa. Without him, much of this work could not have been possible. His continuous statistical expertise and patience has enhanced my knowledge and helped me to become a better researcher. I deeply appreciate everything he has done for me and I look forward to continue working with him in the field of HIV research.  I must also thank Dr. Robert Sindelar, Dr. Ismail Laher and Dr. Robert Reynolds. As members of my supervisory committee, they have helped my research work with their continuous guidance, patience and encouragement. Their involvement is greatly appreciated. In addition, I must thank all staff at the Downtown Infectious Diseases Clinic as well as the Department of Anesthesiology, Pharmacology and Therapeutics at the University of British Columbia where I have been a graduate student for more than 6 years. The continuous support of staff and faculty members of the Department has been really important during all these years.  My research was mostly conducted at the Pender Community Health Centre (PCHC) on the Downtown Eastside of Vancouver where I had the opportunity to learn and work with an exceptional group of trainees, physicians, nurses, counselors and administrative staff. Most importantly, I want to thank all the patients at the Clinic for allowing me to include them in my research. I would also like to thank Dr. Jason Grebely and Behroz Rashidi, for their help and hard work. In addition, I must thank all the doctors at the PCHC including Dr. Stanley DeVlaming, Dr. Mark Viljoen, Dr. Milan Khara, Dr. Annabel Mead, Dr. Fiona Duncan, Dr. Mark McLean, Dr. Ashok Krishnamurthy, Dr. Scott MacDonald and Dr. Cassandra Smith and xi  all the nurses including Natasha Suvorova, Lian McKenzie, Jennifer Quesnelle, Ken Vincent and Lesley Gallagher. Lastly, I must thank all administrative and front desk staff as well as addiction counselors at the Clinic. All these people have provided me with the all resources and support necessary for me to conduct my research at the PCHC. Without their support, none of this work would have been possible. Therefore, I owe the Clinic a great deal of gratitude and thanks. A deep gratitude goes to my mother, sister, brothers and friends, who throughout my life have continuously helped me towards reaching my goals. I must also thank Armenian communities in different countries around the world for the financial and moral support and being there for me whenever I was in need of help. I must not forget also to thank my new homeland, Canada, for accepting me as a loyal citizen and giving me all the support and opportunities to succeed in life. Finally, I would like to acknowledge the Canadian Institutes of Health Research, Boehringer Ingelheim Canada and the University of British Columbia for providing me with financial support as training salary or fellowship awards.  xii  DEDICATION  To My Parents This dissertation is dedicated to my beloved mother, Alice Sisilian and to the loving memory of my father, Krikor Tossonian.  xiii  CO-AUTHORSHIP STATEMENT  CHAPTER II: Treatment of HIV Infection in Injection Drug Users: Directly Observed Therapy and Self Administered Therapy Brian Conway, Stanley DeVlaming, Mark Viljoen, Annabel Mead, Milan Khara, Mark McLean, Fiona Duncan and Cassandra Smith were responsible for the development and recruitment of the cohort described in this paper. Harout K. Tossonian developed the experimental approach together with Jesse D. Raffa, Jason Grebely and Brian Conway. Harout K. Tossonian performed necessary data collection and database management tasks and conducted the analysis of research in collaboration with Jesse D. Raffa, who performed the detailed statistical analysis. Harout K. Tossonian wrote this chapter which was revised by Jesse D. Raffa and Brian Conway.  CHAPTER III: HIV Drug Resistance in Injection Drug Users Receiving HAART within a Directly Observed Therapy Program Brian Conway, Stanley DeVlaming, Mark Viljoen, Annabel Mead, Milan Khara, Mark McLean, Fiona Duncan and Cassandra Smith were responsible for the development and recruitment of the cohort described in this paper. Harout K. Tossonian developed the experimental approach together with Jesse D. Raffa, Jason Grebely and Brian Conway. Harout K. Tossonian performed necessary data collection and database management tasks and conducted the analysis of research in collaboration with Jesse D. Raffa, who performed the detailed statistical analysis. Harout K. Tossonian wrote this chapter which was revised by Jesse D. Raffa and Brian Conway.  CHAPTER IV: Primary Drug Resistance in Antiretroviral Naïve Injection Drug Users Harout K. Tossonian was responsible for developing the experimental approach together with Brian Conway, Jesse D. Raffa and Jason Grebely. Mark Viljoen, Annabel Mead, Milan Khara, Mark McLean, Ashok Krishnamurthy, Stanley DeVlaming and Brian Conway were the physicians involved in the recruitment of the cohort described in this paper. Harout K. Tossonian performed data collection and conducted the analysis of research in collaboration with Jesse D. Raffa, who performed the statistical analysis. Behroz Rashidi assisted with the data collection for the study. Harout K. Tossonian wrote the initial draft of this chapter and attended to the revisions xiv  required for final publication. Brian Conway supervised the research and with Jesse D. Raffa assisted with the revisions of the final draft.  CHAPTER V: Clinical Implications of Mutations at Reverse Transcriptase Codon 135 on Response to NNRTI-Based HAART Harout K. Tossonian was responsible for developing the experimental approach together with Brian Conway, Jesse D. Raffa and Jason Grebely. Mark Viljoen, Annabel Mead, Milan Khara, Mark McLean, Ashok Krishnamurthy, Stanley DeVlaming and Brian Conway were the physicians involved in the recruitment of the cohort described in this paper. Harout K. Tossonian performed data collection and conducted the analysis of research in collaboration with Jesse D. Raffa, who performed the statistical analysis. Harout K. Tossonian wrote the initial draft of this chapter and attended to the revisions required for final publication. Brian Conway supervised the research and with Jesse D. Raffa assisted with the revisions of the final draft.  CHAPTER VI: Hepatotoxicity in Injection Drug Users Receiving Nevirapine-Based HAART Brian Conway, Stanley DeVlaming and Mark Viljoen were responsible for the recruitment of the cohort at the Pender Community Health Centre, while Brian Conway and Robert Reynolds were responsible for the recruitment of patients at the Downtown Infectious Diseases Clinic. Harout K. Tossonian, Jesse D. Raffa and Jason Grebely developed the experimental approach together with Brian Conway. Behroz Rashidi, Colleen Hoffmann, Aneeta Mistry and Alexander Winther assisted in data collection for this study. Harout K. Tossonian performed all the data management and analysis in collaboration with Jesse D. Raffa, who performed the statistical analysis. Harout K. Tossonian wrote this chapter which was revised by Jesse D. Raffa and Brian Conway.  CHAPTER VII: Methadone Dosing Strategies in HIV-Infected Injection Drug Users Enrolled in a Directly Observed Therapy Program Brian Conway, Stanley DeVlaming, Mark Viljoen, Annabel Mead, Milan Khara, Mark McLean, and Fiona Duncan were responsible for the recruitment of the cohort at the Pender Community xv  Health Centre, while Chris Fraser was responsible for the recruitment of patients at the Cool-Aid Community Health Centre. Harout K. Tossonian, Jason Grebely and Jesse D. Raffa developed the experimental approach together with Brian Conway. Brendon Trotter assisted in data collection for this study. Harout K. Tossonian performed all the data management and analysis in collaboration with Jesse D. Raffa, who performed the statistical analysis. Harout K. Tossonian wrote the initial draft of this chapter and attended to the revisions required for final publication. Brian Conway supervised the research and with Jesse D. Raffa assisted with the revisions of the final draft.  xvi  CHAPTER I  Introduction to HIV Infection in Injection Drug Users  1.1 Injection Drug Use and the Epidemic of HIV Infection The ability of many psychoactive drugs to shortcut the reward systems in the human central nervous system creates vulnerability to misuse of both licit and illicit drugs throughout the world. Intravenous injection is a dynamic process and represents the most efficient route of drug administration leading to rapid and strong drug effects. Unfortunately, the sharing of needles and syringes among injection drug users (IDUs) provides an efficient tool for the transmission of blood-borne infections, in particular HIV (1, 2), and contributes significantly to the morbidity and mortality caused by illicit drug use (3). The link between IDU and HIV has been well described since the beginning of the HIV pandemic (4, 5). AIDS was first observed in IDUs in late 1981 (6) and since then an increasing proportion of new HIV infections has been attributed to IDU in many parts of the world (7). Although different prevention programs have been implemented to prevent the transmission of HIV infection among IDUs (8, 9), the number of infections through IDU continues to increase globally (10). In fact, the world’s most volatile HIV epidemics are in areas that are fueled by illicit drug use, particularly heroin and cocaine (11). Thus, preventing transmission of HIV among IDUs is one of the most challenging issues in public health. HIV/AIDS and IDU are both complex disorders and adversely affect each other as two intertwining epidemics. Each disorder individually impacts millions of people, with explosive epidemics reported worldwide (10, 11). In light of the increasingly central role of IDU on the HIV epidemic, understanding of the clinical features of HIV, treatment of HIV disease itself and addressing the special difficulties in providing clinical care to this vulnerable population are of great importance.  1.2 Epidemiology of HIV Infection in Injection Drug Users Despite recent reports of decreases in the prevalence of HIV infection in certain regions of the world, the HIV pandemic continues to spread on an ongoing basis. According to the most recent statistics from the Joint United Nations Program on HIV/AIDS (UNAIDS), approximately 33 1  million people are living with HIV around the world (7). In 2007 alone, 2.0 million people died of the consequences of HIV infection and 2.7 million people became newly infected with HIV (7). The most recent estimates suggest that there are about 16 million individuals who inject illicit drugs globally with about 3 million of these injectors being infected with HIV (10). IDU has been reported in 148 countries, among which 120 have detected HIV among IDUs (10). Although IDUs account for about 10% of the estimated new HIV infections each year (11), they account for 30% of new infections that occur outside of sub-Saharan Africa (7). The largest number of injectors live in China, the United States and Russia, where mid-estimates of HIV prevalence among injectors are 12%, 16% and 37%, respectively (10). In the United States, recent surveillance trends indicate that HIV infection associated with IDU accounts for a decreasing proportion of the total number of infections with HIV (12-14). Despite the declining prevalence rates, drug use behaviors still account for one-third of all new HIV cases (13). In other parts of the world, recent surveillance trends indicate increasing prevalence rates of HIV infection among IDUs (10). Areas of particular concern are countries in East and Southeast Asia, Eastern Europe and Latin America, where the prevalence of HIV through IDU account for more than 40% of the infections (10). Although it is not completely clear how epidemics among IDUs start, it is known that outbreaks can arise very quickly. This is evident from the history of the epidemics in places such as New York City in the 1970s (15), Southeast Asia in 1980s (16), Vancouver in the 1990s (17), and more recently in China (18), Iran (19), Russia (20) and other countries. For example, a decade ago, HIV was not identified among IDUs in Estonia (21). Recent estimates, however, suggest prevalence rates of HIV reaching 50-72% among Estonian IDUs (22, 23). In St. Petersburg, Russia, a study from 2006 estimates the prevalence of HIV infection among IDUs to be 50% (24) compared to a previous estimate of 11% in 2000 (25). These examples serve to illustrate the extent to which IDU-associated HIV epidemics can occur with startling speed. In Canada, a total of 64,800 HIV-positive tests were reported to the Public Health Agency of Canada (PHAC) from 1985 (when reporting began) until the end of 2007 (26). In 2007 alone, the number of positive tests reported was 2,432 mainly from Ontario (44.0%), Quebec (21.5%) and 2  British Columbia (16.3%) (26). Regarding exposure category, men who had sex with men (MSM) (41.3%), heterosexual contact (29.7%) and IDU (20.9%) accounted for the major proportions of positive test reports in 2007 (26). Prior to 1998, women represented 11.9% of all positive tests while in 2007 the proportion was at 24.9% with 40.2% of these positive test reports attributed to IDU (26). During the 1990’s, an explosive HIV epidemic occurred among IDUs in the Downtown Eastside of Vancouver which consists of 10 square city blocks and is home to more than 5000 IDUs (27). Studies from this area reported an annual incidence of HIV infection of 18% and a prevalence of HIV infection of 25% (17, 28), despite the presence of legal needle-exchange programs and free access to methadone maintenance treatment and antiretroviral therapy. Although the number of new HIV infections among IDUs appears to have stabilized overall, the estimated number of new HIV infections among IDU remains unacceptably high (26). Therefore, big challenges remain for the prevention and treatment of HIV infection among individuals who inject drugs especially among women and Aboriginal populations who disproportionately continue to acquire HIV through IDU (26).  1.3 Natural History of HIV Infection in Injection Drug Users 1.3.1 Introduction Human immunodeficiency virus (HIV) is the causative agent of the life-threatening illness known as acquired immune deficiency syndrome (AIDS). It is a blood-borne, sexually transmissible virus that is typically transmitted via sexual intercourse, shared IDU, and motherto-child transmission (during labour or breastfeeding). It is a retrovirus and belongs to the subfamily of lentiviruses which typically show a chronic course of disease with long periods of clinical latency and persistent viral replication. HIV is enveloped, diploid, single-stranded and a positive-sense RNA virus. It contains the 3 species-defining retroviral genes: gag (group-specific antigen; the inner structural proteins), pol (polymerase; also contains integrase and protease viral enzymes) and env (envelope; the outer structural proteins responsible for cell-type specificity). It has 6 additional accessory genes— tat, rev, nef, vif, vpr and vpu (or vpx). There are 2 distinct species of the virus: HIV-1 initially described in 1983 (29, 30) and HIV-2 later described in 1986 (31). Genetically, HIV-1 and HIV-2 are superficially similar, but each contains unique genes and its own distinct replication process. In addition, each species is composed of multiple subtypes or  3  clades (32). As HIV-1 is the major cause of AIDS in the world, our discussion will be limited to HIV-1 infection. HIV begins its life cycle when it binds to a CD4 receptor and one of two co-receptors (CCR5 or CXCR4) on the surface of a CD4 bearing cell. The virus then fuses with the host cell and the viral RNA is released into the cell and converted into double stranded DNA by the enzyme reverse transcriptase. The integrase enzyme integrates the proviral DNA into the cell’s own genome. Cell activation induces transcription of the provirus resulting in viral RNA present in the host cell’s cytoplasm. The viral RNA and viral proteins assemble at the cell membrane into a new virus. The enzyme protease processes HIV proteins into their functional forms. The mature virus then buds from the cell and is released to infect other cells. HIV preferentially targets cells that express the CD4 receptor that is present on cell surfaces of T-lymphocytes, monocytes, macrophages, dendritic cells and microglial cells of the central nervous system. In general, CD4 cells are responsible for inducing specific immunologic responses for any incoming pathogen. As the HIV infection progresses, the CD4 cells decline or become depleted resulting in poor immune function. Once the CD4 cell values decline to very low levels, an HIV-infected individual becomes susceptible to opportunistic infections and normally harmless commensal organisms, which eventually leads to the terminal phase of the illness defined as AIDS. The natural history of HIV disease may differ in IDUs and in individuals who acquire the infection sexually. This may be due to several influencing factors such as repeated exposure to different viral strains through the parenteral route, differences in the initial steps of pathogenesis when the virus enters through the bloodstream rather than through mucosa, repeated exposure to a variety of other antigens, immunologic effects of injected and other drugs, immunologic effects of a drug-injecting lifestyle (such as homelessness or poor nutrition), direct interactions of HIV with an array of other blood-borne pathogens and differences in rates of latent infection or exposure to specific opportunistic pathogens (33). However, the natural history of HIV disease among IDUs seems to be similar to that in patients in other transmission-risk categories (34). In IDUs, as in other patient groups, HIV infection goes through 3 different phases: acute/early infection, chronic infection and AIDS.  4  1.3.2 Acute/Early Infection HIV infection starts as an acute infection with systemic manifestations. Acute HIV infection refers to the first stage of infection, the time immediately after a person is infected and before an antibody response to the infection develops. It is increasingly recognized that events occurring during acute infection may determine the natural course of the disease. Seroconversion refers to the stage when a person develops HIV-specific antibodies. Early HIV infection is used to describe HIV infections that may not be clearly acute, but have likely occurred within 6 to 12 months. Primary HIV infection is a somewhat looser term that describes all acutely and early infected patients equally well. After acute infection with HIV, 40-90% of patients experience an acute flu-like illness (35, 36). Symptoms typically occur 2-6 weeks after exposure to HIV and commonly include fever, rash, fatigue, pharyngitis, weight loss, night sweats, lymphadenopathy, myalgias, headache, nausea, and diarrhea (35, 36). Seroconversion symptoms typically last 14 days but may persist for as long as 10 weeks (36). However, the diagnosis of acute infection is missed in the majority of cases due to the non-specific nature of the symptoms. Therefore, the diagnosis requires a high degree of clinical suspicion, based on clinical symptoms and history of exposure, in addition to specific laboratory tests. Because antibodies may not have yet formed at the time of peak viremia and onset of symptoms, positive results of a p24 antigen assay or a detectable HIV RNA, along with negative or evolving enzyme immunoassay (EIA) and western blot test results, are common diagnostic markers. During acute infection, the virus replicates extensively in the absence of detectable adaptive immune response. HIV plasma viral loads are typically very high in patients with acute infection, often exceeding 1 million copies/mL (37). It is during this initial stage of viral replication that important pathogenic processes occur such as the establishment of proviral reservoirs. These reservoirs consist of persistently infected cells, typically macrophages, and appear to be steadily releasing viruses. Some of the released viruses replenish the tissue reservoirs while others go on to produce more active infection. In addition, during acute infection with HIV, CD4 T-cell levels decline and there is functional impairment of HIV-specific T-cells (38, 39). With the appearance of anti-HIV antibodies and CD8 T-cell responses, the viral load drops by several orders of magnitude before reaching a viral set-point while the CD4 cell count returns to levels within the  5  reference range, although slightly lower than before infection. The set-point following the resolution of the acute infection is a strong predictor of long-term disease progression rates (40). Treatment of primary HIV infection may have a number of virologic, immunologic and clinical benefits. Despite the availability of some clinical data to support early initiation of treatment (37, 41, 42), the long-term clinical benefits of such treatment remain the subject of ongoing randomized clinical trials. Thus, the issue of initiating antiretroviral therapy in patients acutely infected with HIV remains controversial (43). Potential benefits of early treatment include mitigation of acute viral symptoms, early prevention of abnormal T-cell function, decreasing the initial viral load set-point, limiting viral evolution and diversity and reducing the risk of transmission at a time of extraordinarily high virus levels. Risks include high costs, adverse effects and abnormal metabolic findings, development of drug resistance mutations, long-term challenges to adherence as well as toxicities and expected duration of benefits (43).  1.3.3 Chronic Infection After acute infection with HIV, an extended clinically latent or chronic phase follows. This phase of infection is generally asymptomatic in the beginning but gradually progresses and becomes symptomatic. At the initial stages, individuals exhibit few or no signs or symptoms for several years to a decade or more. However, viral replication is clearly ongoing during this time, and the immune response against the virus is effective and vigorous. If untreated, the viral load tends to persist at a relatively steady state, while the CD4 cell count continues to decline eventually leading the patient to the terminal phase of illness. Clinically, patients with CD4 cell counts over 500 cells/mm3 are generally asymptomatic with the exception of mild or moderate lymphadenopathy. When symptoms are present, they are commonly dermatologic. Most patients with CD4 counts between 200 and 500 cells/mm3 remain asymptomatic or have mild disease. Worsening of the chronic skin conditions can be observed. Patients experience recurrent herpes simplex disease, varicella-zoster virus disease, oropharyngeal or vaginal candidiasis, oral hairy leukoplakia as well as recurrent diarrhea, intermittent fever, weight loss, myalgias, arthralgias, headache, and fatigue. Patients develop bacterial infections presenting as sinusitis, bronchitis or pneumonia which become more prevalent during later stages of the infection.  6  The CD4 cell count and plasma viral load are the best prognostic factors for determining the course of disease progression in all HIV-infected individuals (44, 45). The CD4 count is the most sensitive predictor of disease progression and the development of symptomatic HIV infection (46, 47), while the plasma viral load is a very useful predictor of disease course over a more extended period of time and is strongly associated with the rate of subsequent CD4 cell count decline (40, 47). Lower CD4 counts are associated with greater risk of disease progression. For example, the risk of progression is relatively low in patients with CD4 cell counts between 350–500 cells/mm3, increases substantially at CD4 counts below 350 cells/mm3 and becomes greatest at CD4 cell counts below 200 cells/mm3 (48). Thus, the current guidelines generally recommend the initiation of antiretroviral therapy during this chronic phase of infection when the CD4 cell count drops to less than 350 cells/mm3 (49), although more recent cohort studies suggest a benefit with respect to mortality of even earlier initiation of therapy, especially if significant co-morbidities (such as hepatitis C virus infection) are present (50). With the use of highly active antiretroviral therapy (HAART) there is a dramatic change in the natural history of HIV disease (51, 52). HAART can lead to suppression of viral replication, preservation of immune function, less progression to AIDS and prolongation of survival (53, 54), allowing most patients to approach a normal life span (55). The rate of disease progression among individuals infected with HIV is highly variable and is categorized as being rapid, intermediate and late or long-term non-progression (56). The majority of infected individuals (70-80%) experience intermediate disease progression in which they have HIV RNA increase, CD4 T-cell decline and development of AIDS-related illnesses within 6-10 years after acquisition of HIV. In rapid progressors (10-15%), patients experience rapid CD4 T-cell decline and occurrence of AIDS-related events within a few years after infection. On the other hand, in long-term non-progressors (5%), individuals remain healthy without significant changes in CD4 count or plasma viral load for over 10 years (56). Concerning IDUs, a number of studies indicate that progression of HIV disease among this population follows a time course similar to that observed in populations with other routes of HIV acquisition (57, 58). Nevertheless, the effect of illicit drugs injected may affect the progression of HIV infection among IDUs. Several laboratory-based studies indicate that illicit drugs may have immunosuppressive effects and thereby may accelerate HIV disease progression (59, 60). 7  Epidemiological studies show mixed results. Some studies associate drug use with progression of HIV disease while others show similar rates of disease progression among IDUs versus nonIDUs (60). According to some studies, the patterns of drug use affect progression of HIV disease as well, with better duration of survival among intermittent drug users than among persistent drug users (61). However, the enhanced progression of HIV disease in IDUs may be due to a number of other factors such as access to care and adherence to treatment rather than the effects of the illicit drug use per se since IDUs are known to have delayed access to treatment and lower adherence to HAART (62, 63). In addition, the route of intravenous infection may also affect the rate of progression of HIV since specific diseases manifest differently among IDUs. Most importantly, infection with hepatitis C virus (HCV) is highly prevalent among HIV-infected IDUs since both viruses share the same route of transmission (64). It is generally agreed that HIV infection accelerates the progression of HCV-related liver disease (65, 66). On the other hand, the effect of HCV infection on the progression of HIV disease is less clear since there are conflicting reports regarding the effect of HCV on the natural history of HIV disease. Several studies have found no association between HCV infection and progression of HIV disease with or without the use of HAART (67-70); however, others have documented such an association (71-73) and have linked the increased progression of HIV disease to poor immunologic response (71, 74), HCV RNA levels (75) or HCV genotype (76). Similarly, in patients co-infected with HIV and hepatitis B virus (HBV), there are some studies that have found no impact of HBV on HIV disease progression (77, 78) while others have linked HBV to rapid progression of HIV disease (79, 80). Regardless of these conflicting findings, it is clearly evident that co-infection with HCV or HBV affects the natural history of HIV not necessarily by affecting HIV itself but by increasing morbidity and mortality associated with liver disease in HIV co-infected patients (81).  1.3.4 AIDS When the immune system becomes severely compromised, patients begin to develop a variety of diseases and opportunistic infections referred to as AIDS-defining illnesses. The diagnosis of AIDS is given when the CD4 cell count drops below 200 cell/mm3 and/or there is a history of an AIDS-defining illness. During this terminal phase of infection, there is worsening of the symptoms encountered at earlier stages of the disease. Progressive wasting occurs with greater frequency. Most patients experience various neurological problems. Distinguished malignancies 8  occur such as Kaposi’s sarcoma, lymphoma, non-Hodgkin's lymphoma and invasive cervical cancer. Certain opportunistic infections develop such as Pneumocystis pneumonia, tuberculosis, toxoplasmosis, cryptosporidiosis, cryptococcosis, coccidioidomycosis, cytomegalovirus disease, Mycobacterium  avium  complex  disease,  progressive  multifocal  leukoencephalopathy,  histoplasmosis and others. Specific manifestation of these diseases among IDUs may differ from those infected through other routes of transmission because of their different patterns of exposure to routine and opportunistic pathogens. For instance, IDUs are less likely to have cytomegalovirus disease or infection with human herpes virus-8 (the causative agent of Kaposi’s sarcoma) compared to other HIV-patient groups (82, 83). On the other hand, IDUs have a higher prevalence of coinfection with tuberculosis (82, 84). HIV-infected persons who do not receive appropriate treatment for clinically latent or minimally symptomatic tuberculosis progress to more advanced and widespread disease at a rate 10 times greater than that for persons not infected with HIV (85). Treatment of both HIV and tuberculosis simultaneously (according to established guidelines) raises a number of new complicating issues, such as synergistic drug toxicity, lower adherence and deleterious drug interactions. Some of these issues can be productively addressed by using directly observed therapy (DOT) programs (85). Without the use of HAART, survival is short after the clinical diagnosis of AIDS. Patients diagnosed with opportunistic infections have the most rapid mortality. In such individuals, the median survival after the diagnosis of AIDS is considered to be between 12 and 18 months (86). With the use of HAART, however, the natural history of treated HIV disease is modified. There is less progression of HIV disease and marked decline in mortality among HIV-infected individuals (54, 87). This decline in mortality is accompanied by a marked decrease in the incidence of opportunistic infections. With improved survival rates, death among persons with AIDS is more frequently due to chronic diseases such as cardiovascular disease, liver disease and non-AIDS defining cancer (88). The benefit of HAART has been demonstrated in IDUs and mortality rates have declined in this marginalized population as well (89). However, mortality rates in IDUs remain higher than those reported in other patient groups (90, 91). Drug overdose is a leading cause of death among IDUs (92) and depends probably on the purity and quality of illicit drugs used and on the HIV status of 9  the individuals (93). In addition to HIV-related causes, other major causes of death include serious bacterial infections, injuries and accidents (94, 95). Thus, although IDUs benefit significantly from HAART, mortality remains higher in treated HIV-positive IDUs as compared to treated HIV-positive non-IDUs. Several factors probably contribute to the higher mortality rates in IDUs, including delayed initiation of treatment, interruption of medical care, continuous drug use and non-optimal adherence to HAART regimens.  1.4 Treatment of HIV Infection in Injection Drug Users 1.4.1 Introduction The primary goal of antiretroviral therapy is to increase disease-free survival through maximal suppression of viral replication and preservation of immune function. The introduction of HAART in 1996 has revolutionized the treatment of HIV infection resulting in impressive reductions in morbidity and mortality among people living with HIV/AIDS (51, 54). The effectiveness of HAART have been well documented. Antiretroviral therapy seems to be as effective in persons with a history of IDU as in other patient groups (96, 97). However, IDUs derive less benefit from HAART despite the widespread availability of antiretroviral medications (98). The reason for this disparity is due to a number of factors. HAART regimens consist of using three medications from at least two classes of antiretroviral drugs. Generally, two nucleoside reverse transcriptase inhibitors (NRTIs) are paired with one drug from the non-nucleoside reverse transcriptase inhibitor (NNRTI) or protease inhibitor (PI) classes. More recently, drugs from the newer classes such as the fusion inhibitors, entry inhibitors or integrase inhibitors are being used as part of HAART. Each class of antiretroviral drugs attacks the virus at a different stage of the replication cycle. Thus, by using drugs from different classes, HAART aims at ensuring complete viral suppression, achieving maximal immunologic recovery and having the longest duration of activity from the combination. The optimal timing of initiation of antiretroviral therapy depends on consideration of the benefits in balance with the risks of drug toxicity, potential emergence of drug resistance mutations and the understanding that HIV infection is a chronic disease requiring continuous therapy over the course of decades. Whatever CD4 cell count and plasma RNA level thresholds are considered for the initiation of the antiretroviral therapy, the goal is always to achieve and maintain suppression of HIV RNA to less than 50 copies/mL in treatment-naïve as well as in treatment10  experienced patients, to maximize the likelihood of immune restoration and a long-term diseasefree state (49, 99, 100).  1.4.2 Antiretroviral Drugs 1.4.2.1 Nucleoside Reverse Transcriptase Inhibitors (NRTIs) When HIV infects a cell, the enzyme reverse transcriptase (RT) copies the viral single stranded RNA into a double-stranded DNA. The viral DNA is then integrated into the host chromosome, which then allows host cellular processes, such as transcription and translation, to reproduce the virus. NRTIs interrupt the function of RT and prevent the completion of the synthesis of the double-stranded DNA, thus preventing HIV from multiplying. In order to be incorporated into the viral DNA, NRTIs must be activated in the cell by the addition of three phosphate groups to their deoxyribose moiety, to form NRTI triphosphates. Nucleotide analogues, on the other hand, require the addition of only two phosphate groups to form the active compounds. NRTIs act as alternative substrates or false building blocks and compete with the physiological substrates for binding to the RT. These analogues are similar to the naturally occurring deoxynucleotides needed to synthesize the viral DNA. However, they lack a 3'-hydroxyl group on the deoxyribose moiety. As a result, following the incorporation of an NRTI, the next incoming deoxynucleotide cannot form the next 5'-3' phosphodiester bond needed to extend the DNA chain. Thus, the growing DNA chain is terminated and the synthesis of viral DNA is halted. The NRTIs currently used in the treatment of HIV infection include zidovudine (AZT), stavudine (D4T), didanosine (DDI), lamivudine (3TC), abacavir (ABC), emtricitabine (FTC) and tenofovir (TDF). All are nucleoside analogues except for TDF which is a nucleotide adenosine analogue. AZT and D4T are thymidine analogues, while 3TC and FTC are cytidine analogues. Therefore, combinations of AZT plus D4T or 3TC plus FTC are pointless since both drugs compete for the same bases. DDI is an adenosine analogue that is converted to dideoxyadenosine, while ABC is a guanosine analogue. Nowadays, fixed-dose combinations of NRTIs such as Combivir (AZT and 3TC), Kivexa (ABC and 3TC) and Truvada (TDF and FTC) are widely used because they significantly reduce the pill burden, simplify regimens and facilitate adherence.  11  All NRTIs have similar mechanism of antiviral activity. However, they can cause a wide variety of long-term side effects. Mitochondrial toxicity is a major adverse effect of nucleoside analogue treatment (mainly D4T and DDI) and can lead to myopathy, peripheral neuropathy, enlargement of the liver, pancreatitis and especially lactate acidosis (101). It also appears that lipoatrophy may be caused by this toxicity (102). Other major side effects include bone marrow suppression and anemia with the use of AZT (103), nephrotoxicity with the use of tenofovir (104) and hypersensitivity reactions with the use of ABC in 4-6% of the patients (105).  1.4.2.2 Non-Nucleoside Reverse Transcriptase Inhibitors (NNRTIs) NNRTIs include compounds with different chemical structures. NNRTIs, like NRTIs, target the RT enzyme. However, the mechanism of action is different. NNRTIs block complementary DNA elongation by binding directly and noncompetitively to RT at sites distinct from the nucleoside binding site. This results in a conformational change in the protein at its active site, decreasing affinity for nucleoside binding. Unlike NRTIs, they do not require phosphorylation or intracellular processing to become active. The NNRTIs currently used in the treatment of HIV infection include nevirapine (NVP), delavirdine (DLV), efavirenz (EFV) and more recently etravirine (ETV). The antiviral efficacy and tolerability of NNRTIs makes them a favorable component of HAART regimens. Due to high pill burden and the required three times daily dosing, DLV is currently rarely prescribed. On the other hand, a new fixed dose combination pill of FTC, TDF and EFV (known as Atripla) is widely used because it gives the option to reduce HAART to one pill per day. The newest NNRTI, ETV, shows promise as it is effective in suppressing viruses that are resistant to firstgeneration NNRTIs (106). All NNRTIs are metabolized to some degree by the cytochrome P450 system (107). In addition, they act either as inducers or inhibitors of cytochrome P450 enzymes, and thus can affect the metabolism of other drugs such as methadone (107). Both NVP and EFV have similar efficacies differing mainly in their side effect profiles (108, 109). NVP can cause elevation of liver enzymes especially in individuals who have chronic hepatitis (110, 111), such as in IDUs. In addition, an increased risk of hepatotoxicity is present in women with CD4 cell counts >250 cells/mm3 and in men with CD4 cell counts >400 cells/mm3 which has led to warning against use of NVP in such circumstances (112, 113). Rash is another serious adverse effect that can develop during the first few weeks of therapy with NVP (107). 12  EFV can cause rashes as well as elevation of liver enzymes but less frequently than with NVP (108). The major adverse effects associated with the use of EFV involve the central nervous system. The main symptoms include dizziness, hallucinations, insomnia, somnolence and abnormal or vivid dreams (114). Some patients also experience psychiatric symptoms such as anxiety, depression and suicidal ideation (114, 115). Thus, the use of EFV may exacerbate underlying psychiatric disorders in patients (such as IDUs) who have a history of mental health problems.  1.4.2.3 Protease Inhibitors (PIs) The HIV protease enzyme cuts the viral gag-pol polyprotein into its functional subunits. PIs appear to block the necessary cleavage of these polyproteins in the late stages of the viral replicative cycle, causing the production of immature and defective viral particles which are unable to infect new cells. Unlike NRTIs, PIs do not need intracellular processing to become active. The PIs currently used in the treatment of HIV infection include indinavir (IDV), saquinavir (SQV), fosamprenavir (APV), nelfinavir (NFV), ritonavir (RTV), lopinavir (LPV), atazanavir (ATV) and more recently, tipranavir (TPV) and darunavir (DRV). PIs are extensively metabolized by the hepatic cytochrome P450 enzymes which means that PIs can interfere with the hepatic metabolism of other drugs by acting as cytochrome P450 inducers or inhibitors (116). Currently, RTV is not used as a single PI in the treatment of HIV infection because of its poor tolerability (117). However, since it has the ability to inhibit cytochrome P450 metabolism, it is used as a pharmacokinetic booster of several other PIs (118). In general, a low dose of RTV is used to increase the blood levels of other PIs (except for NFV). This useful interaction between RTV and other PIs allows lower and less frequent dosing and may inhibit the growth or the development of drug-resistant viruses (119). The long-term use of several PIs has been shown to be mainly associated with metabolic adverse effects such as hyperlipidemia, hyperglycemia (related to insulin resistance) and lipodystrophy (120). Hyperglycemia and hyperlipidemia in turn may be associated with an increased risk of myocardial infarction (121). Most of the PIs also cause gastrointestinal side effects such as nausea, vomiting and diarrhea, notably with LPV/RTV and NFV. ATV does not have a negative 13  influence on lipid levels and does not cause insulin resistance; however, it can cause hyperbilirubenemia which can result in clinical jaundice (122). Beyond the side effects mentioned above, IDV can in addition cause nephrolithiasis in 5-25% of patients (123). Because of toxicities or high pill burden, some of the older PIs, such as IDV and NFV, are becoming less commonly prescribed.  1.4.2.4 Other Antiretroviral Drugs For entry of HIV into the CD4 T cell, there are three crucial steps: 1) Binding of HIV via the gp120 envelope glycoprotein to the CD4 receptor (attachment); 2) binding of the gp120 to the chemokine co-receptor CCR5 or CXCR4 (co-receptor binding) and finally 3) the fusion of the viral envelope with the host cell membrane through conformational changes of the gp41 portion of the envelope glycoprotein (fusion). Every step of HIV entry can theoretically be inhibited. However, currently there are drugs that inhibit the fusion (fusion inhibitors) and the co-receptor binding steps (co-receptor antagonists) only. Among the fusion inhibitors, enfuvirtide (or T-20) is the antiretroviral drug that is used in clinical practice. Since it is a large peptide, it needs to be administered as a subcutaneous injection (124) which is unappealing for clinicians and patients, especially for hard-to-treat populations such as IDUs. One of the most common adverse effects is local skin reactions at the injection site. Enfuvirtide can optimize HAART efficacy in heavily antiretroviral-experienced patients (125, 126). However, its high cost, inconvenient route and dose of administration keeps its use limited to salvage therapy in treatment-experienced patients with multi-drug resistant HIV (127). Among the co-receptor antagonists, maraviroc is the only approved CCR5 antagonist and the single oral HIV entry inhibitor in clinical use (128). Maraviroc has demonstrated its efficacy in treatment-experienced patients with multiple drug failures and appears to have a favorable safety profile (129). Maraviroc specifically inhibits the replication of R5-tropic HIV variants which predominantly infect macrophages (M-tropic) and are present in the earlier stages of the infection (128, 130). Viruses that use the CXCR4 co-receptor for entry predominantly infect Tcells (T-tropic) and occur in the later stages of infection (128). Maraviroc does not inhibit the replication of X4-tropic viruses (131); therefore, patients who have detectable X4 viruses or dual tropic viruses cannot be candidates for this drug. Given its exclusive activity against CCR5 14  tropic strains, viral tropism testing is mandatory before using CCR5 antagonists in clinical practice (132). Finally, among the newest drug classes, integrase inhibitors seem to be very promising. Raltegravir is the first approved integrase inhibitor and is probably the most exciting drug of all in the treatment of HIV (133). Integrase, along with RT and protease, is one of the three key enzymes in the HIV replication cycle. This enzyme is involved in the integration of viral DNA into the host genome. Raltegravir inhibits the catalytic activity of integrase, thus, interrupting the proliferation of HIV. Several controlled studies have demonstrated the efficacy of raltegravir in suppressing HIV replication in heavily treatment-experienced patients with multi-drug resistant viruses (134, 135). It has a very good safety and tolerability profile and is currently indicated for the treatment of HIV infection in treatment-experienced patients with resistance to multiple antiretroviral agents (134, 135).  1.4.3 HIV Drug Resistance 1.4.3.1 Introduction The development of resistance to antiretroviral drugs is one of the main causes of treatment failure. Incomplete and inconsistent adherence to treatment can lead to the emergence of resistance to antiretroviral drugs and as a consequence may compromise the benefits of HAART (136). This is of great concern for IDUs who generally tend to be non-adherent to antiretroviral medications (137, 138). HIV infection is a dynamic process with high rates of replication and a great potential for genetic variation (139). The rapid development of resistance is due to the high turnover of HIV with the production of approximately 10 million new viruses per day and with the exceptionally high error rate and non-proofreading ability of RT (140). This leads to high rate of mutations and constant production of new viral strains, even in the absence of treatment. In the setting of incomplete viral suppression with HAART, resistance mutations specific for the prescribed antiretroviral drugs can develop, which then become the predominant species of HIV circulating in the body (141).  15  1.4.3.2 Resistance Testing HIV drug resistance is usually evaluated by the use of genotypic resistance testing (142, 143). Genotypic tests are based on the analysis of mutations (codons) associated with resistance. They are determined primarily by the direct sequencing of the amplified HIV genome. Of interest is the sequencing of the pol region which encodes the viral enzymes protease, RT and integrase. Genotype tests only detect viral mutants comprising at least 20 to 30 % of the total population (hence, they cannot detect minority variants) and provide an indirect measurement of drug resistance. Genotypic resistance mutations have been reported for each of the antiretroviral agents in current use. Since the interpretation of genotypic test results is difficult, lists of predefined drug resistance mutations or algorithms for the interpretation of genotypic drug resistance have been developed which are currently used in clinical practice (144, 145). The second method to measure resistance is by using phenotypic resistance tests (142, 143). Phenotypic tests involve direct quantification of drug sensitivity. Viral replication is measured in cell cultures under the selective pressure of increasing concentrations of antiretroviral drugs and is compared to viral replication of the wild type virus. The drug concentration required to inhibit viral replication by 50% is expressed as IC50. The sensitivity of the virus is evaluated by the foldchange value which is the IC50 of the isolate divided by the IC50 of a wild type reference virus. The fold-change value is then compared to a cut-off value to determine whether the HIV isolate is sensitive or resistant. Another approach to evaluating resistance is by using the virtual phenotype (146). This attempts to combine the advantages of the genotypic and the phenotypic assays. This assay relies on a computerized database of more than 10,000 patient samples that include matched genotypic and phenotypic data. With the virtual phenotype, a conventional genotype is first performed on the patient's sample. The resulting genotype is then pattern-matched to other patient samples in the database. The corresponding phenotypes for the matching samples are then extracted from the database and a predicted or virtual phenotype result is synthesized. This assay is faster and less expensive than the phenotype test and since the final report has both genotypic and phenotypic results, this assay is commonly used in clinical practice. The main purpose behind the use of resistance testing is to provide information to assist in the selection of antiretroviral regimens that achieve and maintain full virologic suppression. Various 16  guidelines (49, 147) recommend resistance testing in patients with acute or early infection with HIV because of the high reported rates of transmitted drug resistance. These guidelines also recommend resistance testing in patients with chronic HIV infection because of the rising prevalence of baseline drug resistance in untreated patients with unknown duration of infection. However, drug resistance testing is mainly recommended in patients failing all lines of antiretroviral therapy (49, 147).  1.4.3.3 Resistance to Antiretroviral Drugs Steric inhibition and phosphorylysis are two biochemical mechanisms that lead to NRTI resistance (148). Steric inhibition is caused by mutations (such as M184V, L74V and K65R) that enable RT to recognize and favor the incorporation of naturally occurring nucleotides compared to NRTIs (149, 150). Phosphorylysis is caused by other mutations (such as thymidine analogue mutations or TAMs) that lead to the excision of the NRTIs already incorporated in the growing DNA chain (151). For several NRTIs, such as 3TC or FTC, a high degree of resistance can develop rapidly following only a single mutation (152). However, the 3TC or the FTC-specific mutation, M184V, reduces the replication capacity or the viral fitness (153, 154) and increases the sensitivity of the viruses to other NRTIs thereby suggesting some clinical benefit of using 3TC or FTC despite the presence of this mutation (155). TAMs are selected by AZT and D4T and include the mutations M41L, D67N, K70R, L210W, T215Y and K219Q (156, 157). Three or more TAMs are generally associated with a relevant reduction in the sensitivity to these two drugs. However, the term NAMs (nucleoside analog mutations) is also used instead of TAMs, since these mutations are cross-resistant to all other NRTIs (144). Other key signature mutations include the L74V mutation selected mainly by DDI (158) and the K65R mutation selected by TDF (159). ABC has no key signature mutations but resistance is associated with the mutations K65R, L74V and M184V (160). As with M184V, the mutation K65R leads to a reduction in the viral replication capacity. Besides TAMs, some mutations cause multi-drug resistance to several NRTIs, such as the T69 insertion (161) or the Q151M complex (162). However, these mutations are relatively uncommon. NNRTI resistance is caused by mutations at the binding pocket that reduce the affinity of NNRTI binding to the enzyme RT (163). Unfortunately, the genetic barrier to the development 17  of NNRTI resistance is low. A single mutation, such as the K103N, can confer a high degree of resistance to one or more NNRTIs (164). The overlapping resistance profiles of these drugs can create broad cross-resistance and even prevent the clinical utility of all first-generation NNRTIs (165). Mutations that confer a high level of resistance to NVP, DLV and EFV include mainly the K103N, V106A/M, Y181C, Y188L and G190A/S (166-168). Other mutations associated with this class include the A98G, L100I, K101E/P, V108I and P225H. ETV, a second-generation NNRTI, has a higher genetic barrier and is not affected by the K103N mutation (169). A total of 13 mutations affect the susceptibility of ETV; however, the drug exhibits antiviral activity in the presence of up to three ETV-associated mutations (170). PI resistance usually develops slowly as several mutations must accumulate to affect drug susceptibility (171). Thus, the genetic barrier to the development of PI resistance is high. Usually, a distinction is made between major (primary) and minor (secondary) mutations (172, 173). Major mutations are responsible for significant resistance. They are often the initial mutations arising during selection with PIs and are unique to the particular drugs used. Primary mutations are located within the active site of the target protease enzyme. Hence, they reduce the ability of the PI to bind to the enzyme. On the other hand, minor mutations are located outside the active site and usually occur after major mutations. Secondary mutations may be observed as naturally occurring polymorphisms and by themselves may confer little or no resistance to PIs. However, the accumulation of several minor mutations, on a background of primary mutations, can lead to higher levels of resistance as well as to cross-resistance to several other PIs. The spectrum of PI mutations is very large. Although there is a moderate to high degree of crossresistance between PIs, the primary mutations are relatively specific for individual PIs. They include mainly the following mutations: D30N, V32I, L33F, M46I/L, I47A/V, G48V, I50L/V, I54L/M, V82A/F/L/T/S, I84V, N88S and L90M (144). Minor mutations, such as at positions 10, 20, 36, 63, 71, 77 and 93, do not lead to resistance per se, but can compensate for the reduction in viral fitness caused by major mutations (174). The pharmacokinetic boosting of PIs with low dose RTV results in higher drug levels and creates a higher genetic barrier for the emergence of drug resistance mutations (175, 176). The newer PIs, such as TPV and DRV, have a higher genetic barrier to the development of resistance and better clinical efficacy against multidrugresistant HIV relative to older protease inhibitors (177).  18  Finally, antiretroviral drugs from novel drugs classes (fusion, entry and integrase inhibitors) provide new options in the treatment of patients infected with multi-drug resistant viruses. These newer agents have the advantage of not sharing resistance with other previously administered antiretroviral drugs. However, it is necessary to keep in mind that resistance mutations specific to these new antiretroviral drugs can emerge as well during treatment failure (177).  1.4.3.4 Primary Drug Resistance in Injection Drug Users Resistance to antiretroviral drugs in previously untreated patients, defined as primary or transmitted HIV drug resistance, has significant clinical and public health consequences (178). Transmitted resistance mutations can limit further treatment options and reduce treatment response rates (179, 180). Since 1993, studies have reported transmission of drug-resistant isolates to drug naive individuals (181). The prevalence of primary resistance in recently infected patients differs among demographic regions and ranges from 5 to 25% (180, 182, 183). Because of the high reported rates of transmitted drug resistance in recently infected patients, the current guidelines recommend resistance testing in patients with acute or early infection with HIV (49, 147). The prevalence of primary resistance mutations in drug-naive persons of unknown duration of infection tends to be similar (184-186). Such persons are more typical of HIV-infected patients presenting for initial evaluation and treatment. A concern with testing chronically infected patients is that, in the absence of drug selection pressure, drug-resistant mutations may become undetectable or overgrown by wild-type viruses. These viruses may then persist as archived viruses or as minority species which may not be detectable by current assays and therefore the true prevalence of primary drug resistance may be underestimated. However, because of the rising prevalence of baseline drug resistance in untreated patients with unknown duration of infection and the persistence of primary resistance mutations for periods of up to five years following the date of infection (187-189), current guidelines do recommend resistance testing in patients with chronic HIV infection (49, 147). Little is known as to whether different modes of HIV transmission are associated with an increased risk of primary drug resistance. According to one study, injection drug use was found to be associated with a lower risk of infection with drug resistant viruses (190); while, according to another study, injection drug use represented a predictive factor for transmission of drug 19  resistant viruses (191). In fact, there is some concern that IDUs might have higher levels of transmitted drug resistance because of non-optimal management and care of such patients living with HIV, poor adherence to treatment, relapse to illicit drug use and risky drug injecting as well as sexual behaviors. However, there are few published studies that have assessed the prevalence of primary drug resistance mutations in this population. In one study, the frequency of transmission of drug-resistant HIV amongst recently infected IDUs was found to be extremely low (192). In another study, the prevalence of primary drug resistance was demonstrated to be very high (24%) in this marginalized population (193).  1.4.3.5 Secondary Drug Resistance in Injection Drug Users The emergence of HIV drug resistance during (or because of) treatment, referred to as secondary or acquired drug resistance, can pose a significant threat to the ongoing success and durability of HAART, limit available treatment options and even lead to rapid disease progression or death (194, 195). Under selective drug pressure and in the setting of incomplete adherence, resistance mutations to antiretroviral drugs can develop (178). Non-adherence to therapy is directly associated with incomplete viral suppression (196, 197) and is thought to be the main risk factor for the development of drug resistance. Suboptimal treatment, resulting in drug resistance, can also be the consequence of individual differences in drug metabolism, drug-drug interactions causing patients to fail to reach adequate levels of drugs or side effects of prescribed antiretroviral agents (198, 199). The selection for drug-resistant viruses among viremic patients is very frequent and has significant implications for HIV treatment and transmission. In one large clinical study, an estimated 76.3% of antiretroviral-treated patients with detectable viremia (plasma viral load >500 copies/mL) had resistance mutations to one or more antiretroviral drugs (200). Among viremic patients, the estimated prevalence of resistance to each of the three drug classes ranged from 25.2% for NNRTIs to 40.5% for PIs and 71.4% for NRTIs. 3TC (or FTC) was the single drug with the highest estimated prevalence of resistance of 67.8% (200). In a retrospective study focusing on IDUs, genotypic resistance testing was done in patients who had HIV RNA >400 copies/mL. Despite the fact that only 64% were on HAART during the study period, 31% of active IDUs harbored drug-resistant viruses (201).  20  Several studies indicate that physicians may be reluctant to prescribe HAART to HIV-infected IDUs due to the common belief that IDUs may end up having higher rates of HIV drug resistance because of their poor adherence to HAART (202, 203). However, in one interesting study, it was found that resistance rates to all major classes of antiretroviral drugs were similar between IDUs and non-IDUs after 30 months of follow-up on HAART (204). Thus, although IDUs may have lower rates of adherence to HAART compared to other patient groups (205), they may be adherent enough to select for HIV drug resistance (206). Different strategies have been proposed to address the problem of adherence in IDUs. One such strategy has been the directly observed therapy or DOT (207, 208). However, several studies suggest that the risk of development of drug resistance is greatest in patients with intermediateto-high levels of adherence (206, 209, 210). Therefore, it is possible that interventions that improve adherence, such as DOT, may increase rates of drug resistance by increasing adherence to intermediate-to-high levels in patients who do not achieve full adherence.  1.4.4 Barriers to HIV Treatment 1.4.4.1 Introduction HIV-infected IDUs are often medically and socially marginalized and the treatment of their HIV infection remains a significant challenge. In fact, there are multiple barriers in the treatment of HIV infection among IDUs including limited access to care, inadequate adherence to therapy, co-infection with viral hepatitis, drug interactions with antiretroviral drugs, increased potential for HAART-related adverse events and toxicities, effect of concurrent use of illicit drugs, exacerbation of underlying psychiatric disorders, homelessness and the presence of other complicating co-morbid conditions.  1.4.4.2 Access to Care In different societies, IDUs are highly stigmatized and usually stay away from the mainstream medical care for a number of reasons based on circumstances or personal choice. IDUs may hide their continuing drug use and other health problems out of fear of rejection and tend to be mistrustful of the health care system; while physicians may be reluctant in treating such individuals because they may have negative attitudes and stereotypic views of IDUs such as viewing drug users as manipulative, unmotivated and undeserving of care (203, 211). In addition, clinical care for IDUs may be challenging and stressful for doctors and other health 21  care workers because of the complex array of medical, psychological and social problems associated with illicit drug use. Furthermore, health care services seem to be generally constructed in ways that are difficult for many drug users to access. For these and other reasons, IDUs may have delayed access to care (212, 213). Thus, IDUs may derive less benefit from antiretroviral therapy because of decreased or delayed access to and utilization of health care services. Consequently, HAART may be less likely to be prescribed to IDUs, even when this would be medically indicated (62). In fact, active IDUs initiate HAART later than or at a more advanced stage of infection compared to other populations. This has been shown in a number of studies. In a multicenter hospital-based cohort, amongst treatment naïve patients followed from 1997 to 2003, IDUs were less likely to initiate HAART compared to other patient groups (214). Among a random sample of HIV-positive patients, CD4 cell counts at the time of initiation of HAART were lower in IDUs than in nonIDUs (215). In patients with ongoing heroin use, active IDUs were three times less likely to be prescribed HAART compared to former IDUs (212). However, the use of methadone was associated with an earlier uptake of HAART (216). Thus, methadone substitution therapy may decrease or avoid such delays and even improve access to HIV care.  1.4.4.3 Adherence Adherence to HAART is a key predictor of success for HIV-related therapeutic outcomes, including virologic suppression (137, 196), CD4 cell count responses (217, 218), antiretroviral drug resistance (197, 209), progression to AIDS (219) and mortality (220). Although there are many reasons for failure of HAART, including intolerance to medications and HIV drug resistance, non-adherence to antiretroviral drugs seems to be the most common reason (63, 221). The level of adherence needed to achieve and maintain virologic suppression appears to be over 90% for patients treated with unboosted PIs (196, 197) but the threshold of adherence is believed to be lower for patients treated with boosted PIs and NNRTIs (222, 223). Incomplete adherence to HAART can arise from different reasons. In the general population, typical reasons for missing HAART doses include simply forgetting, being busy, being away from home, concerns about being seen taking medications, changes in daily routine activities and failure to understand dosing or scheduling instructions (224). Factors associated with antiretroviral drugs can have a significant impact on adherence and include issues such as pill 22  burden, dosing frequency, meal restrictions and short-term toxicities (225). In IDUs, incomplete adherence can be related to instability caused by illicit drug use, incarceration, homelessness, poor tolerability of HAART, psychiatric disorders and other co-morbidities associated with injection drug use (226). Since IDUs are often considered to be non-adherent to treatment regimens, some physicians exclude them from or do not promptly treat them with HAART (212). Various studies demonstrate that IDUs have lower rates of adherence to HAART compared to other patient groups, with active substance abuse, psychiatric disorders and low social support being the strongest factors associated with poor adherence (226-228). This often leads to lower rates of virologic suppression and blunted CD4 cell count responses (217, 229). In addition, IDUs are significantly more likely to discontinue HAART after treatment initiation (230). Studies assessing adherence in this population, using patient self-reports, pharmacy records and electronic monitoring, indicate that low-to-moderate levels of adherence of 30-70% to HAART are achieved in IDUs, with higher adherence rates achieved among those engaged in comprehensive services providing HIV and addiction treatment with psychosocial support (227).  1.4.4.4 Co-infections Both HBV and HCV infections are acquired early after the initiation of intravenous injections. They commonly lead to chronic hepatitis with persistent transaminase liver enzymes abnormalities and eventually to cirrhosis and in some cases to hepatocellular carcinoma. In the developed world, it is estimated that the prevalence of HBV infection among HIV-infected IDUs is 7-10 % while the prevalence of HCV infection is 70-95% (231). Given the high prevalence of HBV/HCV, the treatment of HIV infection in co-infected IDUs can be problematic. In addition to the adverse effect of these 2 viral infections on the progression of HIV disease (71-73), they can directly or through anti-hepatitis drugs potentiate adverse events associated with some antiretroviral drugs. Clinical and pharmacological studies indicate that significant hepatotoxicity can be associated with a number of antiretroviral drugs such as nevirapine and full-dose ritonavir (232, 233). Since infection with chronic HBV or HCV is associated with an increased risk of drug-induced hepatotoxicity, the use of certain HAART regimens might therefore lead to further exacerbation of liver damage associated with chronic viral hepatitis (110, 232, 233). The treatment of HCV 23  infection, with pegylated interferon and ribavirin in co-infected patients receiving HAART, can lead to some significant adverse events such as anemia, mitochondrial toxicity and depression (234-236). For example, ribavirin use can result in hemolytic anemia particularly in patients receiving concomitant AZT (237), while it can increase mitochondrial toxicity (manifested by lactic acidosis, myopathy, neuropathy, pancreatitis) in patients receiving concomitant DDI or D4T (238). Besides co-infection with hepatitis B or C, a frequent medical problem in IDUs is intercurrent bacterial infections usually involving the skin and soft tissues (mainly cellulitis and abscesses) or the respiratory tract (mainly pneumonia). These infections are often associated with significant morbidity and mortality (239, 240), and with sepsis they constitute the most common reasons for emergency department visits and hospital admission by IDUs (241, 242). Other bacterial infections highly prevalent in this population include bacterial endocarditis and tuberculosis. All these infections may create barriers and complicate the treatment of HIV infection in IDUs.  1.4.4.5 Adverse Events Side effects from antiretroviral drugs are important barriers to the treatment of HIV infection because of their potential impact on adherence. Patients on HAART commonly suffer from side effects. In real-life settings, up to 25% of patients stop antiretroviral therapy within the first year of treatment because of adverse events (243). As mentioned previously, some of the side effects such as mitochondrial toxicity (from DDI and D4T), hemolytic anemia (from AZT) and hepatotoxicity (from NVP, TPV and RTV) can be more severe in IDUs co-infected with HCV. Treatment with EFV can lead to central nervous system side effects such as hallucinations, insomnia, nightmares and depression which may lead to further psychosis and exacerbation of psychiatric disorders in patients actively injecting illicit drugs. Besides co-infection with HBV/HCV and psychiatric illnesses, other factors contributing to the higher number of adverse events experienced by HIV-infected IDUs include concurrent effects of illicit drugs used, increased sensitivity to pain and symptoms related to other co-morbidities (244). In addition, HIV-infected IDUs may have low self esteem about their ability to adhere to HAART (245) or may have the perception that the side effects of antiretroviral drugs are intolerable (203). Consequently, IDUs report adverse events more than other patient groups. In a cohort study involving IDUs, the rate of treatment discontinuation during the study period was 24  44%. Being in jail (44%) and medication side effects (41%) were the most frequently cited reasons for HAART discontinuation (245). In a 7-year longitudinal study, IDUs were found to have a significantly higher number of side effects and had approximately twice the risk of reporting any side effect than non-IDUs (244).  1.4.4.6 Drug Interactions Another barrier to the treatment of HIV infection in IDUs is the adverse drug interaction of some medications with antiretroviral drugs. For individuals dealing with heroin addiction, pharmacokinetic interactions of antiretroviral agents with methadone or buprenorphine may be significant and require careful monitoring in clinical practice. This is important because opioid substitution therapy may alter metabolism of antiretroviral drugs resulting in increased toxicity or reduced efficacy. On the other hand, antiretroviral medications may alter the level of methadone or buprenorphine resulting in clinical withdrawal or overdose (246). Both drugs are metabolized in the liver by the cytochrome P450 isoenzyme system and most of the adverse drug interactions seem to be mediated through the induction or inhibition of the cytochrome enzymes by various antiretroviral drugs (246). NRTIs generally do not statistically significantly affect methadone levels and therefore do not lead to clinical withdrawal symptoms. Among the NNRTIs, co-administration of NVP or EFV and methadone may result in a significant reduction in levels of methadone, resulting in opioid withdrawal symptoms, which may threaten ongoing adherence to therapy (247, 248). Both NNRTIs exert their effect by inducing isoenzymes of the cytochrome P450 system in the liver. Concerning PIs, each drug has a unique profile of induction and inhibition of and metabolism by specific cytochrome P450 isoenzymes. The pharmacokinetic interactions of methadone with different PIs may or may not be clinically significant (246). As for the newer PIs, the clinical impact of these interactions remains unclear. The use of buprenorphine seems to be associated with fewer adverse drug interactions, compared with methadone treatment; however, the available information is largely anecdotal (249). Recent reports indicate that the use of ATV/RTV may result in an increased buprenorphine exposure causing oversedation and opioid excess (250). The inhibition and induction of P-glycoprotein (P-gp), an efflux protein located mainly in the gastrointestinal tract, is another pharmacokinetic mechanism by which antiretroviral drugs may 25  be affected. P-gp reduces intracellular concentrations of some antiretroviral agents and may be associated with a reduction in the efficacy of HAART (251). Thus, inhibition of P-gp may lead to increased antiviral activity (such as with using low dose RTV for boosting), whereas P-gp induction may lead to lower drug concentrations (such as with St. John’s wort) and increase the likelihood of therapeutic failure (251, 252). Other serious drug interactions with antiretroviral drugs include drugs used in the management of opportunistic infections and primary care such as anti-tuberculosis drugs (rifampin), azole antifungals (ketoconazole), macrolide antibiotics (clarithromycin), cholesterol lowering drugs (statins) and proton pump inhibitors (especially with some PIs such as ATV) (253-255). Finally, significant drug-drug interactions can occur between different antiretroviral drugs used as part of HAART resulting in increased toxicity or unfavorable therapeutic outcomes, such as with the concurrent use of DDI and TDF (256).  1.4.5 Treatment Strategies 1.4.5.1 Introduction Given all these barriers to treatment of HIV infection in IDUs, various programs have been developed in an attempt to address the multiple health issues associated with injection drug use in this population. Besides non-pharmacological interventions (Twelve Step programs) for the treatment of substance dependence, pharmacologic interventions based on methadone or buprenorphine substitution therapy (usually with daily witnessed ingestion) have been shown to be very effective in treating opioid addiction (257, 258). In addition, opioid substitution therapy has been found to be an important tool in promoting adherence to HAART among IDUs based on strategies such as directly observed therapy (DOT) also referred to as directly administered antiretroviral therapy (DAART) (207, 208), with the potential to twin addiction and HAART as a long-term means of engagement in care. Other strategies aimed at addressing the socio-structural barriers include various outreach programs, harm reduction services (such as needle exchange) and interventions based on community care settings which may further build on the DOT model (259).  1.4.5.2 Directly Observed Therapy (DOT) DOT is one the main and promising strategies proposed for the treatment of HIV infection in IDUs. This strategy has been used successfully for the treatment of pulmonary tuberculosis for 26  many years (260). DOT for tuberculosis has worked well among inner city residents who are socially unstable and live on the streets and for individuals with psychiatric illness and ongoing illicit drug use (261-263). Based on the success of DOT for tuberculosis, a model has been adapted for treating HIV infection in hard-to-treat populations such as IDUs. The DOT strategy is based on the direct observation and administration of antiretroviral medications to patients by trained medical staff. It aims at improving adherence to antiretroviral drugs, monitoring the multiple health issues associated with injection drug use, increasing access to HAART, limiting the development and transmission of drug resistance viruses and decreasing long-term morbidity and mortality from HIV/AIDS (205, 264). Various programs incorporating modified versions of DOT for the administration of HAART as an alternative for selfadministered therapy (SAT) have been implemented in recent years. Most studies of DOT have been conducted in institutional settings such as prisons (265-267) or methadone maintenance treatment clinics (268-271) or within resource-rich or resource-poor community-based settings (272-275). The majority of these studies, whether observational or randomized-controlled in design, have demonstrated improved therapeutic outcomes with the use of the DOT strategy in the treatment of HIV infection especially in marginalized populations. The most successful DOT programs to date have resided within methadone maintenance programs for patients receiving therapy for opioid dependence (268-271). In these methadone maintenance programs, patients are usually already required to present themselves daily to have their methadone prescription dispensed and ingestion observed. Therefore, by coupling the coadministration of HAART with methadone during these daily visits, it is possible to properly monitor adherence requirements to HAART in these patients.  1.4.5.3 Doubts about Directly Observed Therapy If compared with the treatment of tuberculosis, HIV infection is non-curable, requires lifelong daily treatment with HAART, has a different biology and dynamics, and cannot be mandated with the force of the law. These key differences pose unique challenges for the treatment of HIV infection using modified DOT. With the need for lifelong antiretroviral therapy, many researchers question the feasibility of implementing such a program over long periods of time (276). In addition, most of the DOT studies evaluate treatment outcomes over relatively short periods of time (i.e. 6 months) with the therapeutic gains being non-sustainable after 27  discontinuation of the program. Thus, some raise doubts about the durability of responses with the use of the DOT strategy (276, 277). Furthermore, a number of studies (including a recent randomized controlled study) do not show clear, long-term statistical differences in virologic and immunologic responses to HAART with the use of DOT strategy compared to SAT (278, 279). Given the rapidity with which HIV drug resistance can emerge in the setting of non-complete virologic suppression, the effect of DOT on the development of resistance may be difficult to predict (276). Data from several studies indicate that the risk of development of drug resistance is greatest in patients with intermediate-to-high levels of adherence (206, 209, 210). Therefore, it is possible that interventions that improve adherence, such as DOT, may paradoxically increase rates of drug resistance by increasing adherence to intermediate-to-high levels where the risk of development of drug resistance is highest in patients not achieving and maintaining full virologic suppression (280, 281). Finally, DOT is an intrusive intervention and daily meetings with health care workers may result in disclosure of HIV infection to family, peers and employers. Thus, some researchers raise concerns about the acceptability of DOT programs by patients because of issues related with stigma, confidentiality and freedom (281). In fact, some patients on HAART consider DOT to be intrusive and unnecessary despite their poor clinical outcomes with SAT (282). These and others concerns (such as cost) therefore raise many doubts about the use of DOT as an intervention in treating HIV infection in IDUs and other vulnerable populations.  28  1.5 Summary and Key Gaps The HIV pandemic continues to spread globally on an on-going basis with injection drug use accounting for a substantial proportion of new HIV infections (7). HIV-infected IDUs do not appear to have a more rapidly progressing natural history of disease compared to non-IDUs except for higher mortality rates (90, 91). Specific disease manifestations among IDUs differ from other patient groups because of their exposure to pathogens (such as HCV) that are associated with injection drug use. Although IDUs significantly benefit from HAART, data increasingly show a gap in treatment outcomes between IDUs and non-IDUs. Factors contributing to suboptimal treatment outcomes in IDUs include delayed access to treatment, incomplete adherence to HAART, increased potential for adverse events and toxicities, antiretroviral drug interactions with methadone or buprenorphine, co-infection with HBV or HCV, psychosocial barriers and competing co-morbid conditions. Optimal adherence, which is predictive of successful virologic suppression by HAART (196), may be more difficult to achieve in IDUs (264). Imperfect adherence to HAART may result in treatment failure and lead to the emergence of antiretroviral drug resistance which could compromise the benefits of HAART, limit future treatment options and result in the transmission of drug-resistant viruses (178). In fact, some physicians may be reluctant to prescribe HAART to drug addicts due to the common belief that IDUs will less likely respond to HAART and more likely develop and transmit HIV resistant viruses within their communities because of their poor adherence to HAART (202, 203). While on the one hand, it may be necessary to initiate HAART when IDUs are stabilized to ensure optimal adherence to treatment; on the other hand, delaying treatment to actively using IDUs may have serious and detrimental health consequences on the lives of these patients (226). In the face of all these challenges, various interventions for the treatment of HIV infection have been developed. These interventions include strategies that directly address known barriers to HAART (259). One such strategy, implemented successfully in the treatment of tuberculosis, is based on DOT (260). Modified versions of DOT for the administration of HAART have been used successfully in a number of settings such as prisons, methadone clinics or the community (265-275). Some researchers, however, raise many concerns about DOT programs because they might not be feasible, acceptable, sustainable or durable in treating vulnerable populations infected with HIV. In addition, some researchers suggest restraint in the enthusiasm for directly 29  observed HIV therapy since efficacy of HAART with DOT in preventing HIV transmission remains unproven and improved HAART adherence with DOT may paradoxically increase resistance to antiretroviral drugs (276, 281).  30  1.6 Purpose and Specific Aims The focus of this thesis will be to evaluate the treatment of HIV infection in IDUs within the context of an established methadone-based DOT program. Treatment of HIV infection in IDUs will be evaluated by assessing the initial and the long-term immunologic and virologic responses to HAART, the prevalence of primary drug resistance in drug naïve patients, the development of drug resistance while on treatment, the adverse events and toxicities with the use of HAART, the antiretroviral drug interactions with methadone and the factors associated with different treatment outcomes in patients receiving HAART. Taken together, these findings will represent an important contribution to the field and provide important guidance for the treatment of HIV infection in IDUs. In order to have a better understanding of treatment of HIV infection in IDUs, my work will primarily focus on the following aims: 1) To evaluate initial and long-term immunologic and virologic responses as well as retention to HAART with DOT relative to SAT. 2) To evaluate rates of accumulation of drug resistance mutations with DOT relative to SAT. 3) To estimate the prevalence of primary drug resistance in a cohort of IDUs naïve to antiretroviral drugs. 4) To evaluate treatment responses in the presence of mutations at RT codon 135 which are highly prevalent in some IDU cohorts. 5) To evaluate the incidence and correlates of hepatotoxicity in a cohort of HIV-infected IDUs receiving NVP-based HAART. 6) To evaluate methadone dose adjustments necessary to address symptoms of opiate withdrawal or toxicity as well as to evaluate clinical outcomes following the initiation of HAART.  31  1.7 References 1. Schoenbaum EE, Hartel D, Selwyn PA, Klein RS, Davenny K, Rogers M, Feiner CS, et al. 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AIDS 2003;17:1383-7. 55  282. Santos CQ, Adeyemi O, Tenorio AR. Attitudes toward directly administered antiretroviral therapy (DAART) among HIV-positive inpatients in an inner city public hospital. AIDS Care 2006;18:808-11.  56  CHAPTER II  Treatment of HIV Infection in Injection Drug Users: Directly Observed Therapy and Self Administered Therapy1  2.1 Introduction The availability of highly active antiretroviral therapy (HAART) has produced a substantial reduction in morbidity and mortality from HIV (1). However, a lack of rigorous adherence is the principal cause of treatment failure and the emergence of multi-drug resistance to HIV medications (2). This is all the more significant among injection drug users (IDUs) who remain a driving force in the spread of HIV infection in certain parts of the world (3). For example, studies from the Downtown Eastside of Vancouver, which is home to several thousand IDUs, report a prevalence of HIV infection of 25%, despite the presence of free access to medical treatment and antiretroviral therapy (4, 5). Unfortunately, there are multiple barriers to the appropriate treatment of HIV infection among IDUs. In general, IDUs derive less benefit from antiretroviral therapy because of decreased or delayed access to and utilization of health care services (6, 7). In addition, they tend to be less adherent to their medications because of their chaotic lifestyle (8, 9). Consequently, HAART may be less likely to be prescribed to IDUs, even when this would be medically indicated (10). These problems with adherence and access to care are further compounded by the increased potential for HAART-related adverse events and toxicities (11), pre-existing co-infection with hepatitis B or C virus (12), antiretroviral drug interactions with methadone or buprenorphine (13) and exacerbation of underlying psychiatric disorders which are common in this marginalized population (14). Therefore, strategies aimed at improving access and adherence to HAART for HIV-infected IDUs are necessary to improve their treatment outcomes. One strategy that has been proposed is directly observed therapy (DOT) (15, 16). This strategy has been used successfully for the treatment of pulmonary tuberculosis for many years (17). DOT aims at improving medication adherence through the direct observation and administration of antiretroviral medications to  1  A version of this chapter has been submitted for publication. Tossonian HK, Raffa JD, Grebely J, Viljoen M, Mead A, Khara M, McLean M, Duncan F, Smith C, DeVlaming S, Conway B. (2009) Treatment of HIV Infection in Injection Drug Users: Directly Observed Therapy and Self Administered Therapy.  57  patients by trained staff. Most studies of DOT have been conducted in institutional settings such as prisons (18-20) or methadone maintenance treatment clinics (21-24) or within communitybased settings (25-31). Most of these studies, observational (21-24, 27, 28) or randomized controlled (25, 29-31) in design, have demonstrated the effectiveness of DOT programs in the treatment of HIV infection in IDUs. Some authors have raised concerns about the increased risk of development of drug resistance mutations during periods of higher but not optimal adherence levels with DOT (32, 33). In a recent randomized controlled study, however, the use of DOT was found to have no impact on the development of drug resistance in IDUs receiving HAART (34). Some researchers have also raised concerns about the durability of treatment responses since most of the DOT studies have evaluated subjects over relatively short periods of time. In addition, a recent randomized controlled study has shown no differences in virologic and immunologic responses to HAART with the use of DOT strategy compared to self-administered therapy (SAT), although the trial did not focus on IDUs (35). With these considerations in mind, the aim of this study was to evaluate the treatment of HIV infection in IDUs within the context of an established DOT program using longitudinal data analysis. Treatment of HIV infection in IDUs was evaluated assessing initial and long-term immunologic and virologic responses to HAART, retention on therapy, treatment discontinuation and correlates of virologic suppression and treatment retention in patients receiving HAART.  2.2 Patients and Methods In a longitudinal cohort study we identified HIV-infected IDUs enrolled in a methadone treatment program at the Pender Community Health Center, a multidisciplinary clinic, located in the Downtown Eastside of Vancouver, Canada, where the treatment of addiction, HIV, hepatitis C virus and other infectious diseases are integrated via systematic collaboration between primary care physicians, addiction specialists, infectious disease specialists, nurses, counselors and researchers (21, 36). All data were collected prospectively in a standardized fashion or by retrospective chart review. Patients were current or past IDUs and most were residents of the Downtown Eastside. IDUs received HAART either as DOT or SAT. In the case of DOT, methadone and antiretroviral 58  agents were ingested under a community pharmacist’s supervision 7 days per week on a daily basis. Participants requiring twice-daily HAART were given the second dose to take at home and were questioned about its administration the following morning. In cases of high sustained adherence with a maximal virologic response to antiretroviral therapy on DOT, IDUs were given the option to switch to weekly or monthly dispensing, being supplied with antiretroviral agents on a weekly or monthly basis in blister packages. The rationale for moving patients to a less intense intervention was to allow them to manage adherence independently and allow us to manage the resources required to maintain the intensive support program, focusing on patients for whom adherence would be expected to be more problematic. However, patients were placed back on the daily intervention if treatment outcomes were not sustained. Weekly and monthly DOTs were considered as extensions of the daily DOT program and were evaluated on such a basis. In case of SAT, patients were given antiretroviral medications every 4-8 weeks and expected to ingest them on their own without any direct observation. In our study, the analysis was based on regimens rather than individuals since some regimens taken by patients were administered as DOT while other regimens as SAT, making it difficult to evaluate treatment outcomes based on patients exclusively assigned to DOT or SAT. Treatment discontinuation was defined as stopping all antiretrovirals completely for greater than 6 months or switching to a new regimen, regardless of the interruption period. Treatment interruption was defined as the period when HAART was stopped for less than 6 months, followed by the resumption of the same regimen. Treatment modification was defined as a change made to one of the backbone nucleoside reverse transcriptase inhibitor (NRTI) components of antiretroviral therapy without changing the other components of HAART. HAART regimens were chosen on an individualized clinical basis, taking into account previous treatment experience, with a view to designing a regimen likely to achieve maximal virologic suppression. As HIV resistance testing was not routinely done in most patients, HAART regimens were not determined based on genotypic test results. Regimens were mainly based on either non-nucleoside reverse transcriptase inhibitors (NNRTIs) or protease inhibitors (PIs), given along with 2 other NRTIs. Patients were counseled at HAART initiation to watch for signs of methadone withdrawal (if applicable) and monitored for clinical manifestations of drug toxicity on a regular basis. Missed doses were reported by the community pharmacist to the patient’s health care provider. 59  Baseline information was collected on relevant demographic, laboratory and clinical data and antiretroviral treatment history. HIV plasma viral load was measured using the Amplicor HIV-1 Monitor™ assay, version 1.5 (Roche Diagnostics, Mississauga, ON). Immunologic response was monitored using CD4 cell count, measured by flow cytometry at the local reference laboratory. HIV plasma viral load and CD4 cell counts were measured at baseline, and at approximately three-month intervals or more frequently if clinically indicated. To examine any differences which may exist between regimens that were administered DOT compared to those that were SAT, we constructed univariate logistic regression models to assess any factors which may be associated with receiving a DOT regimen. Variables which were assessed were: age, sex, hepatitis C status, dosing frequency, type of regimen, treatment interruption, treatment modification, line of therapy, pre-HAART experience, year of treatment initiation, lengths of treatment, baseline viral load and baseline CD4 cell count. In the case of age and baseline CD4 cell count, DOT status was considered the explanatory variable, with age and baseline CD4 cell count being separate response variables. In all other cases, DOT status was the response variable. Generalized estimating equations (GEE) were used to account for the correlated nature of repeated measurements (separate regimens) on each patient. An exchangeable working correlation structure was used in the case of DOT status being the response variable, while an independence working correlation structure was used when age and baseline CD4 cell count were the dependent variable. Statistical significance of multi-category factors (>2 categories) was determined using a Wald-test. An intent-to-treat analysis was used to evaluate treatment outcomes including virologic and immunologic responses, treatment retention and discontinuation of HAART at months 6, 12 and 24. Virologic suppression was assessed as the proportion of patients with plasma viral load <50 copies/mL. Immunologic responses were evaluated by the absolute CD4 cell count and the mean change in CD4 cell count from baseline. Treatment outcomes were analyzed in patients receiving HAART as DOT and SAT at the specified assessment points. Retention rates were determined by computing the percentage of patients who were still on HAART, while treatment discontinuation was assessed by determining the rates and causes of HAART discontinuation at months 6, 12 and 24. Descriptive statistics were also computed including lengths of treatment, continuity of treatment, rates and lengths of treatment interruption, rates and causes of treatment modification and graduation from daily DOT. 60  Factors associated with virologic suppression at 6, 12 and 24 months were assessed by multivariable logistic regression models while factors associated with retention on HAART were assessed using Cox Proportional Hazard models. The factors associated with virologic suppression and treatment retention included the following: age, sex, use of DOT, hepatitis C status, dosing frequency, type of treatment, line of therapy, pre-HAART and HAART experience, treatment interruption, treatment modification, baseline (regimen) viral load and baseline (regimen) CD4 cell count. Differences in absolute CD4 cell count and changes from baseline in CD4 cell count between regimens taken under DOT and SAT were assessed using multivariable linear regression. In all cases, GEE were used to account for the correlated nature of repeated measurements (separate regimens) on each patient. An exchangeable working correlation structure was used for the virologic suppression and retention analyses while an independence working correlation structure was used when baseline CD4 count was the dependent variable. In all multivariable models, initial models were fit with all predictor variables listed above and subject to backwards elimination. All reported p-values were twosided, and p-values below a significance level of 0.05 were considered statistically significant.  2.3 Results A total of 171 IDUs initiated HIV treatment between June 1996 and August 2007, of whom 64 (37%) were females. Most patients were co-infected with hepatitis C virus (94% carrying antiHCV antibodies). The patients were followed up for a mean of 3.5 years. During the study period, DOT was used in 135 (79%) patients while SAT in 119 (70%) patients. The median baseline plasma virus load was >100,000 copies/mL and the mean baseline CD4 cell count was 263 cells/mm3 in the overall group of patients. The baseline characteristics of the patients are shown in Table 2.1. The study evaluated a total of 477 regimens (Figure 2.1). Of these, 252 (53%) were taken as DOT while 225 (47%) as SAT. Among the DOT regimens, 209 (83%) were ingested based on daily DOT, 30 (12%) on weekly DOT and 13 (5%) on monthly DOT. Factors associated with being on a DOT regimen included older age (p<0.001), PI-based HAART (p=0.008), once-daily dosing (p<0.001), prior exposure to HAART (p<0.001), modifications during therapy (p=0.002), treatment during later periods of the study (p<0.001), longer treatment length (p<0.001), lower  61  CD4 cell counts at baseline (p=0.021) and treatment initiation with a suppressed viral load (p<0.001) (Table 2.2). In all regimens evaluated, virologic suppression (HIV RNA <50 copies/mL) was achieved in 135/477 (28%), 116/477 (24%) and 73/477 (15%) of regimens at months 6, 12 and 24, respectively. However, suppression rates were statistically significantly higher using the DOT intervention as compared to the standard of care. With DOT, undetectable viral loads were achieved in 98/252 (39%), 91/252 (36%) and 58/252 (23%) of cases while with SAT, in 37/225 (16%), 25/225 (11%) and 15/225 (7%) of cases at months 6, 12 and 24, respectively (p<0.001, for all comparisons between DOT and SAT) (Table 2.3). As seen in Table 2.3, increases in CD4 cell counts were observed throughout the study period with both DOT and SAT. At 6 months of treatment, the mean changes in CD4 cell counts were +78 cells/mm3 with all regimens combined, +75 cells/mm3 with DOT and +87 cells/mm3 with SAT, with the differences being not statistically significant (p=0.62). Better immunologic responses were obtained with DOT at months 12 and 24; however, the differences were not statistically significant. The mean changes in CD4 cell counts were + 87 cells/mm3 and +105 cells/mm3 in the overall group, +95 cells/mm3 and +127 cells/mm3 with DOT and +64 cells/mm3 and +36 cells/mm3 with SAT at 12 and 24 months, respectively (p=0.24, p=0.21). In the overall group, retention on HAART was reported in 315/477 (66%), 219/477 (46%) and 110/477 (23%) of cases at months 6, 12 and 24, respectively. However, looking into retention rates with DOT and SAT, there were statistically significant differences in the rates of retention at all assessment points. At months 6, 12 and 24, with regimens using DOT, IDUs were retained on treatment in 185/252 (73%), 137/252 (54%) and 76/252 (30%) of cases as compared to 130/225 (58%), 82/225 (36%) and 34/225 (15%) in regimens using SAT (p<0.001, for all comparisons between DOT and SAT) (Table 2.3). The overall rate of treatment discontinuation was relatively high in the study, especially in the SAT group. Most notably, there was more self discontinuation of HAART with the use of the SAT (43%) compared with the use of DOT (30%). A summary of the causes and rates of treatment discontinuation in both study groups is shown in Table 2.4.  62  In a multivariable logistic regression analysis, use of DOT, later initiation of HAART during the study period, older age, modifications during therapy, earlier lines of treatment and having an undetectable viral load at baseline were associated with virologic suppression at month 6. Similar factors were found to be associated with virologic suppression at months 12 and 24 of treatment. In addition, having a baseline CD4 cell count >200 cells/mm3 at month 12 and being hepatitis C-negative at month 24 were associated with virologic suppression. The results of multivariable logistic regression analysis of factors associated with virologic suppression at months 6, 12 and 24 are shown in Table 2.5. Finally, with respect to factors associated with retention on HAART, use of DOT, older age, modifications during therapy, being hepatitis Cnegative, treatment during earlier periods of the study, non-continuous therapy and having an undetectable viral load at baseline were associated with retention on antiretroviral therapy (Table 2.6).  2.4 Discussion DOT is becoming an important strategy for improving treatment outcomes in HIV-infected IDUs at risk of being more non-adherent to antiretroviral therapy. However, few studies have evaluated the long-term feasibility and efficacy of DOT within real-life settings in key target populations. The results of our study show that the treatment of HIV infection in IDUs can be achieved successfully within a DOT program especially when ongoing participation in a methadone maintenance program can provide a preexisting infrastructure for the easy implementation of such a strategy. The results from this longitudinal study demonstrated superior virologic responses in patients who received regimens within a DOT setting compared to those who received treatment as SAT over a period of 2 years. Compared to other DOT studies (23, 27, 31), we reported lower response rates. This is likely due to the fact that patients who had missing data or were switching or terminating therapy were considered as treatment failures. Although we did not directly measure virologic response using the on-treatment analysis, we expect that more than 70% of our IDUs were virologically suppressed based on previous results published by our group (37). Improved immunological responses, measured as an increase in the CD4 cell count, were observed with both DOT and SAT but such responses were not sustained with SAT. It is worth noting that patients initiating regimens within a SAT setting had higher CD4 cell counts at 63  baseline while patients initiating HAART within a DOT setting included a larger number of patients with prior antiretroviral experience, thus biasing us against being able to show an immunologic benefit of the DOT intervention. In addition, the increases in CD4 cell counts with DOT or SAT were modest during the study. Nevertheless, the results are encouraging because CD4 cell responses were maintained and improved throughout the study period with the use of the DOT intervention. In the current study, retention rates were comparable to those reported in other published studies (28, 31, 34, 38, 39) despite the fact that we looked at regimens as the unit of analysis as opposed to patients (as in other DOT studies). These rates were statistically significantly higher with DOT compared to the standard of care at month 6, 12 and 24. Although even higher rates of retention would be more desirable, the impact of what we have already been able to achieve has shown a measurable and sustained virologic benefit. It is worth mentioning that patients who were not kept on their regimens for periods of 2 years or more were not terminating HIV treatment or dropping out of the program. In most cases, such patients were switching to other regimens, which may themselves have been successful. Although beyond the scope of the current report, some patients were successfully retained on a single regimen for periods up to 6 years. The rates of treatment discontinuation were high in our population of IDUs, which is consistent with results from previous studies conducted in this population (40). There was more discontinuation of HAART with SAT compared to DOT. The reasons for treatment discontinuation varied but most of them were driven by two main factors, namely, self discontinuation and adverse events. Notably, there was more HAART discontinuation because of patient decision with or without drug relapse in the SAT arm, while this related more often to drug interactions (mainly interactions of methadone with antiretroviral drugs) in the DOT arm. In our study, one-third of the discontinuations were due to side effects. Initially, such rates might seem to be a major concern. However, given the intensive nature of HAART, the effect of concurrent illicit drug use and the unstable chaotic lifestyle of the patients, these rates may actually be considered acceptable. Some studies have reported that IDUs experience a significantly higher number of side effects than non-IDUs (11). Thus, providing appropriate care to reduce self discontinuation and side effects remain a major challenge, but one that is more easily addressed within a DOT program. 64  After adjusting for potential confounders in a multiple regression model, the use of the DOT intervention was found to be associated with virologic suppression at months 6, 12 and 24. This supports the study by Lucas et al. that has shown that IDUs on directly administered antiretroviral therapy (DAART) were significantly more likely to achieve viral suppression compared to IDU-methadone, IDU-non-methadone and non-IDU groups (23). In our study, the use of DOT was also associated with statistically significant retention on HAART. Therefore, one mechanism by which DOT may improve treatment responses in IDUs is by increasing retention in treatment programs, while another more known mechanism is by increasing adherence to antiretroviral medications (although in our study we did not measure adherence directly). Modifications during therapy were more common in regimens based on DOT, probably due to the fact that DOT patients were more closely monitored for clinical manifestations of drug toxicity or signs of methadone withdrawal. Such modifications were associated with virologic suppression and retention on HAART throughout the study period. In a recent study by Altice et al. patients receiving DAART were more likely to change their regimens to simplified once-daily regimens (31). In our study, NRTI components of HAART were changed to avoid adverse events, initiate hepatitis C treatment (use of some NRTIs contraindicated with ribavirin) and to simplify HAART regimens. On the other hand, our study showed that treatment interruption was associated with retention on HAART. This is likely because interruptions were occurring in patients who were experiencing adverse events to which we could react in a productive manner, maintaining engagement in care within the DOT program. Finally, in our study, some patients highly adherent to treatment were switched to weekly or monthly DOT as they were considered graduates of the DOT program. Although we did not assess these patients separately due to the limited number of DOT “graduates”, such an approach could be a solution to the question that arises regarding the length of keeping patients on daily DOT since the treatment for HIV infection is life-long. Future studies of DOT should allow us to evaluate the long-term impact of switching patients from daily to weekly or monthly DOT. However, the ultimate goal should be provide flexibility with DOT and enable patients to selfadminister their medications over time.  65  One of the main limitations of the study is that treatment intervention was not assigned randomly and some data were collected retrospectively by chart review, raising the possibility of selection, entry as well as other biases. However, most of the highly non-adherent IDUs were placed on DOT (and IDUs known to be adherent to therapy on SAT), a situation that would have probably biased the study against demonstrating a benefit of DOT. The fact that such a benefit could still be demonstrated may actually be an underestimate the potential benefit of the intervention. In addition, regimens used were heterogeneous, so that it may be that the benefit of DOT may vary as a function of the type of regimen (NNRTI or PI-based) or some other factors that we were not able to measure. In addition, we did not measure adherence directly in this report, so we are not able to comment directly on the existence of an adherence “threshold” below which SAT would be more inferior to DOT. Finally, most of the IDUs enrolled in this study were also enrolled in a methadone maintenance program. Thus, the results might not be generalized to other settings, such as individuals for whom cocaine or crystal methamphetamines are the only drugs of addiction to the exclusion of heroin. In conclusion, this longitudinal study of HIV treatment in IDUs show that regimens taken within a DOT setting can be associated with improved virologic and immunologic outcomes compared to regimens taken as standard SAT. We believe this is due not only to increased adherence to HAART but also maintained engagement of patients in long-term medical care, in our case within a multidisciplinary community clinic. These results are extremely encouraging, and speak to the need for further study of DOT programs in this population, to include the evaluation of specific regimen types, criteria from step-down from DOT, durability of response and effect on long-term mortality. Most importantly, this could be an important strategy to allow us to address the underlying problem of addiction in the most efficient way possible.  66  Table 2.1 Baseline patient characteristics Number of patients  171  Age (SD)  38.1 (8.5)  Male (%)  107 (63)  Hepatitis C-positive (%)  161 (94)  Ever on directly observed therapy (%)  135 (79)  Ever on self-administered therapy (%)  119 (70)  Mean follow-up (Years, SD)  3.54 (2.25) 3  Mean CD4 (SD) in cells/mm  263 (219)  Median viral load (Q1-Q3) in copies/mL  >100,000 (40,800->100,000)  NOTE. Q1 indicates first quartile; Q3 indicates third interquartile; SD indicates standard deviation  67  Table 2.2 Factors associated with being on a DOT regimen Overall (N=477)  DOT (N=252)  SAT (N=225)  p-value  Age (years) Mean 38.5 39.7 37.0 <0.001 SD 7.6 7.3 7.5 Sex Male (%) 309 (65) 166 (66) 143 (64) 0.83 Female (%) 168 (35) 86 (34) 82 (36) Hepatitis C status Positive (%) 457 (96) 242 (96) 215 (96) 0.75 Negative (%) 20 (4) 10 (4) 10 (4) Dosing frequency Once-daily (%) 230 (48) 167 (66) 63 (28) <0.001 Twice-daily (%) 200 (42) 84 (33) 116 (52) Thrice-daily (%) 47 (10) 1 (1) 46 (20) Regimen based on NRTIs (%) 18 (4) 14 (6) 4 (2) 0.008 NNRTIs (%) 185 (39) 86 (34) 99 (44) PIs (%) 274 (57) 152 (60) 122 (54) Continuity of treatment Continuous (%) 393 (82) 209 (83) 184 (82) 0.65 Interrupted (%) 84 (18) 43 (17) 41 (18) Interruption length (months) Mean 0.58 0.46 0.72 0.059 SD 1.55 1.23 1.82 Modifications during therapy 9 (4) 0.002 With changes (%) 40 (8) 31 (12) 216 (96) Without changes (%) 437 (92) 221 (88) Line of therapy First-line (%) 128 (27) 47 (19) 81 (36) <0.001 Second-line (%) 192 (40) 82 (32) 110 (49) Third-line (%) 130 (27) 105 (42) 25 (11) > Third-line (%) 27 (6) 18 (7) 9 (4) Pre-HAART experience Pre-HAART (%) 148 (31) 71 (28) 77 (34) 0.15 HAART only (%) 329 (69) 181 (72) 148 (66) Year group of treatment 1996-1999 (%) 127 (27) 12 (5) 115 (51) <0.001 2000-2004 (%) 312 (65) 208 (82) 104 (46) 2005-2007 (%) 38 (8) 32 (13) 6 (3) Regimen length (days)∗ 463 559 354 <0.001 Mean 460 512 365 SD Baseline CD4 count (cells/mm3) Mean 212 193 237 0.021 SD 199 197 199 Baseline viral load (copies/mL)∗∗ 25 (5) 22 (9) 3 (1) <0.001 <50 (%) 22 (5) 16 (6) 6 (3) 50-1,000 (%) 213 (45) 117 (46) 96 (44) 1,000-100,000 (%) 211 (45) 97 (39) 114 (52) >100,000 (%) NOTE. DOT indicates directly observed therapy; HAART indicates highly active antiretroviral therapy; NNRTI indicates non-nucleoside reverse transcriptase inhibitor; NRTI indicates nucleoside reverse transcriptase inhibitor; PI indicates protease inhibitor; SAT indicates self administered therapy; SD indicates standard deviation; *p-value based on log transformation of the regimen length; ∗∗baseline viral load missing in 6 patients.  68  Table 2.3 Treatment outcomes at months 6, 12 and 24 Overall  DOT  SAT  p-value*  Baseline (%)  477 (100)  252 (100)  225 (100)  -  Month 6 (%)  315 (66)  185 (73)  130 (58)  <0.001  Month 12 (%)  219 (46)  137 (54)  82 (36)  <0.001  110 (23)  76 (30)  34 (15)  <0.001  Baseline (SD)  212 (199)  193 (197)  237 (199)  0.021  Month 6 (SD)  289 (219)  273 (208)  326 (241)  0.14  Month 12 (SD)  330 (201)  319 (189)  359 (232)  0.23  386 (229)  383 (240)  394 (197)  0.79  Baseline (SD)  0  0  0  -  Month 6 (SD)  +78 (142)  +75 (139)  +87 (150)  0.62  Month 12 (SD)  +87 (145)  +95 (146)  +64 (144)  0.24  Month 24 (SD)  +105 (214)  +127 (180)  +36 (297)  0.21  Baseline (%)  25 (5)  22 (9)  3 (1)  0.001  Month 6 (%)  135 (28)  98 (39)  37 (16)  <0.001  Month 12 (%)  116 (24)  91 (36)  25 (11)  <0.001  Month 24 (%)  73 (15)  58 (23)  15 (7)  <0.001  Retention on treatment  Month 24 (%) 3  Mean CD4 count (cells/mm )  Month 24 (SD) 3  Mean change in CD4 count (cells/mm )  Viral load suppression (<50 copies/mL)  NOTE. DOT indicates directly observed therapy; SAT indicates self administered therapy; SD indicates standard deviation; *p-values are un-adjusted.  69  Table 2.4 Causes and rates of treatment discontinuation at months 6, 12 and 24 Overall  DOT  All causes Month 6 (%) 162/477 (34) 67/252 (27) Month 12 (%) 258/477 (54) 115/252 (46) Month 24 (%) 367/477 (77) 176/252 (70) Self discontinuation and relapse Month 6 (%) 60/162 (37) 21/67 (31) Month 12 (%) 103/258 (40) 38/115 (33) Month 24 (%) 134/367 (37) 53/176 (30) Adverse events Month 6 (%) 66/162 (41) 29/67 (43) Month 12 (%) 86/258 (33) 42/115 (36) Month 24 (%) 109/367 (30) 55/176 (31) Virologic failure Month 6 (%) 1/162 (1) 1/67 (2) Month 12 (%) 10/258 (4) 6/115 (5) Month 24 (%) 28/367 (7) 14/176 (8) Drug interactions Month 6 (%) 8/162 (5) 7/67 (10) Month 12 (%) 14/258 (5) 10/115 (9) Month 24 (%) 23/367 (6) 13/176 (7) Loss to follow-up Month 6 (%) 10/162 (6) 3/67 (4) Month 12 (%) 18/258 (7) 8/115 (7) Month 24 (%) 21/357 (6) 11/176 (6) Treatment simplification Month 6 (%) 4/162 (2) 1/67 (2) Month 12 (%) 7/258 (3) 3/115 (3) Month 24 (%) 15/367 (4) 7/176 (4) Incarceration Month 6 (%) 5/162 (3) 1/67 (2) Month 12 (%) 8/258 (3) 2/115 (2) Month 24 (%) 12/367 (3) 5/176 (3) Study participation Month 6 (%) 3/162 (2) 1/67 (2) Month 12 (%) 5/258 (2) 2/115 (2) Month 24 (%) 13/367 (4) 10/176 (6) Death Month 6 (%) 5/162 (3) 3/67 (4) Month 12 (%) 7/258 (3) 4/115 (3) Month 24 (%) 12/367 (3) 8/176 (5) NOTE. DOT indicates directly observed therapy; SAT indicates self administered therapy  SAT 95/225 (42) 143/225 (64) 191/225 (85) 39/95 (41) 65/143 (45) 81/191 (43) 37/95 (39) 44/143 (31) 54/191 (28) 0/95 (0) 4/143 (3) 14/191 (7) 1/95 (1) 4/143 (3) 10/191 (5) 7/95 (8) 10/143 (7) 10/191 (5) 3/95 (3) 4/143 (3) 8/191 (4) 4/95 (4) 6/143 (4) 7/191 (4) 2/95 (2) 3/143 (2) 3/191 (2) 2/95 (2) 3/143 (2) 4/191 (2)  70  Table 2.5 Factors associated with virologic suppression at months 6, 12 and 24 Adjusted Odds  95% Confidence  p-value  Ratio  Intervals  Use of DOT  2.74  1.71-4.38  <0.001  Time since starting therapy (per year)  1.27  1.07-1.51  0.007  Age (per 10 year increase)  2.06  1.53-2.78  <0.001  Modifications during therapy  3.06  1.58-5.94  <0.001  Third-line of therapy  0.31  0.13-0.74  0.009  >Third-line of therapy  0.19  0.05-0.78  0.021  Baseline viral load <50 copies/mL  4.99  1.49-16.73  0.009  Use of DOT  4.33  2.40-7.80  <0.001  Age (per 10 year increase)  1.67  1.21-2.27  0.002  Modifications during therapy  3.39  1.69-6.81  <0.001  Baseline CD4 <200 cells/mm3  0.52  0.31-0.86  0.017  Baseline viral load <50 copies/mL  4.92  1.72-14.02  0.003  Use of DOT  3.17  1.57-6.41  0.001  Age (per 10 year increase)  1.76  1.19-2.61  0.005  Hepatitis C-positive  0.27  0.08-0.89  0.032  13.63  6.17-30.10  <0.001  Baseline CD4 <200 cells/mm  0.44  0.23-0.83  0.012  Baseline viral load <50 copies/mL  2.95  1.01-8.59  0.047  Factors at month 6  Factors at month 12  Factors at month 24  Modifications during treatment 3  NOTE. DOT indicates directly observed therapy  71  Table 2.6 Factors associated with retention on HAART Factor  Hazard Ratio  95% Confidence Intervals  p-value  Use of DOT  1.92  1.49-2.50  <0.001  Age (per 10 year increase)  1.39  1.20-1.61  <0.001  Modifications during therapy  2.94  2.00-4.35  <0.001  Hepatitis C-positive  0.49  0.28-0.85  0.012  Treatment between 2000-2004  0.65  0.50-0.86  0.002  Treatment between 2005-2007  0.76  0.42-1.37  0.35  Continuous therapy  0.51  0.40-0.65  <0.001  Baseline viral load <50 copies/mL  2.94  1.75-5.00  <0.001  NOTE. DOT indicates directly observed therapy  72  Figure 2.1 Flow chart of study patients and regimens  IDU cohort patients N=215  Patients naïve to HAART N=36  Patients initiated HAART N=179  Patients newly on HAART excluded N=8  Total patients considered N=171  Total regimens evaluated N=477  Regimens taken as DOT N=252  Regimens taken as SAT N=225  Daily dispensed N=209  Weekly dispensed N=30  Monthly dispensed N=13 NOTE. 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Clin Infect Dis 2001;33:1417-23. 9. Stein MD, Rich JD, Maksad J, Chen MH, Hu P, Sobota M, Clarke J. Adherence to antiretroviral therapy among HIV-infected methadone patients: effect of ongoing illicit drug use. Am J Drug Alcohol Abuse 2000;26:195-205. 10. Celentano DD, Galai N, Sethi AK, Shah NG, Strathdee SA, Vlahov D, Gallant JE. Time to initiating highly active antiretroviral therapy among HIV-infected injection drug users. AIDS 2001;15:1707-15.  74  11. Carrieri MP, Villes V, Raffi F, Protopopescu C, Preau M, Salmon D, Taieb A, et al. Selfreported side-effects of anti-retroviral treatment among IDUs: a 7-year longitudinal study (APROCO-COPILOTE COHORT ANRS CO-8). Int J Drug Policy 2007;18:288-95. 12. Fisher DG, Reynolds GL, Jaffe A, Perez MJ. Hepatitis and human immunodeficiency virus co-infection among injection drug users in Los Angeles County, California. J Addict Dis 2006;25:25-32. 13. Bruce RD, Altice FL, Gourevitch MN, Friedland GH. Pharmacokinetic drug interactions between opioid agonist therapy and antiretroviral medications: implications and management for clinical practice. J Acquir Immune Defic Syndr 2006;41:563-72. 14. Chander G, Himelhoch S, Moore RD. Substance abuse and psychiatric disorders in HIVpositive patients: epidemiology and impact on antiretroviral therapy. Drug 2006;66:769-89. 15. Behforouz HL, Farmer PE, Mukherjee JS. From directly observed therapy to accompagnateurs: enhancing AIDS treatment outcomes in Haiti and in Boston. Clin Infect Dis 2004;38:S429-36. 16. Stenzel MS, McKenzie M, Mitty JA, Flanigan TP. Enhancing adherence to HAART: a pilot program of modified directly observed therapy. AIDS Read 2001;11:317-9, 324-8. 17. Chaulk CP, Kazandjian VA. Directly observed therapy for treatment completion of pulmonary tuberculosis: Consensus Statement of the Public Health Tuberculosis Guidelines Panel. JAMA 1998;279:943-8. 18. Fischl M, Castro J, Monroig R, Scerpella E, Thompson L, Rechtine D, Thomas D. Impact of directly observed therapy on long-term outcomes in HIV clinical trials [abstract 528]. In: Program and Abstracts of the 8th Conference on Retroviruses and Opportunistic Infections (Chicago). Alexandria, VA: Foundation for Retroviruses and Human Health, 2001:202. 19. Kirkland LR, Fischl MA, Tashima KT, Paar D, Gensler T, Graham NM, Gao H, et al. Response to lamivudine-zidovudine plus abacavir twice daily in antiretroviral-naive, incarcerated patients with HIV infection taking directly observed treatment. Clin Infect Dis 2002;34:511-8. 20. Wohl DA, Stephenson BL, Golin CE, Kiziah CN, Rosen D, Ngo B, Liu H, et al. Adherence to directly observed antiretroviral therapy among human immunodeficiency virus-infected prison inmates. Clin Infect Dis 2003;36:1572-6. 21. Conway B, Prasad J, Reynolds R, Farley J, Jones M, Jutha S, Smith N, et al. Directly observed therapy for the management of HIV-infected patients in a methadone program. Clin Infect Dis 2004;38:S402-8. 75  22. Lucas GM, Weidle PJ, Hader S, Moore RD. Directly administered antiretroviral therapy in an urban methadone maintenance clinic: a nonrandomized comparative study. Clin Infect Dis 2004;38:S409-13. 23. Lucas GM, Mullen BA, Weidle PJ, Hader S, McCaul ME, Moore RD. Directly administered antiretroviral therapy in methadone clinics is associated with improved HIV treatment outcomes, compared with outcomes among concurrent comparison groups. Clin Infect Dis 2006;42:162835. 24. Tyndall MW, McNally M, Lai C, Zhang R, Wood E, Kerr T, Montaner JG. Directly observed therapy programmes for anti-retroviral treatment amongst injection drug users in Vancouver: Access, adherence and outcomes. Int J Drug Policy 2007;18:281-7. 25. Altice FL, Mezger JA, Hodges J, Bruce RD, Marinovich A, Walton M, Springer SA, et al. Developing a directly administered antiretroviral therapy intervention for HIV-infected drug users: implications for program replication. Clin Infect Dis 2004;38:S376-87. 26. Wohl AR, Garland WH, Squires K, Witt M, Larsen R, Kovacs A, Hader S, et al. The feasibility of a community-based directly administered antiretroviral therapy program. Clin Infect Dis 2004;38:S388-92. 27. Macalino GE, Mitty JA, Bazerman LB, Singh K, McKenzie M, Flanigan T. Modified directly observed therapy for the treatment of HIV-seropositive substance users: lessons learned from a pilot study. Clin Infect Dis 2004;38:S393-7. 28. Mitty JA, Macalino GE, Bazerman LB, Loewenthal HG, Hogan JW, MacLeod CJ, Flanigan TP. The use of community-based modified directly observed therapy for the treatment of HIVinfected persons. J Acquir Immune Defic Syndr 2005;39:545-50. 29. Macalino GE, Hogan JW, Mitty JA, Bazerman LB, Delong AK, Loewenthal H, Caliendo AM. A randomized clinical trial of community-based directly observed therapy as an adherence intervention for HAART among substance users. AIDS 2007;21:1473-7. 30. Smith-Rohrberg D, Mezger J, Walton M, Bruce RD, Altice FL. Impact of enhanced services on virologic outcomes in a directly administered antiretroviral therapy trial for HIV-infected drug users. J Acquir Immune Defic Syndr 2006;43:S48-53. 31. Altice FL, Maru DS, Bruce RD, Springer SA, Friedland GH. Superiority of directly administered antiretroviral therapy over self-administered therapy among HIV-infected drug users: a prospective, randomized, controlled trial. Clin Infect Dis 2007;45:770-8. 32. Lucas GM, Flexner CW, Moore RD. Directly administered antiretroviral therapy in the treatment of HIV infection: benefit or burden? AIDS Patient Care STDS 2002;16:527-35. 76  33. Goicoechea M, Best B, Seefried E, Wagner G, Capparelli E, Haubrich R; California Collaborative Treatment Group (CCTG). Failure of modified directly observed therapy combined with therapeutic drug monitoring to enhance antiretroviral adherence in a patient with major depression. AIDS Patient Care STDS 2006;20:233-7. 34. Maru DS, Kozal MJ, Bruce RD, Springer SA, Altice FL. Directly administered antiretroviral therapy for HIV-infected drug users does not have an impact on antiretroviral resistance: results from a randomized controlled trial. J Acquir Immune Defic Syndr 2007;46:555-63. 35. Wohl AR, Garland WH, Valencia R, Squires K, Witt MD, Kovacs A, Larsen R, et al. A randomized trial of directly administered antiretroviral therapy and adherence case management intervention. Clin Infect Dis 2006;42:1619-27. 36. Conway B, Grebely J, Tossonian H, Lefebvre D, DeVlaming S. A systematic approach to the treatment of HIV and hepatitis C virus infection in the inner city: a Canadian perspective. Clin Infect Dis 2005;41:S73-8. 37. Tossonian HK, Raffa JD, Grebely J, Trotter B, Viljoen M, Mead A, Khara M, et al. Methadone dosing strategies in HIV-infected injection drug users enrolled in a directly observed therapy program. J Acquir Immune Defic Syndr 2007;45:324-7. 38. Garland WH, Wohl AR, Valencia R, Witt MD, Squires K, Kovacs A, Larsen R, et al. The acceptability of a directly-administered antiretroviral therapy (DAART) intervention among patients in public HIV clinics in Los Angeles, California. AIDS Care 2007;19:159-67. 39. Maru DS, Bruce RD, Walton M, Mezger JA, Springer SA, Shield D, Altice FL. Initiation, adherence, and retention in a randomized controlled trial of directly administered antiretroviral therapy. AIDS Behav 2008;12:284-93. 40. Kerr T, Marshall A, Walsh J, Palepu A, Tyndall M, Montaner J, Hogg R, et al. Determinants of HAART discontinuation among injection drug users. AIDS Care 2005;17:539-49.  77  CHAPTER III  HIV Drug Resistance in Injection Drug Users Receiving HAART within a Directly Observed Therapy Program2  3.1 Introduction Highly active antiretroviral therapy (HAART) for the treatment of HIV infection has remarkably improved survival and reduced progression to AIDS (1). However, incomplete and inconsistent adherence to HAART can lead to the emergence of resistance to antiretroviral drugs and as a consequence may compromise the benefits of antiretroviral therapy (2). This is all the more significant among injection drug users (IDUs) who are less likely to demonstrate a high level of adherence to antiretroviral medications (3, 4). Interventions based on directly observed therapy (DOT), for the administration of HAART, have been shown to be effective for the treatment of HIV infection among IDUs in a number of settings (5-10). However, higher levels of adherence with DOT does not necessarily mean lower rates of emergence of drug resistance mutations (DRMs). In fact, several studies have suggested that the risk of development of drug resistance is greatest in patients with intermediate-to-high level of adherence (11-14). It is possible that interventions that improve adherence, such as DOT, may paradoxically increase rates of drug resistance by increasing adherence to intermediate-to-high levels in patients who do not achieve full adherence. However, there are few studies that have closely assessed the impact of DOT may have on the emergence of DRMs. In a study using a Markov computer simulation model, the use of DOT was associated with improved clinical outcomes in populations with low levels of adherence but was not effective at preventing drug resistance (15). In fact, it was associated with higher rates of resistance mutations in treatment-experienced patients receiving non-ritonavir protease inhibitor (PI)-based therapy (15). On the other hand, in a recent randomized controlled study of directly administered antiretroviral therapy (DAART) compared with self-administered therapy (SAT), the use of DOT was found to have no impact on the development of drug resistance in IDUs receiving HAART (16). 2  A version of this chapter has been submitted for publication. Tossonian HK, Raffa JD, Grebely J, Viljoen M, Mead A, Khara M, McLean M, Duncan F, Smith C, DeVlaming S, Conway B. (2009) HIV Drug Resistance in Injection Drug Users Receiving HAART within a Directly Observed Therapy Program.  78  With this in mind, the aim of our study was to evaluate the rates of accumulation of DRMs in HIV-infected IDUs receiving HAART through DOT relative to SAT, using longitudinal data analysis within the context of an established DOT program.  3.2 Patients and Methods In a longitudinal, prospective and retrospective, cohort study we identified HIV-infected IDUs enrolled in a methadone treatment program at the Pender Community Health Center, a multidisciplinary clinic, located in the Downtown Eastside of Vancouver, Canada, whereby the treatment of addiction, HIV, hepatitis C virus and other infectious diseases are integrated via systematic collaboration involving primary care physicians, addiction specialists, infectious disease specialists, nurses, counselors and researchers (5, 17). All patients were current or past IDUs and most were residents of the Downtown Eastside. IDUs received HAART either as DOT or SAT between June 1996 and August 2007. In the case of DOT, methadone and antiretroviral agents were ingested under a community pharmacist’s supervision 7 days per week on a daily basis. Participants requiring twice-daily HAART were given the second dose to take at home and were questioned about its administration the following morning. In case of SAT, patients were given antiretroviral medications every 4-8 weeks and expected to ingest them on their own without any direct observation. Since many patients had utilized both DOT and SAT administered HAART over the long period of study conduct, the unit of evaluation was the treatment regimen rather than the individual patient. This allowed patients who received different regimens as DOT or SAT to serve as their own controls. HAART regimens were chosen on an individualized basis, taking into account previous treatment experience, with a view of designing a regimen likely to achieve maximal virologic suppression. Regimens were mainly based on either non-nucleoside reverse transcriptase inhibitors (NNRTIs) or PIs, given along with 2 other nucleoside reverse transcriptase inhibitors (NRTIs). Patients were counseled at HAART initiation to watch for signs of methadone withdrawal (if applicable) and monitored for clinical manifestations of drug toxicity on a regular basis. Treatment interruption was defined as the period when HAART was stopped for less than 6 months, followed by the resumption of the same regimen. Treatment modification was defined as a change made to one of the backbone NRTI components of antiretroviral therapy without changing the other components of HAART.  79  Baseline information (at the start of the eligible first regimen) was collected on relevant demographic, laboratory and clinical data and antiretroviral treatment history. HIV plasma viral load was measured using the Amplicor HIV-1 Monitor™ assay, version 1.5 (Roche Diagnostics, Mississauga, ON). Immunologic response was monitored using CD4 cell count, measured by flow cytometry at the local reference laboratory. HIV plasma viral load and CD4 cell counts were measured at baseline, and at approximately three-month intervals or more frequently if clinically indicated. Genotypic testing was done at baseline of each regimen then at the time of confirmed virologic failure (HIV RNA >400 copies/mL). In cases where genotypic resistance testing was not done prospectively, it was performed retrospectively on archived blood samples. Genotypic drug resistance testing was done using the VirtualPhenotypeTM Assay (VIRCO Lab, Mechelen, Belgium). Data were analyzed considering only major DRMs defined according to the International AIDS Society guidelines (IAS-USA table, 2008) (18). HIV drug resistance genotyping was attempted on plasma samples with HIV RNA levels >400 copies/mL. Samples with HIV plasma viral load <400 copies/mL were assumed to have no new resistance mutations. Regimens were excluded from the study if no blood samples were available for resistance testing (no viral load testing done in regimens taken less than 4 months or due to loss to follow-up) or if genotypes were not amplifiable. Due to the drastically different nature of regimens based upon triple nucleoside therapy, we chose to remove these regimens (n=19) from the analysis, but retained any prior or subsequent regimens these patients may have contributed to the analysis. Cumulative genotypic resistance mutations, obtained before treatment initiation, were used to evaluate resistance for each regimen at baseline. Similarly, cumulative genotypic resistance mutations, obtained during treatment period, were used to estimate the emergence of DRMs for each regimen while on treatment. DRMs were calculated as the number of new mutations (i.e. not previously detected or present at regimen baseline) over time from regimen initiation to the date of the most recent genotype or viral load test. The outcome of interest was the total number (count of reverse transcriptase and protease inhibitor mutations) of new DRMs that emerged during each regimen with DOT and SAT. In order to examine any apparent differences between regimens conducted under DOT and SAT, a univariate analysis was performed to look at factors which may have been associated with 80  being on a DOT regimen. Factors considered were age, sex, hepatitis C virus antibody status, regimen length, type of regimen (NNRTI, PI, PI/ritonavir), dosing frequency (once vs. twice/thrice daily), line of therapy (first, second, third, >third line), continuity of therapy, modifications during therapy, pre-HAART experience, year of therapy (1996-1999, 2000-2004, and 2005-2007), baseline CD4 cell count <200 cell/mm3, baseline viral load <400 copies/mL, having an amplifiable genotype sample at baseline, number of major IAS baseline DRMs (no DRMs, 1-2 DRMs, >2 DRMs) and the total number of susceptible antiretroviral drugs for the given HAART regimen (≤1, 2, 3). Differences were assessed using linear (Gaussian) regression for continuous factors and logistic regression for discrete factors. To account for the correlated nature of the regimens, where each patient may contribute several regimens to each analysis, generalized estimating equations (GEE) were used with either an exchangeable or independent working correlation structure (dependent on model fit for each case) to fit such models. The total number of DRMs and years of follow-up were tabulated for each of total, NRTI, NNRTI, and PI DRMs stratified by each of the discrete factors listed above. In the cases of NNRTI and PI DRMs, the regimens were restricted to those utilizing NNRTI- or PI-based HAART. Estimates for rates of mutations in each of the strata were computed using univariate Poisson regression models, with the number (count of) new DRMs in each regimen being the response variable. An offset term was included in all such models to adjust for the variable regimen lengths, which allows estimates derived from such models to be standardized to a unit time (as opposed to unit regimen). GEE were again used, this time with an independence working correlation structure. Standard errors (SE) were computed for each estimate on the natural log scale. Lastly, multivariable Poisson regression models were fit as described above for each of total, NRTI, NNRTI, and PI DRMs (each restricted to the subset of relevant regimens). Initial models were fit with all predictor variables listed above and subject to backwards elimination. All reported p-values were two-sided.  3.3 Results As seen in Figure 3.1, among the initial cohort of 215 IDUs followed-up at our clinic, 179 individuals initiated treatment comprising a total of 557 regimens. With the initial exclusion of NRTI-based regimens, 538 regimens were evaluated for study purposes. A total of 140 regimens 81  were additionally excluded because of short duration of treatment (N=83), loss to follow-up (N=32) and inability to perform genotypic resistance testing on isolates (N=25). Thus, the remaining 398 regimens were included in the analysis. Of these, 270 (67.8%) were taken as DOT while 128 (32.2%) as SAT. Overall, 165 patients were included in the study, with 141 contributing at least one DOT regimen, 85 contributing at least one SAT regimen and 61 having at least one of each. Sixty (36.4%) patients were females and 153 (92.7%) were HCV antibody positive. The patients were followed up for a median of 2.0 years. The baseline characteristics of the patients are shown in Table 3.1. Factors associated with being on a DOT regimen included older age (p=0.003), longer duration of treatment (p=0.033), boosted PI-based HAART (p<0.001), once-daily dosing (p<0.001), prior exposure to HAART (p<0.001), modifications during therapy (p=0.002), no pre-HAART exposure (p=0.044), treatment during later periods of the study (p<0.001), treatment initiation with a suppressed viral load (p<0.001), having an amplifiable genotype at baseline (p<0.001) and having DRMs at baseline (p=0.01) (Table 3.2). Total number of DRMs was accumulated at mean rates of 0.38 and 0.67 mutations per year during regimens using DOT and SAT, respectively (p=0.022). The mean rates of DRMs were statistically significantly less accumulating in regimens that were administered as DOT in case of NRTI (p=0.007) DRMs but not in cases of NNRTI (p=0.99) and PI (p=0.28) DRMs (Tables 3.3 and 3.4). Lower rates of total DRMs were measured in regimens which were based on boosted (p<0.001) or unboosted PIs (p=0.02), were taken as first-line HAART (p=0.038), initiated during later periods of the study (p=0.001), were modified during therapy (p<0.001) and had baseline CD4 cell count >200 cells/mm3 (p=0.011) (Tables 3.3). The mean rates of accumulation of NRTI, NNRTI and PI DRMs stratified by different covariates are shown in Table 3.4. Interestingly, having >2 DRMs at baseline was associated with less emergence of NRTI DRMs (p=0.03) but with more accumulation of NNRTI DRMs (p=0.012) and PI DRMs (p=0.015) (Table 3.4). In the final fit of multivariable Poisson regression model, factors associated with accumulation of total DRMs included: use of boosted PIs (RR=0.19; 95% CI=0.09-0.39; p<0.001), use of unboosted PIs (RR=0.36; 95% CI=0.18-0.72; p=0.004), ≥3rd line of HAART (RR=3.11; 95% CI=1.39-6.94; p=0.006), modifications during therapy (RR=0.13; 95% CI=0.04-0.40; p<0.001) 82  and baseline CD4 cell count ≤200 cells/mm3 (RR=1.94; 95% CI=1.19-3.17; p=0.008) (Table 3.5). The use of DOT was not associated with statistically significant accumulation of total DRMs (RR=0.70; 95% CI=0.41-1.23; p=0.21) or NNRTI DRMs (RR=1.16; 95% CI=0.64-2.09; p=0.63). However, there was a tendency for less accumulation of NRTI DRMs (RR=0.58; 95% CI=0.33-1.05; p=0.074) and PI DRMs (RR=0.45; 95% CI=0.19-1.06; p=0.068) with the use of the DOT strategy (Table 3.5). Other factors associated with accumulation of NRTI, NNRTI and PI DRMs are shown in Table 3.5.  3.4 Discussion Some physicians fear that prescribing HAART to IDUs may result in higher rates of resistance because of lower rates of adherence to HAART. Some studies have shown resistance rates to be similar in patients with versus without a history of injection drug use (19). However, very few studies have evaluated the impact of DOT on the emergence of DRMs within real-life settings, especially in key target populations such as IDUs. The results of our study with longitudinal resistance data show that DOT may not lead to an increase in DRMs. The relationship between the development of drug resistance and adherence is postulated to be a bell-shaped or a concave relationship with rates of maximal accumulation of DRMs occurring at moderate levels of adherence ranging from 80-90% for PIs and at broader ranges for NNRTIs (11-14). Thus, some authors have raised concerns about the increased risk of development of drug resistance mutations with DOT since such an intervention may shift the adherence distribution towards higher levels placing virologically non-suppressed patients in an intermediate adherence window that is maximal for the selection of HIV drug resistance (20, 21). In our study, DOT did not seem to prevent the development of DRMs; however, it did not lead to higher levels of resistance in HIV-infected IDUs receiving HAART as predicted by various mathematical models (12, 15), even though most of the DOT regimens were not taken as firstline HAART. Despite the fact that we looked at regimens as the unit of analysis as opposed to patients (as in other DOT studies), the results of our study support the findings of the study by Maru et al. which has shown no clinically meaningful increase in antiretroviral drug resistance with the use of DAART (16).  83  The use of PIs, mainly ritonavir-boosted, was more common with regimens based on DOT and was statistically significantly associated with fewer emergences of DRMs. In addition, PI-based regimens, taken within a DOT setting, seemed to have less tendency of developing DRMs. Adherence-resistance relations differ with the type of regimen used. However, since we did not measure adherence directly in this study, we are unable to comment directly on thresholds of adherence associated with the use of PIs or NNRTIs in our patients. Nevertheless, as reported by several other studies, boosted PI-based regimens are generally associated with lower rates of emergence of DRMs because of their higher genetic barrier and having a more forgiving profile in term of having the ability to achieve virologic suppression with less than optimal levels of adherence (22, 23). Thus, the use of a PI-based regimen would probably be a better option regarding the development of DRMs in IDUs who are suspected of being not fully adherent to HAART. Modifications during antiretroviral therapy, defined as a change made to one of the backbone NRTI components of antiretroviral therapy without changing the other components of HAART, were generally made to avoid adverse events, initiate hepatitis C treatment (use of some NRTIs contraindicated with ribavirin) and to simplify HAART regimens. Modifications during therapy were more common in regimens based on DOT, probably due to the fact that DOT patients were more closely monitored for clinical manifestations of drug toxicity, signs of methadone withdrawal or problems in adherence. In other DOT studies, patients receiving DAART were more likely to change their regimens to simplified once daily regimens (10, 16). In our study, such modifications were associated with less accumulation of DRMs. Thus, one mechanism by which DOT may reduce the development of DRMs is by having proper monitoring and better engagement of the patients in care resulting in improved clinical outcomes. There are several limitations to our study. First, treatment intervention was not assigned randomly and some data were collected retrospectively by chart review, raising the possibility of various biases. The reason why one therapy was chosen over another is likely based on important baseline factors that likely predict the accumulation of DRMs. Second, the patient population and the regimens used were heterogeneous, so that it may be that the development of resistance may vary as a function of the type of regimen (NNRTI or PI-based) and the different thresholds of adherence associated with these regimens or some other confounding factors that we were not able to measure. Third, our analysis approach assumed that there were no new mutations 84  accumulating in cases of virologic suppression. This assumption is actually not very true since resistance mutations still develop at a lower rate (11). If resistance develops in virologically suppressed patients then the rates of DRMs would be greater than our current estimates. Further, our analytical approach assumes that all DRMs (whether inside or outside a class of DRMs) will have the same impact on therapy, when clearly this is not the case. Finally, the conclusions drawn for this population of IDUs may not be generalizeable to other populations or treatment settings. In conclusion, this longitudinal study of HIV drug resistance in IDUs show that regimens taken within a DOT setting may lead to a less rapid accumulation of DRMs, although such benefit did not reach statistical significance in our current observations. At the very least, we can clearly state that it is not associated with an enhanced rate of accumulation of such mutations. Our results provide further support for the use of DOT as a tool for the administration of HAART to difficult-to-treat populations such as IDUs. If this is done with a multidisciplinary and comprehensive program addressing all of the patient’s health care needs, the possibility of an unintended deleterious consequence (including more rapid emergence of DRMs) appears extremely small.  85  Table 3.1 Baseline patient characteristics Number of patients  165  Mean age (SD) in years  38.1 (8.6)  Males (%)  105 (63.6)  Hepatitis C antibody positive (%)  153 (92.7)  Mean CD4 cell count (SD) in cells/mm3  239 (222)  Median viral load (Q1-Q3) in copies/mL  >100 000 (40 800- >100 000)  Median follow-up period (Years, Q1-Q3)  2.0 (1.2-3.2)  NOTE. Q1 indicates first quartile; Q3 indicates third interquartile; SD indicates standard deviation  86  Table 3.2 Factors associated with being on a DOT regimen DOT N=270  SAT N=128  p-value  Age (years) Mean (SD) 40.9 (8.0) 37.6 (8.1) 0.003 Sex Male (%) 176 (65.2) 78 (60.9) 0.55 Hepatitis C Antibody positive (%) 255 (94.4) 119 (93.0) 0.63 Regimen length (days) Mean (SD) 569 (482) 467 (411) 0.033 Regimen based on NNRTIs (%) 84 (31.1) 61 (47.7) <0.001 Unboosted PIs (%) 39 (14.4) 32 (25.0) Boosted PIs (%) 147 (54.4) 35 (27.3) Dosing frequency Once-daily (%) 204 (75.6) 40 (31.2) <0.001 Twice or Thrice daily (%) 66 (24.4) 88 (68.8) Line of therapy First-line (%) 50 (18.5) 43 (33.6) <0.001 Second-line (%) 91 (33.7) 61 (47.7) Third-line (%) 117 (43.3) 16 (12.5) > Third-line (%) 12 (4.4) 8 (6.2) Continuity of treatment Continuous (%) 220 (81.5) 101 (78.9) 0.40 Modifications during therapy With changes (%) 33 (12.2) 5 (3.9) 0.012 Pre-HAART experience Pre-HAART (%) 67 (24.8) 45 (35.2) 0.044 Year group of treatment 1996-1999 (%) 10 (3.7) 54 (42.2) <0.001 2000-2004 (%) 166 (61.5) 67 (52.3) 2005-2007 (%) 94 (34.8) 7 (5.5) Baseline CD4 count (cells/mm3)** Mean (SD) 223 (209) 247(210) 0.29 ≤200 (%) 157 (59.0) 58 (54.2) 0.60 >200 (%) 109 (41.0) 49 (45.8) Baseline viral load (copies/mL) ≤400 (%) 54 (20.0) 5 (3.9) <0.001 >400 (%) 216 (80.0) 123 (96.1) Baseline genotype Amplifiable 247 (91.5) 93 (72.7) <0.001 Major IAS baseline DRMs No DRMs 172 (63.7) 95 (74.2) 0.01 1-2 DRMs 33 (12.2) 22 (17.2) >2 DRMs 65 (24.1) 11 (8.6) N of susceptible ARVs at baseline 5 (1.9) 2 (1.6) 0.95 ≤1 (%) 36 (13.3) 17 (13.3) 2 (%) 229 (84.8) 109 (85.1) ≥3 (%) NOTE. ARV indicates antiretroviral drugs; DOT indicates directly observed therapy; DRM indicates drug resistance mutations; HAART indicates highly active antiretroviral therapy; NNRTI indicates non-nucleoside reverse transcriptase inhibitor; PI indicates protease inhibitor; SAT indicates self administered therapy; SD indicates standard deviation; **Regimens missing baseline CD4 cell count values  87  Table 3.3 Mean rates of accumulation of total drug resistance mutations Factor  Total Mutations/FU (Years)  Rate (SE)**  p-value  Type of therapy DOT 115/304.0 0.38 (0.16 ) 0.022 SAT 87/130.4 0.67 (0.18) Sex Female 76/151.6 0.50 (0.21) 0.65 Male 126/282.8 0.45 (0.15) Dosing BID/TID 107/194.1 0.55 (0.17) 0.15 QD 95/240.3 0.40 (0.17) Type of regimen NNRTI 137/171.5 0.80 (0.17) Boosted PI 31/175.2 0.18 (0.30) <0.001 Unboosted PI 34/87.7 0.39 (0.27) 0.02 Line of therapy 1st line 31/105.2 0.29 (0.29) 2nd line 103/178.2 0.58 (0.18) 0.038 3rd/3rd+ line 68/151.0 0.45 (0.21) 0.24 Year Group <2000 57/113.3 0.50 (0.22) 2000-2004 139/257.3 0.54 (0.16) 0.80 >2004 6/63.8 0.09 (0.47) 0.001 Continuity Interrupted 51/119.8 0.42 (0.20) 0.65 Non-interrupted 151/314.6 0.48 (0.15) Modifications to therapy Changes made 4/82.0 0.05 (0.60) <0.001 No changes made 198/352.0 0.56 (0.12) Baseline CD4 cell count ≤200 cells/mm3 124/204.1 0.62 (0.15) 0.011 >200 cells/mm3 66/196.9 0.32 (0.21) Baseline plasma viral load ≤400 copies/mL 18/363.0 0.25 (0.41) 0.11 >400 copies/mL 184/71.4 0.51 (0.13) Hepatitis C status Negative 14/31.6 0.44 (0.53) 0.92 Positive 188/402.8 0.47 (0.12) Pre-HAART experience Pre-HAART 82/139.0 0.59 (0.20) 0.14 HAART only 120/295.4 0.41 (0.15) Baseline DRMs No DRMs 139/292.3 0.48 (0.16) 1-2 DRMs 29/61.1 0.47 (0.31) 0.99 >2 DRMs 34/81.0 0.42 (0.27) 0.70 NOTE. BID indicates twice daily dosing; DOT indicates directly observed therapy; DRM indicates drug resistance mutations; FU indicates follow-up; HAART indicates highly active antiretroviral therapy; NNRTI indicates non-nucleoside reverse transcriptase inhibitor; PI indicates protease inhibitor; QD indicates once daily dosing; SAT indicates self administered therapy; TID indicates thrice daily dosing;**Standard error for log of the rate  88  Table 3.4 Mean rates of accumulation of drug resistance mutations according to class Factor  Type of therapy DOT SAT Sex Female Male Dosing BID/TID QD Type of regimen NNRTI Boosted PI Unboosted PI Line of therapy 1st line 2nd line 3rd/3rd+ line Year Group <2000 2000-2004 >2004 Continuity Interrupted Non-interrupted Changes to therapy Changes made No changes made Baseline CD4 ≤200 cells/mm3 >200 cells/mm3 Baseline viral load ≤400 copies/mL >400 copies/mL Hepatitis C status Negative Positive Pre-HAART Pre-HAART HAART only Baseline DRMs No DRMs 1-2 DRMs >2 DRMs  NRTI Mutations/ FU (Years)  Rate (SE)**  p-value  NNRTI Rate (SE)** Mutations/FU (Years)  43/304.0 41/130.4  0.14 (0.19) 0.31 (0.22)  0.007  49/107.7 29/63.8  0.45 (0.23) 0.45 (0.21)  0.99  16/196.3 10/66.6  0.08 (0.45) 0.15 (0.35)  0.28  31/151.6 53/282.8  0.20 (0.24) 0.18 (0.17)  0.77  31/64.0 47/107.5  0.48 (0.27) 0.44 (0.22)  0.77  6/87.6 20/175.3  0.07 (0.68) 0.11 (0.35)  0.51  45/194.1 39/240.3  0.23 (0.21) 0.16 (0.19)  0.20  30/81.3 48/90.2  0.37 (0.24) 0.53 (0.22)  0.25  19/112.8 7/150.1  0.17 (0.36) 0.05 (0.64)  0.087  55/171.5 11/175.2 18/87.7  0.32 (0.20) 0.06 (0.37) 0.21 (0.28)  <0.001 0.20  78/171.5 -  0.45 (0.17) -  -  12/175.2 14/87.7  0.07 (0.55) 0.16 (0.35)  0.19 -  9/105.2 51/178.2 24/151.0  0.09 (0.38) 0.29 (0.19) 0.16 (0.27)  0.004 0.18  14/62.0 43/86.3 21/23.2  0.23 (0.39) 0.50 (0.22) 0.90 (0.23)  0.07 0.002  4/43.2 6/91.9 16/127.8  0.09 (0.61) 0.07 (0.47) 0.13 (0.45)  0.65 0.70  30/113.3 50/257.3 4/63.8  0.26 (0.26) 0.19 (0.18) 0.06 (0.61)  0.35 0.03  20/69.2 56/98.4 2/3.93  0.29 (0.26) 0.57 (0.20) 0.51 (0.81)  0.034 0.50  6/44.1 20/158.9 0/59.9  0.14 (0.47) 0.13 (0.38) 0  0.90 -  24/119.8 60/314.6  0.20 (0.27) 0.19 (0.17)  0.88  23/55.4 55/116.1  0.42 (0.23) 0.47 (0.22)  0.68  2/64.4 24/198.5  0.03 (0.69) 0.12 (0.33)  0.08  2/82.0 82/352.0  0.02 (0.70) 0.23 (0.14)  0.001  1/31.9 77/139.6  0.03 (0.94) 0.55 (0.17)  0.003  1/50.1 25/212.8  0.02 (1.02) 0.12 (0.32)  0.10  50/204.1 29/196.9  0.25 (0.18) 0.14 (0.26)  0.078  47/98.4 29/65.4  0.72 (0.23) 0.29 (0.24)  0.007  17/105.7 5/131.4  0.13 (0.43) 0.05 (0.51)  0.12  7/363.0 77/71.4  0.10 (0.41) 0.21 (0.15)  0.082  8/150.7 70/20.8  0.38 (0.58) 0.46 (0.17)  0.75  2/212.3 24/50.6  0.04 (0.67) 0.11 (0.33)  0.13  10/31.6 74/402.8  0.32 (0.59) 0.18 (0.14)  0.37  4/14.0 74/157.5  0.28 (0.37) 0.47 (0.18)  0.22  0/17.6 26/245.3  0 0.11 (0.31)  -  33/139.0 51/295.4  0.24 (0.23) 0.17 (0.17)  0.27  24/47.7 54/123.8  0.50 (0.27) 0.43 (0.21)  0.67  18/91.3 8/171.6  0.20 (0.41) 0.05 (0.41)  0.012  68/292.3 9/61.1 7/81.0  0.23 (0.18) 0.15 (0.42) 0.09 (0.41)  0.32 0.03  59/148 12/15.8 7/7.2  0.40 (0.20) 0.76 (0.31) 0.97 (0.30)  0.08 0.012  6/143.8 4/45.3 16/73.8  0.04 (0.47) 0.09 (0.62) 0.22 (0.44)  0.34 0.015  p-value  PI Rate (SE)** Mutations/FU (Years)  p-value  NOTE. BID indicates twice daily dosing; DOT indicates directly observed therapy; DRM indicates drug resistance mutations; FU indicates follow-up; HAART indicates highly active antiretroviral therapy; NNRTI indicates nonnucleoside reverse transcriptase inhibitor; NRTI indicates nucleoside reverse transcriptase inhibitor; PI indicates protease inhibitor; QD indicates once daily dosing; SAT indicates self administered therapy; TID indicates thrice daily dosing;**Standard error for log of the rate  89  Table 3.5. Factors associated with accumulation of drug resistance mutations Factor Relative Rate (RR) 95% Confidence Intervals p-value Total Mutations Use of DOT 0.70 0.41-1.23 0.21 Use of unboosted PI 0.36 0.18-0.72 0.004 Use of boosted PI 0.19 0.09-0.39 <0.001 2.03 0.98-4.20 0.056 2nd line of therapy 3/3rd+ line of therapy 3.11 1.39-6.94 0.006 Modifications to therapy 0.13 0.04-0.40 <0.001 1.94 1.19-3.17 0.008 Baseline CD4 ≤200 cells/mm3 NRTI Mutations Use of DOT 0.58 0.33-1.05 0.074 Use of boosted PI 0.19 0.07-0.49 <0.001 3.49 1.56-7.82 0.002 2nd line of therapy 3/3rd+ line of therapy 4.29 1.65-11.15 0.003 Modification to therapy 0.15 0.04-0.52 0.003 NNRTI Mutations Use of DOT 1.16 0.64-2.09 0.63 Modifications to therapy 0.10 0.01-0.76 0.026 Baseline CD4 ≤200 cells/mm3 2.07 1.17-3.64 0.012 Baseline Total Mutation 1-2 2.22 1.34-3.67 0.002 Baseline Total Mutation >2 2.16 1.25-3.72 0.006 Age (per 10 year increase) 0.57 0.39-0.83 0.003 PI Mutations Use of DOT 0.45 0.19-1.06 0.068 Use of boosted PI 0.27 0.08-0.92 0.037 Baseline Total Mutation >2 11.46 3.61-36.4 <0.001 NOTE. DOT indicates directly observed therapy; NNRTI indicates non-nucleoside reverse transcriptase inhibitor; NRTI indicates nucleoside reverse transcriptase inhibitor; PI indicates protease inhibitor  90  Figure 3.1 Flow chart of study patients and regimens  IDU cohort patients N=215 Patients naïve to HAART N=36 Patients initiated HAART N=179  Total regimens prescribed N=557  NRTI-based regimens excluded N=19 Total regimens evaluated N=538  DOT N=270  SAT N=128  Resistance test done N=235  Viral load suppressed N=163  Total regimens included N=398  Total regimens excluded N=140  DOT N=41  SAT N=99  Lost to follow-up N=32  Unable to genotype N=25  Short treatment duration N=83 NOTE. DOT indicates directly observed therapy; HAART indicates highly active antiretroviral therapy; IDU indicates injection drug user; NRTI indicates nucleoside reverse transcriptase inhibitor; SAT indicates self administered therapy  91  3.5 References 1. Mocroft A, Ledergerber B, Katlama C, Kirk O, Reiss P, d'Arminio Monforte A, Knysz B, et al. Decline in the AIDS and death rates in the EuroSIDA study: an observational study. Lancet 2003;362:22-9. 2. Harrigan PR, Hogg RS, Dong WW, Yip B, Wynhoven B, Woodward J, Brumme CJ, et al. Predictors of HIV drug-resistance mutations in a large antiretroviral-naïve cohort initiating triple antiretroviral therapy. J Infect Dis 2005;191:339-47. 3. Arnsten JH, Demas PA, Farzadegan H, Grant RW, Gourevitch MN, Chang CJ, Buono D, et al. Antiretroviral therapy adherence and viral suppression in HIV-infected drug users: comparison of self-report and electronic monitoring. Clin Infect Dis 2001;33:1417-23. 4. Lucas GM, Cheever LW, Chaisson RE, Moore RD. Detrimental effects of continued illicit drug use on the treatment of HIV-1 infection. J Acquir Immune Defic Syndr 2001;27:251-9. 5. Conway B, Prasad J, Reynolds R, Farley J, Jones M, Jutha S, Smith N, et al. Directly observed therapy for the management of HIV-infected patients in a methadone program. Clin Infect Dis 2004;38:S402-8. 6. Mitty JA, Macalino GE, Bazerman LB, Loewenthal HG, Hogan JW, MacLeod CJ, Flanigan TP. The use of community-based modified directly observed therapy for the treatment of HIVinfected persons. J Acquir Immune Defic Syndr 2005;39:545-50. 7. Lucas GM, Mullen BA, Weidle PJ, Hader S, McCaul ME, Moore RD. Directly administered antiretroviral therapy in methadone clinics is associated with improved HIV treatment outcomes, compared with outcomes among concurrent comparison groups. Clin Infect Dis 2006;42:162835. 8. Tyndall MW, McNally M, Lai C, Zhang R, Wood E, Kerr T, Montaner JG. Directly observed therapy programmes for anti-retroviral treatment amongst injection drug users in Vancouver: Access, adherence and outcomes. Int J Drug Policy 2007;18:281-7. 9. Macalino GE, Hogan JW, Mitty JA, Bazerman LB, Delong AK, Loewenthal H, Caliendo AM, et al. A randomized clinical trial of community-based directly observed therapy as an adherence intervention for HAART among substance users. AIDS 2007;21:1473-7. 10. Altice FL, Maru DS, Bruce RD, Springer SA, Friedland GH. Superiority of directly administered antiretroviral therapy over self-administered therapy among HIV-infected drug users: a prospective, randomized, controlled trial. Clin Infect Dis 2007;45:770-8.  92  11. Bangsberg DR, Charlebois ED, Grant RM, Holodniy M, Deeks SG, Perry S, Conroy KN, et al. High levels of adherence do not prevent accumulation of HIV drug resistance mutations. AIDS 2003;17:1925-32. 12. Bangsberg DR, Porco TC, Kagay C, Charlebois ED, Deeks SG, Guzman D, Clark R, et al. Modeling the HIV protease inhibitor adherence-resistance curve by use of empirically derived estimates. J Infect Dis 2004;190:162-5. 13. Sethi AK, Celentano DD, Gange SJ, Moore RD, Gallant JE. Association between adherence to antiretroviral therapy and human immunodeficiency virus drug resistance. Clin Infect Dis 2003;37:1112-8. 14. Raffa JD, Tossonian HK, Grebely J, Petkau AJ, DeVlaming S, Conway B. Intermediate highly active antiretroviral therapy adherence thresholds and empirical models for the development of drug resistance mutations. J Acquir Immune Defic Syndr 2008;47:397-9. 15. Kagay CR, Porco TC, Liechty A, Charlebois E, Clark R, Guzman D, Moss AR, et al. Modeling the impact of modified directly observed antiretroviral therapy on HIV suppression and resistance, disease progression and death. Clin Infect Dis 2004;38:S414-20. 16. Maru DS, Kozal MJ, Bruce RD, Springer SA, Altice FL. Directly administered antiretroviral therapy for HIV-infected drug users does not have an impact on antiretroviral resistance: results from a randomized controlled trial. J Acquir Immune Defic Syndr 2007;46:555-63. 17. Conway B, Grebely J, Tossonian H, Lefebvre D, DeVlaming S. A systematic approach to the treatment of HIV and hepatitis C virus infection in the inner city: a Canadian perspective. Clin Infect Dis 2005;41:S73-8. 18. Johnson VA, Brun-Vezinet F, Clotet B, Gunthard HF, Kuritzkes DR, Pillay D, Schapiro JM, et al. Update of the Drug Resistance Mutations in HIV-1. Top HIV Med. 2008;16:138-45. 19. Wood E, Hogg RS, Yip B, Dong WW, Wynhoven B, Mo T, Brumme CJ, et al. Rates of antiretroviral resistance among HIV-infected patients with and without a history of injection drug use. AIDS 2005;19:1189-95. 20. Liechty CA, Bangsberg DR. Doubts about DOT: antiretroviral therapy for resource-poor countries. AIDS 2003;17:1383-7. 21. Lucas GM, Flexner CW, Moore RD. Directly administered antiretroviral therapy in the treatment of HIV infection: benefit or burden? AIDS Patient Care STDS 2002;16:527-35. 22. Lima VD, Gill VS, Yip B, Hogg RS, Montaner JS, Harrigan PR. Increased resilience to the development of drug resistance with modern boosted protease inhibitor-based highly active antiretroviral therapy. J Infect Dis 2008;198:51-8. 93  23. Shuter J, Sarlo JA, Kanmaz TJ, Rode RA, Zingman BS. HIV-infected patients receiving lopinavir/ritonavir-based antiretroviral therapy achieve high rates of virologic suppression despite adherence rates less than 95%. J Acquir Immune Defic Syndr 2007;45:4-8.  94  CHAPTER IV Primary Drug Resistance in Antiretroviral Naïve Injection Drug Users3  4.1 Introduction Highly active antiretroviral therapy (HAART) for the treatment of HIV infection has significantly improved survival and reduced progression to AIDS (1). However, the development of resistance to antiretroviral drugs is a major obstacle that can limit the long-term efficacy of HAART (2). Resistance to antiretroviral drugs in previously untreated patients, defined as primary HIV drug resistance, is of growing importance. In recent seroconverters and treatmentnaïve patients, its prevalence has increased to 5-25% in certain population-based surveys (3-7). Among untreated subjects of unknown duration of HIV infection, the estimated prevalence of primary drug resistance is similar (8-13). However, fewer studies have assessed the prevalence of primary drug resistance mutations in injection drug users (IDUs). Data from one study demonstrated a prevalence of 24% in this risk group (14). Novel strategies, such as directly observed therapy (DOT), have been employed to administer HAART to IDUs (15-17). This has been especially successful when combined with the treatment of addiction. Co-administration of HAART with methadone has led to levels of virologic suppression similar to those reported in clinical trials. However, there is some concern that even in the setting of DOT, this population is at high risk for non-adherence and the development of secondary drug resistance. This, in turn, may lead to a dramatic rise in the transmission of drug resistance to HIV-uninfected individuals in the IDU community, as well as to individuals who may come in contact with members of this community. With this in mind, the current study was undertaken to estimate the prevalence of mutations associated with drug resistance in a cohort of antiretroviral-naive IDUs with established chronic infection.  4.2 Patients and Methods All HIV-infected IDUs attending an inner city community health centre in Vancouver, Canada from June 1996 to February 2007 were identified and enrolled into this retrospective, 3  A version of this chapter has been accepted for publication. Tossonian HK, Raffa JD, Grebely J, Viljoen M, Mead A, Khara M, McLean M, Krishnamurthy A, DeVlaming S, Conway B. (2009) Primary Drug Resistance in Antiretroviral Naïve Injection Drug Users. International Journal of Infectious Diseases. In press.  95  observational study. The Pender Community Health Centre is a multi-disciplinary health care facility providing primary care, addiction services and treatment of infectious diseases predominantly to marginalized individuals in the Downtown Eastside of Vancouver, an area with a high prevalence of illicit drug use. Individuals with newly diagnosed HIV infection were eligible for baseline genotypic resistance testing. Patients were excluded from the study if no blood samples were available for resistance testing before the start of any antiretroviral treatment. Genotypic resistance testing was performed on archived blood samples. The first available plasma samples were used for resistance testing. Genotypic drug resistance testing was done using the VirtualPhenotypeTM Assay (VIRCO Lab, Mechelen, Belgium). Primary drug resistance was defined as the presence of a major mutation according to the International AIDS Society guidelines (IAS-USA table, 2007) (18). Revertants at reverse transcriptase (RT) codon 215 (A/C/D/E/G/H/I/L/N/S/V) were also included. Secondary or minor mutations, defined as mutations that have small impact on resistance, were deemed polymorphisms while isolates with no mutations were classified as wild-type virus. Phenotypic drug resistance testing was assessed using the virtual phenotype. The virtual phenotype is an estimate of the phenotype (fold-changes in IC50) for a patient's HIV genotype by matching it with other genotypes available in large databases in which they are paired with phenotypes. Results of the analysis were expressed for each drug as clinical cut-off values (CCO) with the lower (CCO1) and upper (CCO2) clinical cut-offs being the baseline foldchange associated with 20% and 80% loss of the wild type virologic response due to viral resistance, respectively. Biological cut-off values (BCO), which indicate the normal range of susceptibility of wild type HIV-strains to each drug in vitro, were used in case CCO values were not available. Thus, to predict drug activity, HIV isolates were categorized as sensitive, reduced susceptibility (CCO1) or resistant (CCO2, BCO) for each drug based on cut-offs values described by Virco (19, 20). Demographic data were collected at treatment baseline. First-line antiretroviral regimens were chosen on an individualized basis, but without taking into account the presence of any pretreatment drug resistance mutations, as genotypic test results were only available retrospectively, long after antiretroviral therapy was initiated. Since the date of HIV infection was not available 96  for the patients and since detuned assays, low-sensitivity enzyme-linked immunosorbent assay (ELISA) antibody test, were not performed to determine whether or not the patients had recent infection, all of the patients were considered to have established chronic HIV infection. Antiretroviral agents were ingested under a community pharmacist’s supervision on a daily basis within a DOT program. HIV plasma viral load was measured using the Amplicor HIV-1 Monitor™ assay, version 1.5 (Roche Diagnostics, Mississauga, ON). Virologic suppression was defined as having a plasma viral load <400 copies/mL at some point during treatment regardless of the follow-up period as long as the patient had a viral load measurement after initiating firstline antiretroviral treatment. Immunologic response was monitored using the CD4 cell count, measured by flow cytometry at the local reference laboratory. HIV plasma viral load and CD4 cell counts were measured at baseline, and at approximately three month intervals or more frequently if clinically indicated. Calculation of confidence intervals for the percentage of patients with primary drug resistance was done using a Wilson score interval. In patients placed on first-line antiretroviral therapy, the rates of virologic suppression following treatment initiation were compared in patients with or without primary resistance mutations. Tests for independence between two discrete variables were done using the χ2 or Fisher’s exact test, as appropriate. All reported p-values were twosided, and p-values below a significance level of 0.05 were considered statistically significant.  4.3 Results Among the 174 IDUs screened, a total of 128 were included in the analysis of whom 48 (37.5%) were female. Forty-six patients (18 female; mean age 45.6) were excluded from the study because there were no stored blood samples available for retrospective resistance testing. Most patients were co-infected with hepatitis C virus (93.8% carrying anti-HCV antibodies). The number of IDUs enrolled in the study per year was as follows: 14, 11, 10, 17, 12, 15, 4, 11, 9, 17, 4 and 4 patients from 1996 to 2007, respectively. The mean age at the time of resistance testing was 42.8 years. At diagnosis, median CD4 cell count and plasma viral load were 300 cells/mm3 and 71 150 copies/mL, respectively. The median time from HIV diagnosis to drug resistance testing was 86.5 days. All patients carried subtype B virus. Among the 128 patients, 101 were then placed on first-line therapy, with 3 receiving solely nucleoside reverse transcriptase inhibitors (NRTIs), 50 non-nucleoside reverse 97  transcriptase inhibitor (NNRTI)-based therapy and 48 protease inhibitor (PI)-based therapy. A clinical decision to delay HAART was made in the other 27 cases. The baseline characteristics of the patients are presented in Table 4.1. Overall, 6 (4.7%, 95% Confidence Interval (CI): 2.2-9.8%) had primary genotypic drug resistance, with the following mutations in the RT gene: L100I (1), K103N (1), Y181C (1), M184V (1), Y188L (1) and T215D (1). By drug class, the estimated prevalence of mutations conferring resistance was 1.6% (95% CI: 0.4-5.5%) for NRTIs and 3.1% (95% CI: 1.2-7.8%) for NNRTIs (Table 4.2). There were no cases of multi-class resistance or major PI resistance mutations. Moreover, there was no significant temporal trend in the prevalence of mutations by year of enrolment, with 3 of the identified cases of primary resistance mutations occurring prior to the year 2000, and the remaining three occurring after the year 2000 (Table 4.3). Among the non-major mutations, there were 5 patients infected with solitary RT mutations including T69S (3) and V118I (2). Polymorphisms in the RT gene were common, occurring in 107 (83.6%) of the sequences. RT polymorphisms were present at positions K20R (13), T39A (2), K43Q (1), A98S (7), K101Q (1), K103R (2), V106I (3), V179I (9), V189I (1), H208Y (1), H221Q (1), L283I (17) and G333E (9); however, the most common polymorphisms were associated with I135G/L/M/R/T/V (58) and R211Q/K (66). Polymorphisms in the protease gene were extremely common, occurring in 115 (89.8%) of the sequences at the following positions: L10I/V (15), I13V (4), K20R (1), L33I (1), E35D (59), M36I (38), K45R (2), Q58E (2), D60E (7), L63P (67), A71T/V (24), V75I (1), V77I (37) and I93L (73). Overall, 6 (4.7%, 95% CI: 2.2-9.8%) patients had primary phenotypic drug resistance for lamivudine/emtricitabine (1), nevirapine/delavirdine/efavirenz (3) and nelfinavir (2) (Tables 4.2 and 4.3). Four of the patients who had genotypic resistance to K103N, Y181C, M184V and Y188L had phenotypic resistance while the 2 other patients with mutations L100I and T215D had phenotypes within the normal susceptible range (Table 4.3). On the other hand, 2 patients with secondary PI mutations had primary phenotypic resistance to nelfinavir (Table 4.3). By drug class, the estimated prevalence of mutations conferring resistance was 0.8% (95% CI: 0.14.3%) for NRTIs, 2.3% (95% CI: 0.8-6.7%) for NNRTIs and 1.6% (95% CI: 0.4-5.5%) for PIs (Table 4.2). In addition, 25 (19.5%, 95% CI: 13.6-27.2%) had reduced susceptibility to PIs for the following antiretroviral drugs: 22 (17.2%, 95% CI: 11.6-24.7%) for nelfinavir, 16 (12.5%, 98  95% CI: 7.8-19.3%) for indinavir, 13 (10.2%, 95% CI: 6.0-16.6%) for amprenavir and 2 (1.6%, 95% CI: 0.4-5.5%) for saquinavir. Among the 25 patients with reduced susceptibility to PIs, 4 had primary phenotypic resistance as well. However, in all patients with reduced susceptibility to PIs, there were no primary PI mutations detected. Following initiation of antiretroviral therapy, virologic suppression, defined as reaching a detection limit of 400 copies/mL, was achieved at some point during treatment in 67/101 (66.3%) patients. Following the initiation of therapy, suppression was first detected after a median of 90 days (corresponding with the testing schedule for HIV plasma viral load). All 6 patients who had primary genotypic drug resistance initiated treatment; however, only 3 patients had resistance mutations affecting first-line HAART (Table 4.3). In patients with primary drug resistance, virologic suppression was achieved in 4/6 (66.7%) cases with the 2 virologic failures occurring in patients with relevant NNRTI mutations (L100I, K103N). In patients without primary resistance, virologic suppression was achieved in 63/95 (66.3%, p=0.99) cases. As seen it Table 4.3, all 6 patients who had primary phenotypic drug resistance initiated treatment; however, only 1 patient had phenotypic resistance affecting the first-line HAART selected for them. In patients with primary phenotypic resistance, virologic suppression was achieved in 5/6 (83.3%) cases with one virologic failure occurring in the patient with relevant NNRTI phenotypic resistance. In patients without primary phenotypic resistance, virologic suppression was achieved in 62/95 (65.3%, p=0.66) cases. Finally, among the 25 patients having reduced susceptibility to PIs, 20 initiated antiretroviral therapy (1 based on NRTIs, 7 on NNRTIs and 12 on PIs) with similar rates of virologic suppression achieved in patients with and without reduced phenotypic susceptibility to PIs.  4.4 Discussion In this study, the prevalence of primary genotypic drug resistance was 4.7% in chronically infected treatment-naïve IDUs. Mutations conferring resistance to NNRTIs (3.1%) and NRTIs (1.6%) were seen; however, there were no cases of major PI resistance mutations or multi-class resistance. Similarly, the prevalence of phenotypic drug resistance was 4.7%, although the resistance profile using the virtual phenotype analysis included resistance to NRTIs (0.8%), NNRTIs (2.3%) and PIs (1.6%). In addition, 25 (19.5%) patients had reduced susceptibility to PIs. 99  On the other hand, very high frequencies of polymorphisms in the RT (83.6%) and the protease genes (89.9%) were detected. Although these have only a small impact on resistance, they may play a role in the viral fitness or the evolution of resistance once drug pressure is applied. Alternatively, they may simply be “signature” mutations for the strains being transmitted within this IDU community, as certain specific patterns (such as genetic changes at codon 135 in the RT gene) were observed with a much higher frequency than previously reported in untreated patient populations (21). The lack of standard criteria for the definition of resistance makes it hard to compare results from various studies. In addition, differences in prevalence of primary resistance may be related to varying distribution of subtypes, transmission route and study design. In our evaluation, the resistance mutations considered were based on the IAS-USA guidelines (2007 revision) (18) without considering solitary mutations such as the V118I and T69S. With the inclusion of these mutations in the analysis, the prevalence of primary drug resistance becomes 8.6%, which is consistent with HIV primary resistance rates reported in non-IDU populations (10, 12, 22, 23). This being said, the results of our study support the findings of an earlier study that has shown an extremely low frequency of transmission of drug-resistant HIV strains among recent IDU seroconverters (24). Notably, there was some level of discordance between our genotypic and phenotypic results. Two patients who had resistant genotypes (T215D and L100I) had phenotypes within the normal susceptible range. The mutation T215D is a revertant mutation that is phenotypically not interpreted as resistant, while in the case of L100I, the BCO value was below the threshold of resistance (CCO values were not available). On the other hand, two patients who had resistant phenotypes did not have major genotypic mutations; however, they were resistant to nelfinavir because they had multiple minor PI mutations. As reported in literature, discordance between genotypic and phenotypic results is common and is probably due to having different algorithms and interpretations of test results (25, 26). Some authors have suggested that IDUs are more likely to carry HIV isolates with primary drug resistance (27). Our results do not support this, with a prevalence below 5%, corresponding to the lower end of rates reported even in non-IDU populations. This might seem quite surprising 100  since non-optimal management and care of IDUs living with HIV, poor adherence to treatment, relapse to illicit drug use and risky drug injecting as well as sexual behaviors might lead to higher levels of resistance being transmitted within this population. However, this is probably mitigated by limited prior exposure of IDUs to antiretroviral medications associated with problems of access to care and less likelihood of prescribing HAART to IDUs because of the very issues outlined above (28, 29). Interestingly, there was no increasing trend in the prevalence of resistance mutations by year of enrollment. While a stable prevalence of primary drug resistance was detected in some studies (30, 31), others showed an increase in prevalence of resistance (5, 11, 22, 32). The stability of prevalence of HIV primary drug resistance in IDUs may be related to the adoption of prevention programs as well as structured treatment programs such as DOT directed at this population as HAART was being considered on a more widespread basis. A limitation of the study is that it was done in patients with chronic rather than acute infection, thereby possibly underestimating the true prevalence of primary resistance. However, several studies have demonstrated the persistence of primary resistance mutations for periods of up to five years following the date of infection (especially for NNRTIs) (33-35), supporting the rationale for testing the chronically infected individuals and relying on such data as a representation of at least the true risk of transmission of NNRTI primary resistance. Second, some studies have shown that drug-resistant low level minority species, not detected by standard genotypic resistance testing, may be transmitted and yet may impact the success of antiretroviral therapy (36). This remains a possibility, but the overall virologic success of HAART observed in our population does not suggest that this was a major factor in our study group. Third, when comparing rates of virologic suppression among patients with and without primary drug resistance, some caution is warranted since our results are based on very few patients with drug resistance. Fourth, although this study did not include data for the actual phenotype, several studies have demonstrated an excellent correlation between interpretations derived from the virtual and the actual phenotype, suggesting that the virtual phenotype is a viable alternative for the actual phenotype (19, 20). Fifth, the results of our study represent only one clinic and may not be completely transferable to other IDU populations. Finally, in IDU populations, confounding factors may be substantial. For example, a wild-type virus cluster spreading rapidly may be heavily overrepresented and may therefore strongly influence prevalence. In our study, a 101  phylogenetic analysis to assess this question was not done. However, the long recruitment time and the diversity of the population argue against this being a significant issue in our results. In conclusion, the prevalence of primary drug resistance in our population of IDUs is relatively low (<5%). The standard of care in developed countries now mandates the use of resistance testing prior to the prescription of initial HAART regimens (37). However, if one were to need to initiate HAART in one of our IDU patients on an urgent basis, our data suggest that the use of a PI-based regimen would probably be most effective. We will continue to monitor this parameter over time to establish whether any change in this prevalence is occurring and whether it should lead to specific guidelines for the initiation of HAART in this population, especially when individual drug resistance test results are not readily available.  102  Table 4.1 Baseline patient characteristics Number of Patients  128  Sex Female (%)  48 (37.5)  Male (%)  80 (62.5)  Age in years Mean (SD)  42.8 (8.9)  Hepatitis C status Positive (%)  120 (93.8)  Median plasma viral load (copies/mL) at HIV diagnosis All patients (Q1-Q3)  71 150 (17 525->100 000)  With primary resistance (Q1-Q3)  64 250 (22 600-89 400)  Without primary resistance (Q1-Q3)  71 150 (17 600->100 000)  3  Median CD4 cell count (cells/mm ) at HIV diagnosis All patients (Q1-Q3)  300 (180-480)  With primary resistance (Q1-Q3)  425 (213-518)  Without primary resistance (Q1-Q3)  300 (180-460)  Median plasma viral load (copies/mL) at treatment baseline All patients (Q1-Q3)  90 300 (43 800->100 000)  With primary resistance (Q1-Q3)  56 300 (18 850-85 420)  Without primary resistance (Q1-Q3)  95 300 (47 700->100 000)  3  Median CD4 cell count (cells/mm ) at treatment baseline All patients (Q1-Q3)  195 (117-358)  With primary resistance (Q1-Q3)  220 (163-285)  Without primary resistance (Q1-Q3)  195 (112-362)  Duration of HIV diagnosis (months) Median (Q1-Q3) Initiated antiretroviral therapy  2.9 (0.7-25.0) 101  NRTI-based  3  NNRTI-based  50  PI-based  48  NOTE. NNRTI indicates non-nucleoside reverse transcriptase inhibitor; NRTI indicates nucleoside reverse transcriptase inhibitor; PI indicates protease inhibitor; Q1 indicates first quartile; Q3 third interquartile; SD indicates standard deviation  103  Table 4.2 Prevalence of primary genotypic and phenotypic drug resistance Genotype  Number  Percentage  95% Confidence Intervals  Mutations  (N=128) All Mutations  6  4.7  2.2-9.8  See below  NRTI Mutations  2  1.6  0.4-5.5  M184V, T215D  NNRTI Mutations  4  3.1  1.2-7.8  L100I, K103N, Y181C, Y188L  Number  Percentage  95% Confidence Intervals  Phenotype  Resistance to Antiretrovirals  (N=128) All Resistance  6  4.7  2.2-9.8  See below  NRTI Resistance  1  0.8  0.1-4.3  Lamivudine/Emtricitabine  NNRTI Resistance  3  2.3  0.8-6.7  Nevirapine/Delavirdine/Efavirenz  PI Resistance  2  1.6  0.4-5.5  Nelfinavir  NOTE. NNRTI indicates non-nucleoside reverse transcriptase inhibitor; NRTI indicates nucleoside reverse transcriptase inhibitor; PI indicates protease inhibitor  104  Table 4.3 Patients with primary genotypic and phenotypic resistance N  Test Date  Genotypic Resistance RT  Protease  Phenotypic  Reduced  HAART  BL-VL  BL-CD4  FU-VL  FU-CD4  Resistance  Response∗∗  Regimen  copies/mL  cells/mm3  copies/mL  cells/mm3  1  06/1997  Y188L, R211K, G333E  A71V, V77I  NNRTIs  IDV, NFV  3TC/D4T/IDV  11 000  290  <50  980  2  11/1997  A98S, I135M, R211K, T215D  I93L  NO  NO  3TC/D4T /DLV  8920  270  <50  540  3  10/1998  I135R, M184V  L63P, V77I, I93L  3TC, FTC  NO  DDI/D4T/NFV  >100 000  730  338  720  4  07/1999  I135T, L283I  L10I, L63P, A71V, V77I, I93L  NFV  IDV, SQV, APV  3TC/DDI/SQV/ RTV  81 500  170  146  350  5  05/2001  L100I, I135T  E35D, L63P, I93L  NO  NO  3TC/D4T/EFV  90 500  50  87 200  180  6  09/2001  V179I, R211K  L10I, E35D, Q58E, D60E, L63P, A71V, I93L  NFV  IDV, SQV, APV  3TC/DDI/NVP  86 900  270  <50  370  7  08/2002  K103N, R211K  M36I, I93L  NNRTIs  NO  3TC/DDI/NVP  42 200  160  17 600  80  8  02/2003  Y181C, R211K  E35D, L63P, A71V, V77I, I93L  NNRTIs  IDV, NFV  3TC/D4T/LPV/ RTV  70 200  170  155  260  NOTE. ∗∗ indicates resistance > 20% but < 80% of the wild type virologic response; 3TC indicates lamivudine; BL indicates baseline; APV indicates amprenavir; D4T indicates stavudine; DDI indicates didanosine; DLV indicates delavirdine; EFV indicates efavirenz; FTC indicates emtricitabine; FU indicates follow-up; IDV indicates indinavir; LPV indicates lopinavir; NNRTI indicates non-nucleoside reverse transcriptase inhibitor; NFV indicates nelfinavir; NVP indicates nevirapine; RT indicates reverse transcriptase; RTV indicates ritonavir; SQV indicates saquinavir  105  4.5 References 1. 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Accessed December 1, 2007.  109  CHAPTER V  Clinical Implications of Mutations at Reverse Transcriptase Codon 135 on Response to NNRTI-Based Therapy4  5.1 Introduction The use of highly active antiretroviral therapy (HAART) has proven remarkably effective in controlling the progression of HIV/AIDS and prolonging survival, but these benefits can be compromised by the development of drug resistance. Recent estimates of the prevalence of drug resistance during the first years of widespread use of potent antiretroviral therapy have indicated that about 70% of treated adults with detectable viremia have isolates with drug resistance mutations (1, 2). Clinically significant resistance to some drugs, notably non-nucleoside reverse transcriptase inhibitors (NNRTIs), can emerge after just a brief exposure to this class of medications. A single point mutation, such as the K103N, is sufficient to cause high-level NNRTI resistance to all first-generation agents in this class (3). The majority of NNRTI resistance can be accounted for by the presence of recognized mutations in the reverse transcriptase (RT) gene. However, certain additional polymorphisms present in different settings may result in decreased drug susceptibility. In individuals with such polymorphisms who were never treated with NNRTIs, phenotypic analysis has revealed evidence of measurable decreases in drug susceptibility (4). A number of reports have suggested that mutations at RT codon 135 (I to one of T/M/V/L/R/K) may impact NNRTI susceptibility. In vitro, a 2.5-fold increase in IC50 to nevirapine or delavirdine (5) and to nevirapine or efavirenz (6) was observed in the absence of any prior drug exposure. Some databases list the I135T mutation as being associated with NNRTI resistance (7). More recently, genetic changes at codon 135 were associated with the subsequent accumulation of mutations in subjects receiving an NNRTI-containing regimen, indicating that such mutations may provide an alternate route for the development of high-grade NNRTI drug resistance (8). Other reports, however, have not identified such a relationship (9).  4  A version of this chapter has been published. Tossonian HK, Raffa JD, Grebely J, Viljoen M, Mead A, Khara M, McLean M, Krishnamurthy A, DeVlaming S, Conway B. Clinical Implications of Mutations at Reverse Transcriptase Codon 135 on Response to NNRTI-Based Therapy. The Open Virology Journal 2007;1:8-13.  110  Over the past several years we have developed a successful program for the treatment of HIVinfected injection drug users (IDUs) within a directly observed therapy (DOT) program using simplified treatment regimens (10, 11). In our IDU drug-naive population, we have detected mutations at RT codon 135 (mainly 135T) in more than 40% of cases (12). These mutations occurring at such a high frequency may have a significant impact on response rates to NNRTIs and their use in clinical practice. With this in mind, we sought to evaluate the CD4 cell counts and plasma viral load responses to NNRTI-based regimens as well as the evolution of nucleoside reverse transcriptase inhibitor (NRTI) and NNRTI resistance mutations in patients with or without mutations at codon 135 prior to the initiation of therapy.  5.2 Patients and Methods HIV-infected IDUs with pre- and post-treatment genotypic resistance testing who received NNRTIs for the first time and for more than 1 month were included in this retrospective study. HAART regimens were received through a DOT program at the Pender Community Health Centre, a multidisciplinary clinic located on the Downtown Eastside of Vancouver, Canada. HAART regimens were individualized, based on considerations of efficacy and toxicity with regimens being mainly based on nevirapine or efavirenz along with two NRTIs. CD4 cell counts and HIV plasma viral load responses to NNRTI-based therapy were compared in patients with or without mutations at RT codon 135 at baseline. HIV plasma viral load was measured using the Amplicor HIV-1 Monitor™ assay (Roche Diagnostics, Mississauga, ON). Plasma viral load was defined as below the limit of quantitation if it was <400 HIV RNA copies/mL. Immunologic response was monitored using the CD4 cell count, measured by flow cytometry at the local reference laboratory. HIV plasma viral load and CD4 cell counts were measured at baseline, and at approximately three month intervals or more frequently if clinically indicated. Efficacy was evaluated based on the most recent HIV plasma viral load and CD4 cell count measurements or the last one prior to a change in therapy if applicable. Baseline data were also collected on patient demographics, antiretroviral treatment history and the presence of any pre-treatment drug resistance mutations, as defined by the International AIDS Society guidelines (IAS-USA table, October 2006) (13). To evaluate the evolution of NRTI and NNRTI resistance mutations in the setting of virologic breakthrough, genotypic drug resistance testing was done using the VirtualPhenotypeTM Assay, 111  VIRCO Lab, Mechelen, Belgium. Genotypic resistance tests were performed at baseline and at the time of each confirmed virologic breakthrough (viral load >400 copies/mL). In cases where genotypic testing was not done previously, frozen plasma samples were available for retrospective testing. The rates of accumulation of individual and multiple NRTI and NNRTI resistance mutations were calculated in patients failing NNRTI-based therapy. In addition, the rates of acquisition of NRTI and NNRTI resistance mutations were compared in patients with and without mutation 135 at baseline. Tests for independence between two discrete variables were done using the χ2 or Fisher’s exact test, as appropriate. Continuous variables were assessed using either Student’s t-test (age) or the Mann-Whitney test (absolute and change from baseline in CD4 cell count). All reported p-values were two-sided, and p-values below a significance level of 0.05 were considered statistically significant.  5.3 Results The study included a total of 68 patients identified from a retrospectively collected database of IDUs who received NNRTIs for the first time and for more than 1 month and who had pre- and post-treatment genotypic resistance testing. At baseline, 30 patients (19 male) were identified as having a mutation at RT codon I135 [135T (17), 135V (7), 135M (3), 135R (2), 135L (1)] while 38 patients (25 male) had no mutations at codon 135. All patients carried subtype B virus. Median baseline CD4 cell counts and plasma viral loads were 190 cells/mm3, 81,350 copies/mL and 190 cells/mm3 (p=0.99), 65,800 copies/mL (p=0.44) in the two groups, respectively. Twenty-three patients received nevirapine, 6 received efavirenz and 1 received delavirdine in the first group, while 32 patients received nevirapine, 5 received efavirenz and 1 patient received delavirdine in the second group. In all cases, the agent of interest was given along with 2 NRTIs and/or protease inhibitors to constitute a HAART regimen. The NRTIs commonly used included lamivudine (n=25 and 32) and didanosine (n=24 and 32) in patients with and without baseline mutations at codon 135, respectively. Other NRTIs, including stavudine, zidovudine and tenofovir, were used as part of HAART in the remaining few patients with equal distribution in the two study groups. PIs were used in 7 regimens only, including lopinavir, sequinavir, nelfinavir and indinavir. The baseline characteristics of study patients are presented in Table 5.1.  112  At a median follow-up period of 15 months, the median changes in CD4 cell counts were +135 cells/mm3 and +90 cells/mm3 (p=0.32) while the CD4 cell counts were 300 cells/mm3 and 345 cells/mm3 (p=0.84) in patients with and without baseline mutations at codon 135, respectively (Figure 5.1). Virologic suppression (HIV RNA <400 copies/mL) was achieved in 16/30 (53%) of patients having mutations at codon 135 at baseline and 16/38 (42%) in patients without baseline mutations at codon 135 (p=0.50) (Figure 5.2). As seen in Tables 5.2, of those not suppressed with a baseline codon 135 mutation, 10/14 (71%) had NNRTI resistance mutations [K103N (6), Y181C (3), G190A (2), V108I (1)] and 4/14 (29%) had NRTI resistance [M184I/V (3), L74V (2), TAMs (2)]. In patients with no such mutation, 16/22 (73%) had NNRTI resistance [K103N (10), Y181C (9), G190A (7), V108I (2), L100I (1), V106M (1), Y188C (1)] and 12/22 (55%) had NRTI resistance [M184I/V (12), L74V (3), TAMs (1)] (p>0.05 for all comparisons). In patients who experienced a virologic breakthrough and had a baseline mutation at codon 135, 8/14 (57%) evolved a single NNRTI resistance mutation, a finding that was observed in only 4/22 (18%) patients who did not have such a baseline mutation (p=0.029). The latter group was more likely to evolve multiple mutations (12/22 [57%] cases), a finding that was much less frequently observed (2/12 [14%] cases) in the setting of a 135 mutation at baseline (p=0.033).  5.4 Discussion The virologic impact of mutations at codon 135 is not clear despite the fact that such mutations are extremely common. It is likely that mutations at RT codon 135 affect NNRTI susceptibility by virtue of their proximity, in the p51 component of the RT dimer, to the NNRTI binding site (14). The β7-β8-loop (residues 132-140) in the p51 subunit of HIV RT contributes to the formation of the base of NNRTI- binding pocket. In one study, mutations at codons 132, 135 and 138 in the 51 subunit of RT conferred high-level resistance to nevirapine and delavirdine and low level resistance to efavirenz (15). Position 135 of HIV RT is known as the anchor position of the HLA-B51 restricted cytotoxic T lymphocyte (CTL) responses (16). CTL escape mutations occur at critical sites within HLA-restricted CTL epitopes where an amino acid substitution may abrogate epitope-HLA binding, reduce T-cell receptor recognition, or generate antagonistic CTL responses (17). According to Mallal et al., a strong association was observed between HLA-B51 and the presence of mutations at codon 135 of RT. In particular, all individuals with HLA-B51 had an I to T (79%) or V (21%) mutation at position 135 suggesting that non-synonymous 113  mutations at position 135 of RT may lead to viral escape from host CTL responses (18). Other studies, however, have associated HLA-B51 with slow progression to AIDS (19). Thus, the explanation for the differences in the number of mutations observed in our study may be secondary to differential representation of MHC alleles in both study groups. We plan to undertake the appropriate evaluations in our cohort to determine whether MHC variations provide a unifying explanation for our observations. Mutations at RT codon 135 are not widely recognized as associated with more classical NNRTIresistance mutations and their clinical implications are unclear. Brown et al. found that the amino acid site 135 of RT was associated with reduced susceptibility to both nevirapine and delavirdine (5). Vavro et al. reported that a decreased susceptibility to efavirenz and nevirapine at baseline was seen in viruses with mutations at codon 135 in antiretroviral naïve patients (8). In addition, they showed that mutations at codon 135 at baseline were associated with the accumulation of NNRTI-resistance mutations (8). Harrigan et al., however, found no relationship between the presence of mutations at codon 135 and virologic response (9). In agreement with previous reports, we did not detect statistically significant differences in immunologic or virologic responses in patients on NNRTI-based HAART as a function of the baseline genotype at codon 135. Furthermore, such mutations at baseline were not associated with the development or accumulation of NNRTI mutations while on therapy in the setting of loss of virologic suppression. Nevertheless, if resistance occurred, patients who had baseline 135 mutations were more likely to evolve single NNRTI resistance mutations (8/14 vs. 4/22, p=0.029) which is in agreement with results from previous studies (8). However, patients who had baseline 135 mutations were less likely to evolve multiple NNRTI resistance mutations (2/14 versus 12/22, p=0.033) and possibly some NRTI mutations such as the M184V (3/14 versus 12/22, p=0.08). This contrasts with previous reports which suggested that the presence of baseline mutations at codon 135 may accelerate the accumulation of more classical NNRTI resistance mutations. It should be emphasized that many of these data were generated with non-subtype B strains of HIV. This may explain why, in populations infected with subtype B virus, very different results may be obtained (8, 20). Although it is difficult to attribute all the mutations we observed to NNRTI treatment, it is generally agreed that NRTIs do not affect the evolution of NNRTI-associated mutations or mutations selected by non-reverse transcriptase inhibitors. However, one recent publication 114  reports some intriguing associations between NNRTI and NRTI mutations. Cane et al. identified 24 accessory RT codon mutations, including mutations at codon 135, as significantly associated with the accumulation of thymidine analogue mutations (TAMs) (21). In our study, we did not find a statistically significant association between mutations at codon 135 and TAMs; however, most of our patients were not on HAART regimens containing stavudine or zidovudine nor did they have complex resistance patterns including TAMs. Outside of this context, we are not aware of any reports of NRTIs selecting for or associated with mutations at codon 135. In the absence of any evidence to the contrary, we must conclude that the patterns of genetic change we have observed are associated with NNRTI use. One important implication of our results relates to the possible sequencing of NNRTIs in clinical practice, with the availability of second generation agents in this class. It may be that in patients with baseline mutations at codon 135, NNRTI resistance may be associated with the development of a single additional mutation, increasing the likelihood of agents such as etravirine remaining effective. This would not be the case if multiple NNRTI mutations were to develop, a situation that may be more likely with subtype B virus wild type at the 135 codon of RT. The study has several limitations. First, it was conducted in a relatively small number of patients. Second, the study was done on IDUs enrolled in a DOT program and thus, the results might not be generalized to non-IDU populations or non-DOT settings. Third, resistance mutations considered were only those based on the IAS-USA guidelines and some other crucial or unknown mutations might not have been properly evaluated including minor mutations such as L283I. According to Brown et al., this can lead to further decrease in susceptibility to NNRTIs in the context of a pre-existing 135 mutation (5). Fourth, our study did not account for the effect of the different amino acid positions at codon 135 as some specific mutations might be more important than others. Fifth, phenotypic assays were not done, without which it is hard to know for certain whether the observed mutations had any effect on the sensitivity or resistance to NNRTIs. Historically, however, and in the bulk virtual phenotypes performed in our centre, mutations at codon 135 were associated with a 1.2-1.3-fold decrease in NNRTI susceptibility, suggesting a minimal effect on the efficacy of agents in this class as a result of this mutation alone.  115  In conclusion, our results indicate no statistically significant differences in CD4 cell counts and HIV plasma viral load responses to NNRTI-based regimens as a function of baseline 135 genotype. However, in patients with baseline mutations at codon 135 and experiencing virologic breakthrough, there was more evolution of single and less evolution of multiple NNRTI resistance mutations. This may have important implications with respect to the initial selection of patients to receive NNRTI-based therapy at baseline with a view of choosing NNRTIs using newer agents in this class in subsequent courses of therapy.  116  Table 5.1 Baseline patient characteristics With baseline 135 mutation  Without baseline 135 mutation  30  38  Male (%)  19 (63)  25 (66)  Female (%)  11 (37)  13 (34)  Mean  45.7  39.2  SD  9.4  7.1  Median  190  190  Q1 - Q3  130 - 270  110 - 270  Median  81,350  65,800  Q1 - Q3  32,700 - >100,000  26,780 - >100,000  Median  15.7  14.2  Q1 - Q3  5.0-37.2  5.4-21.6  Nevirapine (%)  23 (77)  32 (84)  Efavirenz (%)  6 (20)  5 (13)  Delavirdine (%)  1 (3)  1 (3)  RT mutations (%)  3 (10)  3 (8)  0.99  Naïve to antiretrovirals (%)  14 (47)  21 (55)  0.63  N  p-value  Gender 0.99  Age (years) 0.003  CD4 (cells/mm3) 0.99  Viral load (copies/mL) 0.44  Follow-up period (months) 0.50  Regimen based on 0.76  NOTE. Q1 indicates first quartile; Q3 third interquartile; RT indicates reverse transcriptase; SD indicates standard deviation  117  Table 5.2 Rates of drug resistance following virologic breakthrough on NNRTI-based therapy Mutations NRTIs  With baseline 135 mutation Without baseline 135 mutation  p-value  4/14 (29%)  12/22 (55%)  0.18  L74V  2 (14%)  3 (14%)  0.99  M184I/V  3 (21%)  12 (55%)  0.08  TAMs  2 (14%)  1 (5%)  0.55  1 Mutation  1 (7%)  7 (32%)  0.12  >1 Mutation  3 (21%)  5 (23%)  0.99  10/14 (71%)  16/22 (73%)  0.99  L100I  0 (0%)  1 (5%)  0.99  K103N  6 (43%)  10 (45%)  0.99  V106M  0 (0%)  1 (5%)  0.99  V108I  1 (7%)  2 (9%)  0.99  Y181C  3 (21%)  9 (41%)  0.29  Y188C  0 (0%)  1 (5%)  0.99  G190A  2 (14%)  7 (32%)  0.43  1 Mutation  8 (57%)  4 (18%)  0.029  >1 Mutation  2 (14%)  12 (55%)  0.033  4/14 (29%)  5/22 (23%)  0.71  NNRTIs  No resistance mutations  NOTE. NNRTI indicates non-nucleoside reverse transcriptase inhibitor; NRTI indicates nucleoside reverse transcriptase inhibitor; TAM indicates thymidine analogue mutation  118  Figure 5.1 Median CD4 cell counts and median increases in CD4 cell counts at the latest follow-up visit in patients with and without mutations at codon 135.  p=0.32  -200  0  200  400  3  Change from Baseline in CD4 Cell Count (cells/mm )  0  200  400  600  800  1000  3  Most Recent CD4 Cell Count (cells/mm )  1200  600  1400  p=0.84  Without 135 Mutation  With 135 Mutation  Without 135 Mutation  With 135 Mutation  119  % of Virologic Suppression  Figure 5.2. Virologic suppression at the latest follow-up visit in patient with and without mutations at codon 135.  60% 50%  16/30 16/38  53%  40% 42% 30% 20% 10% 0% With 135 Mutation  Without 135 Mutation  p=0.50  120  5.5 References 1. Cheung PK, Wynhoven B, Harrigan PR. Which HIV-1 drug resistance mutations are common in clinical practice? AIDS Rev 2004;6:107-16. 2. Richman DD, Morton SC, Wrin T, Hellmann N, Berry S, Shapiro MF, Bozzette SA. The prevalence of antiretroviral drug resistance in the United States. AIDS 2004;18:1393-1401. 3. Petropoulos CJ, Parkin NT, Limoli KL, Lie YS, Wrin T, Huang W, Tian H, et al. A novel phenotypic drug susceptibility assay for HIV type 1. J Antimicrob Chemother 2000;44:920-8. 4. Little SJ, Daar ES, D'Aquila RT, Keiser PH, Connick E, Whitcomb JM, Hellmann NS, et al. Reduced antiretroviral drug susceptibility among patients with primary HIV infection. JAMA 1999;282:1142-9. 5. Brown AJ, Precious HM, Whitcomb JM, Wong JK, Quigg M, Huang W, Daar ES, et al. Reduced susceptibility of human immunodeficiency virus type I (HIV-1) from patients with primary HIV infection to nonnucleoside reverse transcriptase inhibitors is associated with variation at novel amino acid sites. J Virol 2000;74:10269-73. 6. Vavro C, Florance A, St. Claire M. The impact of non-B subtype HIV-1 infection on antiretroviral drug susceptibility in North and South America: 2003: Proceedings of the 2nd IAS Conference on HIV Pathogenesis and Treatment; 2003 Jul 13-16; Paris, France; 2003. 7. Petropoulos CJ, Chappey C, Parkin NT. High-level resistance to HIV-1 non-nucleoside reverse transcriptase inhibitors (NNRTIs) in the absence of known resistance mutations: 2003: Proceedings of the 43rd Annual Interscience Conference on Antimicrobial Agents and Chemotherapy; 2003 Sept 14-17; Chicago, USA; 2003. 8. Vavro C, Florance A, Irlbeck D, Wine B, St Clair M. Mutations at codon 135 at baseline are associated with the accumulation of NNRTI-resistance mutations while on EFV-containing regimens: 2004: Proceedings of the 11th Conference on Retroviruses and Opportunistic Infections; 2004 Feb 8-11; San Francisco, USA; 2004. 9. Harrigan PR, Hertogs K, Verbiest W, Larder B, Yip B, Brumme ZL, Alexander C, et al. Modest decreases in NNRTI susceptibility do not influence virological outcome in patients receiving initial NNRTI-containing triple therapy. Antivir Ther 2003;8:395-402. 10. Conway B, Prasad J, Reynolds R, Farley J, Jones M, Jutha S, Smith N, et al. Directly observed therapy for the management of HIV-infected patients in a methadone program. Clin Infect Dis 2004;38:S402-8.  121  11. Conway B, Grebely J, Tossonian H, Lefebvre D, DeVlaming S. A systematic approach to the treatment of HIV and hepatitis C virus infection in the inner city: a Canadian perspective. Clin Infect Dis 2005;41:S73-8. 12. Tossonian H. Raffa J, Viljoen M, Khara M, Mead A, McLean M, Duncan F, et al. Prevalence and impact of primary resistance in drug naïve injection drug users (IDUs). Can J Infect Dis Med Microbiol 2006;17:37A. 13. Johnson VA, Brun-Vezinet F, Clotet B, Kuritzkes DR, Pillay D, Schapiro JM, Richman DD. Update of the drug resistance mutations in HIV-1: Fall 2006. Top HIV Med 2006;14:125-30. 14. Jacob-Molina A, Ding J, Nanni RG, Clark AD, Lu X, Tantillo C, Williams RL, et al. Crystal structure of human immunodecifiency virus type 1 reverse transcriptase complexed with doublestranded DNA at 3.0 A resolution shows bent DNA. Proc. Natl. Acad. Sci. USA 1993;90:63204. 15. Sluis-Cremer N, Radzio J, Camarasa MJ, Tachedjian G, Nissley D. Role of the β7-β8-loop in the 51 kDa subunit of HIV-1 reverse transcriptase in protein stability and drug resistance. Antivir Ther 2005;10:S99. 16. Korber B, Brander C, Haynes BF, Moore JP, Koup R, Walker BD, Watkind DI. HIV Molecular Immunology Database 1999. Theoretical Biology and Biophysics, Los Alamos, New Mexico, 1999. 17. McMichael A, Rowland-Jones S. Cellular immune responses to HIV. Nature 2001;410:9807. 18. Mallal S, Moore C, John M, James I, Nolan D, Sayer D, Witt C. Characteristic changes in HIV reverse transcriptase sequence at sites encoding known CTL epitopes at a population level: 2000: Proceedings of the 13th International AIDS Conference; 2000 Jul 9-14; Durban, South Africa; 2000. 19. Tomiyama H, Sakaguchi T, Miwa K, Oka S, Iwamoto A, Kaneko Y, Takiquchi M. Identification of multiple HIV-1 CTL epitopes presented by HLA-B5101 molecules. Hum Immunol 1999;60:177-86. 20. Florence A, Vavro C, St Clair M, Irlbeck D. Genotypic associations with non-nucleoside reverse trancriptase inhibitor susceptibility in circulating recombinant forms of HIV-1 strains in North and South America. Antivir Ther 2003;8:S121. 21. Cane PA, Green H, Fearnhill E, Dunn D. Identification of accessory mutations associated with high-level resistance in HIV-1 reverse transcriptase. AIDS 2007;21:447-55.  122  CHAPTER VI Hepatotoxicity in Injection Drug Users Receiving Nevirapine-Based HAART5  6.1 Introduction The treatment of HIV infection with highly active antiretroviral therapy (HAART) has significantly improved survival and reduced progression to AIDS (1). However, treatment with many antiretroviral drugs is associated with a number of potentially serious adverse events (2, 3) that may require the discontinuation of therapy. Several studies have indicated significant elevations of liver enzymes in 5% to 30% of HIV-positive patients treated with HAART, mainly during the first year of treatment (4, 5). Although all classes of antiretroviral drugs currently available have been associated with hepatotoxicity (4-8), the most serious clinical events have been associated with the use of non-nucleoside reverse transcriptase inhibitors (NNRTIs), particularly nevirapine (NVP) (9-12). The long-term medical treatment of HIV-infected injection drug users (IDUs) presents multiple challenges, including concerns regarding adherence to therapy and access to care. These problems are further compounded by the increased potential for HAART-related adverse events. Co-infection with hepatitis B virus (HBV) or hepatitis C virus (HCV) is very common in these patients and may increase the risk of drug-associated hepatotoxicity (4, 13). Several studies have found that HCV co-infected patients are at increased risk to develop severe hepatotoxicity following the initiation of NNRTI-containing HAART such as NVP (9-11), raising concerns about the use of such agents in this population. As the prevalence of HCV infection among IDUs reaches up to 95% (14, 15), there is an urgent need to assess this concern quantitatively in an objective manner. With this in mind, we undertook this study to evaluate the incidence and correlates of hepatotoxicity in a cohort of HIV-infected IDUs receiving NVP-based HAART and to compare the findings to those measured in non-IDUs receiving similar therapy.  5  A version of this chapter will be submitted for publication. Tossonian HK, Raffa JD, Grebely J, Rashidi B, Hofmann C, Mistry A, Winther A, DeVlaming S, Conway B. Hepatotoxicity in Injection Drug Users Receiving Nevirapine-Based HAART.  123  6.2 Patients and Methods Clinical data were extracted from a large database that included information on patient demographics, antiretroviral therapy and laboratory data for over 500 HIV-infected patients in Vancouver, Canada. In a retrospective manner, we searched the database for all patients treated with NVP between 1998 and 2005. The population was divided according to the risk factor for HIV-infection (IDUs versus non-IDUs). IDUs received medications within a directly observed therapy (DOT) program at the Pender Community Health Center, a multidisciplinary clinic located in the Downtown Eastside of Vancouver. Methadone and antiretroviral agents were ingested under a community pharmacist’s supervision on a daily basis under DOT. On the other hand, non-IDUs received medications dispensed monthly through a tertiary health care clinic. Patients were eligible for inclusion in the study if they were receiving NVP (Viramune®, 400 mg/day) for the first time along with nucleoside reverse transcriptase inhibitors (NRTIs) and/or protease inhibitors (PIs). HAART regimens were chosen on an individualized basis, taking into account previous treatment experience, with a view of designing a regimen likely to achieve maximal virologic suppression. Patients were counseled at HAART initiation and were followed-up regularly to watch for signs of methadone withdrawal (in case of IDUs) and other adverse events. Clinical and laboratory manifestations of hepatotoxicity were monitored monthly and reported to the physician as appropriate. Demographic and clinical information, as well as laboratory tests (alanine aminotransferase (ALT), aspartate aminotransferase (AST), HIV plasma viral load, CD4 cell counts and HCV serologies) were all recorded at baseline. As a standard of care, HIV plasma viral loads and CD4 cell counts were performed every 3 months or as clinically indicated. HIV plasma viral load was measured using the Amplicor HIV-1 Monitor™ assay (Roche Diagnostics, Mississauga, ON). Immunologic response was monitored using the CD4 cell count, measured by flow cytometry at the local reference laboratory. ALT and AST levels were evaluated at baseline then at months 1, 3, 6, 9 and 12. Hepatotoxicity was classified based on changes relative to the upper limit of normal (ULN) as follows: grade 0 (<1.25 x ULN); grade 1 (1.25-2.5 x ULN); grade 2 (>2.5-5.0 x ULN); grade 3 (>5.0-10.0 x ULN) and grade 4 (> 10.0 x ULN). At the reference laboratory where our study patients were evaluated, the ULN for ALT was 55 IU/L and for AST 45 IU/L. Hepatotoxicity was defined as a single grade 3 or grade 4 elevation in ALT and/or AST levels.  124  Baseline characteristics were compared using χ2 test or Fisher’s exact test, as appropriate. Univariate and multivariable logistic regression models were retrospectively used to assess possible prognostic factors associated with hepatotoxicity during the first year of treatment. Variables considered in the multivariable analysis included age, gender, IDU status, HCV coinfection, baseline ALT, baseline AST, baseline CD4 cell count, baseline HIV plasma viral load, antiretroviral treatment experience and concomitant use of PIs. All reported p-values were twosided, and p-values below a significance level of 0.05 were considered statistically significant.  6.3 Results A total of 162 patients (80 IDUs, 54 males and 82 non-IDUs, 76 males) were included in the study. Among the IDUs, 75 (93.8%) were co-infected with HCV with the majority having ongoing cocaine use, while in non-IDUs, 25 (30.5%) were co-infected with HCV (p<0.001). The median duration that patients were on a NVP-containing regimen was 13.2 and 24.4 months in IDUs and non-IDUs, respectively (p=0.013). The median baseline ALT levels were similar in IDUs (39 IU/L) and non-IDUs (38 IU/L) (p=0.32); however, AST levels were higher in IDUs (44 IU/L) compared to non-IDUs (31 IU/L) (p=0.001). In addition, 26 (32.5%) and 36 (45%) IDUs had baseline ALT and AST levels above the ULN, while 20 (24.3%) (p=0.33) and 20 (24.3%) (p=0.01) non-IDUs had such levels. The median baseline HIV plasma viral loads were 64,750 copies/mL in IDUs and 26,650 copies/mL in non-IDUs, while median baseline CD4 cell counts were 190 cells/mm3 in IDUs and 290 cells/mm3 in non-IDU patients. The baseline characteristics of patients included in each treatment group are summarized in Table 6.1. The majority of patients were given NVP along with 2 NRTIs to constitute a HAART regimen; however, in some cases PIs were also prescribed (4 IDUs and 15 non-IDUs, p=0.013), likely associated with the fact that more non-IDUs were not naïve to HAART (64 vs. 56, p=0.008). Among the 80 IDUs receiving NVP, 58 received NVP in combination with lamivudine (3TC) and didanosine (DDI), 12 in combination with 3TC and stavudine (D4T), 5 in combination with D4T and DDI and the remaining 5 with other combinations. Among the 82 non-IDUs receiving NVP, 20 received NVP in combination with 3TC and DDI, 20 in combination with 3TC and D4T, 9 in combination with D4T and DDI and the remaining 33 with other combinations. During the follow-up period, median ALT levels were comparable between IDUs and non-IDUs. At month 12, median ALT levels were 47 IU/L in IDUs and 35 IU/L in non-IDUs (p=0.24). However, median AST levels were statistically significantly higher in IDUs as compared to non125  IDUs (53 vs. 33 IU/L, p<0.001). Median ALT and AST levels throughout the first 12 months of treatment are shown in Figures 6.1. As shown in Figures 6.2 and 6.3, no statistically significant differences were observed in grade 3 and 4 hepatotoxic events between IDUs and non-IDUs, although events occurred at different times following the initiation of therapy. Interestingly, the peak incidence of elevation of transaminase levels was observed after one month of therapy in non-IDUs but was delayed until the third month in IDUs. In addition, the elevations of ALT and AST were not restricted to the first 3 months of therapy. In non-IDUs, transaminase elevations were observed up to month 6 of therapy, while in IDUs such elevations were first detected up to month 9. Overall, grade 3 and 4 ALT elevations were observed in 11% and 13%, AST levels in 13% and 9%, ALT and AST levels in 9% and 7% and ALT or AST levels in 15% and 15% in IDUs and non-IDUs, respectively. None of these differences were statistically significant. In IDUs, NVP was discontinued in 38 (48%) cases with the main reasons being non-adherence to HAART (20%), hepatotoxicity with or without clinical symptoms (9%) and drug interactions with methadone (9%). In non-IDUs, NVP was discontinued in 31 (38%) patients with the main causes of discontinuation being adverse events (12%), virologic failure (10%) and hepatotoxicity with or without clinical symptoms (9%). There were no cases of fulminant hepatitis or death reported during the treatment period. Hepatotoxicity was resolved after discontinuation of antiretroviral therapy in all patients (Table 6.2). The results of multivariable analysis of risk factors associated with grade 3 and 4 hepatotoxicity are shown in Tables 6.3 and 6.4. After adjustments in the multivariable model, the following factors were found to be associated with hepatotoxicity: HCV infection (OR: 8.11; 95% CI: 1.95-33.78; p=0.004), being naïve to HAART (OR: 4.23; 95% CI: 1.38-12.94; p=0.01) and having baseline ALT > ULN (OR: 2.88; 95% CI: 1.04-7.97; p=0.04). When adjusting for these risk factors, IDUs were less likely to achieve grade 3 or 4 hepatotoxicity (OR: 0.16; 95% CI: 0.04-0.60; p=0.006). Age, gender, concomitant use of PIs, baseline AST > ULN, baseline viral load and baseline CD4 cell count (>250 in females and >400 in males) were not associated with a higher risk of liver toxicity.  126  6.4 Discussion Based on data generated in clinical trials and observational cohorts, hepatotoxicity is a known cause of treatment interruption among HIV-infected patients receiving NVP-based HAART in up to 18% of cases (16, 17). This may be especially true for IDUs, who are almost universally co-infected with HCV. In this study, although severe hepatotoxicity did occur in our cohort patients, the majority of patients did not develop severe hepatotoxicity following the initiation of NVP-based antiretroviral therapy. The incidence of hepatotoxicity associated with NVP use in our cohort of IDUs was similar to that reported in non-IDUs during the first year of therapy. Overall, ≥ grade 3 increases in ALT or AST were observed in 15% of participants in both IDUs and non-IDUs, with treatment discontinued due to hepatotoxicity in 9% of both groups. Over 12 months of observation, transaminase levels were generally higher in IDUs and this was especially true for AST levels. As expected, this may be the case because of co-infection with HCV. IDUs with chronic HCV infection frequently experience an increase in transaminase levels after beginning antiretroviral treatment. Interestingly, the peak incidence of elevation of ALT and AST levels was observed after one month of therapy in non-IDUs, but was delayed until the third month in IDUs. This suggests that there is a separate pathogenesis for our observations in the two groups. In IDUs, this may be driven by HCV co-infection, and may represent some form of immune reconstitution or HCV re-activation that would resolve on its own even if NVP therapy were continued. In non-IDUs, it may be more attributable to NVP itself, as this group included patients with higher CD4 cell counts, a phenomenon that is more common in this population than among IDUs. In addition, transaminase elevations were observed until month 6 in non-IDUs and month 9 in IDUs. Several reports have described late onset NVP hepatotoxicity typically occurring after 4-5 months of therapy (9, 18). This may be due to NRTI toxicity, high cumulative dose of the drug and chronic hepatitis infection. Viral hepatitis has been shown to be a major independent risk factor for the development of hepatotoxicity in patients receiving HAART (9-11, 13). In addition, elevated baseline ALT levels have also been found to be an independent risk factor for hepatotoxicity in a number of studies (4, 9, 13). In our study, the risk of severe hepatotoxicity was eightfold higher among patients co-infected with HCV, four-fold higher in patients naïve to antiretroviral therapy and three-fold higher in patients having elevated ALT levels at baseline. Application of these criteria  127  can allow us to define a population of IDUs and non-IDUs in whom nevirapine-based therapy can be safely prescribed. Various studies have found that women who had a CD4 cell count >250 cells/mm3 and men who had a CD4 cell count >400 cells/mm3 are considered to be at greater risk for hepatotoxicity which has led to warning against use of NVP in such circumstances (19-21). On the other hand, other studies have found no increased hepatotoxicity with regard to sex and CD4 cell counts (22). In our study, hepatotoxicity associated with higher CD4 cell counts in women having baseline CD4 cell count >250 cells/mm3 and in men having baseline CD4 cell counts >400 cells/mm3 was not observed, at least within the limits of the statistical power of the analysis. The study has several limitations. Because of its retrospective nature, a selection bias may have occurred, with NVP-based therapy being systematically avoided in those at highest risk of hepatotoxicity. As such, groups were not homogenous regarding baseline gender, follow-up period, transaminase levels, CD4 cell counts, and previous exposure to antiretrovirals and concomitant use of PIs. However, the multivariable analysis overcame to a certain extent the biases caused by the unbalanced groups. It should be pointed out, however, that a number of factors that may be associated with hepatotoxicity (HBV co-infection, alcohol use, opportunistic infections) were not well evaluated. These may have contributed to our findings, especially if they were unbalanced between IDUs and non-IDUs. This being said, we have no real reason to suspect that this is the case. Finally, hepatotoxicity was classified based on severe elevations of ALT and AST enzyme levels only and relative to the ULN, and more subtle NVP-related effects were not evaluated in our analysis. It is reassuring that no case of hepatotoxicity associated with severe morbidity or mortality was observed, either on or off NVP. In conclusion, our results suggest that the majority of HCV co-infected IDUs will not develop hepatotoxicity following the initiation of NVP-based HAART. If they do, this will occur at a rate similar to that observed in non-IDUs. Furthermore, the mechanism of elevation of transaminase levels may be different from that previously reported in IDUs and may not preclude the continuation of NVP therapy once it occurs as it may not relate directly to the drug itself. Therefore, NVP should still be considered a credible therapeutic option in this population, with careful monitoring of liver function tests during the first year of therapy.  128  Table 6.1 Baseline patient characteristics Factor  IDUs  Non-IDUs  p-value  80  82  -  Male (%)  54 (67.5)  76 (92.7)  <0.001  Mean age in years (SD)  43.8 (8.9)  46.5 (9.1)  0.053  HCV-antibody positive (%)  75 (93.8)  25 (30.5)  <0.001  Median  39  38  0.32  Q1 - Q3  28-68  24-59  -  26 (32.5)  20 (24.3)  0.33  Median  44  31  0.001  Q1 - Q3  31-66  25-46  -  36 (45.0)  20 (24.3)  0.01  Median  190  290  0.06  Q1 - Q3  105-310  120-530  -  Median  64,750  26,650  0.002  Q1 - Q3  18,720- >100,000  <50- >100,000  -  13.2 (1-90)  24.4 (1-100)  0.013  34 (42.5)  18 (22.0)  0.008  4 (5.0)  15 (18.3)  0.013  DDI/3TC/NVP  58  20  -  D4T/3TC/NVP  12  20  -  D4T/DDI/NVP  5  9  -  Other Category  5  33  -  N  ALT (U/L)  ALT > ULN (%) AST (U/L)  AST > ULN (%) 3  CD4 cell count (cells/mm )  Plasma viral load (copies/mL)  Median follow-up period (Range) Naïve to HAART (%) Protease inhibitor use (%) Frequently used regimens  NOTE. 3TC indicates lamivudine; ALT indicates alanine aminotransferase; AST indicates aspartate aminotransferase; D4T indicates stavudine; DDI indicates didanosine; HAART indicates highly active antiretroviral therapy; HCV indicates hepatitis C virus; IDU indicates injection drug user; NVP indicates nevirapine; Q1 indicates first interquartile; Q3 indicates third quartile; SD indicates standard deviation; ULN indicates upper limit of normal  129  Table 6.2 Causes and rates of treatment discontinuation at month 12 IDUs (N=80)  Non-IDUs (N=82)  38 (48%)  31 (38%)  Hepatotoxicity  7 (9%)  7 (9%)  Virologic failure  3 (4%)  8 (10%)  Adverse events  5 (6%)  10 (12%)  Non-adherence  16 (20%)  4 (5%)  Others (e.g. methadone interactions)  7 (9%)  2 (2%)  Death  0 (0%)  0 (0%)  Number of discontinuations  NOTE. IDU indicates injection drug user  130  Table 6.3 Factors associated with grade 3 and 4 hepatotoxicity in a multivariable logistic regression analysis before model selection Factor  Odds Ratio  95% Confidence Intervals  p-value  IDU (vs. non-IDU)  0.16  0.04-0.68  0.012  Baseline ALT > ULN  3.08  0.71-13.29  0.13  Baseline AST > ULN  0.81  0.19-3.50  0.78  Age (per year increase)  0.96  0.90-1.02  0.22  HCV-antibody positive  9.01  2.04-39.77  0.004  Naive to HAART  4.04  1.17-14.00  0.028  Sex is male  1.90  0.46-7.92  0.38  Use of protease inhibitors  0.74  0.11-4.91  0.76  0.60  0.15-2.36  0.47  Baseline CD4 cell count <200 cells/mm  0.42  0.12-1.54  0.19  Baseline viral load >1000 copies/mL  1.10  0.24-5.15  0.90  CD4 cell count/sex contraindication 3  NOTE. ALT indicates alanine aminotransferase; AST indicates aspartate aminotransferase; HAART indicates highly active antiretroviral therapy; HCV indicates hepatitis C virus; IDU indicates injection drug user; ULN indicates upper limit of normal  131  Table 6.4 Factors associated with grade 3 and 4 hepatotoxicity in a multivariable regression analysis after model selection (by backward elimination until only statistically significant factors are remained in the model) Factor  Odds Ratio  95% Confidence Intervals  p-value  IDU  0.16  0.04-0.68  0.006  Baseline ALT > ULN  2.88  1.04-7.97  0.042  HCV-antibody positive  8.11  1.95-33.78  0.004  Naive to HAART  4.23  1.38-12.94  0.011  NOTE. ALT indicates alanine aminotransferase; IDU indicates injection drug user; HAART indicates highly active antiretroviral therapy; HCV indicates hepatitis C virus; ULN indicates upper limit of normal  132  Figure 6.1 Median alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels during the  ALT U/L  first 12 months of treatment in injection drug users (IDUs) and non-IDUs  80 70 60 50 40 30 20 10 0  p=0.13  p=0.15  p=0.55  50 39 38  BL  42  40 33  38  39  M1  M3  M6  p=0.28  p=0.24  46  47  34  35  M9  M12  Time  AST U/L  IDU  80 70 60 50 40 30 20 10 0  p=0.93  p=0.0003  Non-IDU  p=0.006  52 44 31  BL  47  p=0.006  p<0.001  54  53  36 31  34  34  34  33  M1  M3  M6  M9  M12  Time IDU  Non-IDU  133  Figure 6.2 Alanine aminotransferase (ALT) and aspartate aminotransferase (AST) elevations: Grade 3 and 4 hepatotoxicity in injection drug users (IDUs) and non-IDUs  IDU ALT  IDU AST  Non-IDU ALT  14  14  9.8  10  9.4  Percentage  Percentage  Non-IDU AST  12  12  8  5.2  6  3.6  3.2  4  M1  M3  7.8  6  5.7  3.4  3.1  M6  M9  0  0  M 12  BL  M1  M3  M6  M9  M 12  Time  Time  p  3.3  2  0  0 BL  9.4  8  4  3.9  2.9  2  9  10  M1  M3  M6  M9  M12  0.42  0.27  0.99  0.37  0.99  p  M1  M3  M6  M9  M12  0.71  0.26  0.41  0.36  0.99  134  Figure 6.3 Grade 3 and 4 hepatotoxicity: Alanine aminotransferase (ALT) and/or aspartate aminotransferase (AST) elevations at any time point in injection drug users (IDUs) and non-IDUs  IDU  20%  Non-IDU  18%  15%  Percentage  16% 14% 12%  13% 11%  15%  13% 9%  10% 8%  9%  7%  6% 4% 2% 0% ALT  p=0.81  AST  p=0.45  ALT and AST  p=0.78  ALT or AST  p=0.99  135  6.5 References 1. Palella FJ, Delaney KM, Moorman AC, Loveless MO, Fuhrer J, Satten GA, Aschman DJ, et al. Declining morbidity and mortality among patients with advanced human immunodeficiency virus infection. N Eng J Med 1998;338:853-60. 2. Carr A, Cooper DA. Adverse effects of antiretroviral therapy. Lancet 2000;356:1423-30. 3. Powderly WG. Long-term exposure to lifelong therapies. J Acquir Immune Defic Syndr 2002;29:S28-40. 4. Sulkowski MS, Thomas DL, Chaisson RE, Moore RD. Hepatotoxicity associated with antiretroviral therapy in adults infected with human immunodeficiency virus and the role of hepatitis C or B virus infection. JAMA 2000;283:74-80. 5. Nunez M, Lana R, Mendoza JL, Martín-Carbonero L, Soriano V. Risk factors for severe hepatic injury after introduction of highly active antiretroviral therapy. J Acquir Immune Defic Syndr 2001;27:426-31. 6. Gisolf EH, Dreezen C, Danner SA, Weel JL, Weverling GJ; Prometheus Study Group. Risk factors for hepatotoxicity in HIV-1-infected patients receiving ritonavir and sequinavir with or without stavudine. Clin Infect Dis 2000;31:1234-9. 7. Sulkowski MS, Mehta SH, Chaisson RE, Thomas DL, Moore RD. Hepatotoxicity associated with protease inhibitor-based antiretroviral therapy regimens with or without concurrent ritonavir. AIDS 2004;18:2277-84. 8. Mira JA, Macias J, Giron-Gonzalez JA, Merino D, González-Serrano M, Jiménez-Mejías ME, Caballero-Granado FJ, et al. Incidence of and risk factors for severe hepatotoxicity of nelfinavircontaining regimens among HIV-infected patients with chronic hepatitis C. J Antimicrob Chemother 2006;58:140-6. 9. Martinez, E, Blanco JL, Arnaiz JA, Pérez-Cuevas JB, Mocroft A, Cruceta A, Marcos MA, et al. Hepatotoxicity in HIV-1-infected patients receiving nevirapine-containing antiretroviral therapy. AIDS 2001;15:1261-8. 10. Sulkowski MS, Thomas DL, Mehta SH, Chaisson RE, Moore RD. Hepatotoxicity associated with nevirapine or efavirenz-containing antiretroviral therapy: role of hepatitis C and B infections. Hepatology 2002;35:182-9. 11. Martin-Carbonero L, Nunez M, Gonzalez-Lahoz J, Soriano V. Incidence of liver injury after beginning antiretroviral therapy with efavirenz or nevirapine. HIV Clin Trials 2003;4:115-20.  136  12. Maggiolo F, Arici C, Airoldi M, Ripamonti D, Quinzan G, Gregis G, Ravasio V, et al. Reasons for discontinuation of nevirapine-containing HAART: results from an unselected population of a large clinical cohort. J Antimicrob Chemother 2007;59:569-72. 13. den Brinker M, Wit FW, Wertheim-van Dillen PM, Jurriaans S, Weel J, van Leeuwen R, Pakker NG, et al. Hepatitis B and C virus co-infection and the risk for hepatotoxicity of highly active antiretroviral therapy in HIV-1 infection. AIDS 2000;14:2895-902. 14. Roy K, Hay G, Andragetti R, Taylor A, Goldberg D, Wiessing L. Monitoring hepatitis C virus infection among injecting drug users in the European Union: a review of the literature. Epidemiol Infect 2002;129:577-85. 15. Backmund M, Reimer J, Meyer K, Gerlach JT, Zachoval R. Hepatitis C virus infection and injection drug users: prevention, risk factors and treatment. Clin Infect Dis 2005;40:S330-5. 16. Sanne I, Mommeja-Marin H, Hinkle J, Bartlett JA, Lederman MM, Maartens G, Wakeford C, et al. Severe hepatotoxicity associated with nevirapine use in HIV-infected subjects. J Infect Dis 2005;191:825-9. 17. Dieterich DT, Robinson PA, Love J, Stern JO. Drug-induced liver injury associated with the use of nonnucleoside reverse-transcriptase inhibitors. Clin Infect Dis 2004;38:S80-9. 18. Clarke S, Harrington P, Condon C, Kelleher D, Smith OP, Mulcahy F. Late onset hepatitis and prolonged deterioration in hepatic function associated with nevirapine therapy. Int J STD AIDS 2000;11:336-7. 19. Stern JO, Robinson PA, Love J, Lanes S, Imperiale MS, Mayers DL. A comprehensive hepatic safety analysis of nevirapine in different populations of HIV infected patients. J Acquir Immune Defic Syndr 2003;34:S21-33. 20. FDA advisory on nevirapine. AIDS Treat News 2005;409:7. 21. Boehringer Ingelheim Pharmaceuticals. Viramune (nevirapine) package insert. January 2004. 22. Manfredi R, Calza L. Nevirapine versus efavirenz in 742 patients: no link of liver toxicity with female sex, and a baseline CD4 cell count greater than 250 cells/µl. AIDS 2006;20:2233-6.  137  CHAPTER VII  Methadone Dosing Strategies in HIV-Infected Injection Drug Users Enrolled in a Directly Observed Therapy Program6  7.1 Introduction The treatment of HIV-1-infected injection drug users (IDUs) with highly active antiretroviral therapy (HAART) presents multiple challenges, including problems of adherence to therapy and access to care. For individuals dealing with heroin addiction, pharmacokinetic interactions of antiretroviral (ARV) agents with methadone are additional challenges to successful therapy (1, 2). Co-administration of nevirapine (NVP) or efavirenz (EFV) and methadone can result in a significant reduction in exposure to methadone, resulting in opioid withdrawal symptoms, which may threaten ongoing adherence to therapy (2-4). The pharmacokinetic interactions of methadone with lopinavir/ritonavir (LPV/r) may or may not be clinically significant (5, 6). With regard to atazanavir (ATV), little information is available pertaining to its interactions with methadone. A recent report indicates no clinically relevant pharmacokinetic interactions between unboosted ATV and methadone (7). One approach to the use of HAART in this population could be its delivery through directly observed therapy (DOT) programs. DOT has demonstrated efficacy in increasing adherence to HAART among patients enrolled in a methadone program (8) and in improving virological suppression in previously non-adherent patients (9). Several studies have demonstrated the feasibility and efficacy of DOT programs in community-based clinics (10, 11). In this study, we have evaluated changes in methadone doses necessary to address symptoms of opiate withdrawal or toxicity and clinical outcomes following the initiation of new HAART regimens based on NVP, EFV, LPV/r or ATV with the co-administration of HAART and methadone within a DOT program.  6  A version of this chapter has been published. Tossonian HK, Raffa JD, Grebely J, Trotter B, Viljoen M, Mead A, Khara M, McLean M, Duncan F, Fraser C, DeVlaming S, Conway B. Methadone Dosing Strategies in HIV-Infected Injection Drug Users Enrolled in a Directly Observed Therapy Program. Journal of Acquired Immune Deficiency Syndrome 2007;45:324-7.  138  7.2 Patients and Methods Within a prospective, observational study, we identified HIV-infected IDUs enrolled in a methadone treatment program that initiated a new HAART regimen between 2001 and 2005. All medical care for these patients was coordinated through one of two multidisciplinary clinics in Vancouver and Victoria, Canada. Methadone and ARV agents were ingested under a community pharmacist’s supervision on a daily basis under DOT. The HAART regimen was chosen on an individualized basis with a view to designing a regimen likely to achieve maximal virologic suppression. Follow-up was according to clinical standards, with changes in methadone dose being made as required to alleviate symptoms of opiate withdrawal or toxicity. The new stable dose of methadone (within 3 months of initiating HAART) was recorded. Only patients initiating regimens containing NVP, EFV, LPV/r and ATV (with or without ritonavir boosting) were included in this study. Patients were excluded if they were not on a stable dose of methadone (>1 month) at initiation of HAART, or if they did not continue the same regimen for at least 3 months following its initiation. HIV plasma viral load was measured using the Amplicor HIV-1 Monitor™ assay (Roche Diagnostics, Mississauga, ON). Immunologic response was monitored using CD4 cell counts, measured by flow cytometry at the local reference laboratory. The most recent on-treatment HIV plasma viral load and CD4 cell count were used to evaluate HAART efficacy prior to and following the initiation of the study regimen. Baseline data were also collected on relevant demographic and ARV treatment history. The change in methadone dosing associated with the initiation of HAART was calculated as the difference between the post- and the pre-HAART methadone doses. Non-parametric statistical methods (Mann-Whitney and Kruskal-Wallis tests) were used to compare differences between treatment groups for methadone dose as well as CD4 cell counts. Tests for independence between two discrete variables were done using the χ2 or Fisher’s exact test, as appropriate. A multivariable logistic regression model with requiring an increase in methadone dose as the response variable was initially fit with regimen group, sex, age, pre-HAART methadone dose, HAART experience, baseline CD4 cell counts and the logarithm of the baseline HIV plasma viral load. This model was then subjected to backwards elimination, with only statistically significant effects remaining in the final model. All reported p-values were two-sided, and pvalues below 0.05 were considered statistically significant. 139  7.3 Results The study included 120 HIV-positive patients (46 female, 74 male) identified from a prospectively collected database of IDUs, after having excluded 15 patients who were not on a stable methadone dose or were not on treatment for at least 3 months following HAART initiation. All patients were co-infected with hepatitis C virus and the majority had ongoing cocaine use (predominately injected or smoked as “crack”). At baseline, treatment groups were comparable in all respects except for treatment experience and CD4 cell count. The median baseline methadone dose was 80 mg/day with no statistically significant differences between the patient groups. Thirty-seven patients received NVP, 18 received EFV, 33 received LPV/r and 32 received ATV with 23 boosted and 9 unboosted with ritonavir. In all cases, the agent of interest was given along with 2 nucleoside reverse transcriptase inhibitors to constitute a HAART regimen. The median changes in methadone doses for each group are shown in Table 7.1 and Figure 7.1. Statistically significant differences were detected among treatment groups in both the median change in methadone dose (p<0.001) and the median percentage change in methadone dose (p<0.001). A smaller proportion of patients receiving EFV-based HAART (61%, 11/18) required increases in methadone dose as compared to those receiving NVP-based HAART (86%, 32/37, p=0.04). In addition, patients receiving EFV had median methadone increases of 13.1% (+7.5 mg/day, p=0.004) as compared to 30.8% (+20 mg/day, p<0.001) of patients receiving NVP. No patients receiving either NVP or EFV required decreases in methadone dose from baseline. However, in those receiving LPV/r and ATV-based HAART, 27% (9/33) and 22% (7/32) required increases in methadone dose (p=0.77) while 24% (8/33) and 25% (8/32) of patients required decreases in doses (p=0.99). Median changes in methadone dose were 0 mg/day in LPV/r containing regimens (p=0.56) and 0 mg/day in ATV-based HAART (p=0.95), with or without ritonavir boosting. Statistically significant differences in the median change in methadone dose were detected in pair-wise comparisons of NVP and EFV (p=0.017), NVP and ATV (p<0.001), NVP and LPV/r (p<0.001), EFV and ATV (p=0.001), and EFV and LPV/r (p=0.002). No statistically significant differences in median methadone dose change were detected between ATV and LPV/r (p=0.73). In a multiple logistic regression analysis, after model selection, only regimen group was independently associated with requiring an increase in methadone dose (p<0.001). 140  Virologic suppression (HIV RNA <400 copies/mL) was achieved in 26/37 (70%), 12/18 (67%), 25/33 (76%) and 24/32 (75%) of patients receiving NVP, EFV, LPV/r and ATV-based regimens (p=0.89) while median changes in CD4 cell counts were 90 cells/mm3, 85 cells/mm3, 120 cells/mm3 and 95 cells/mm3, respectively (p=0.70). At most recent follow-up, the CD4 cell counts were 360 cells/mm3, 330 cells/mm3, 250 cells/mm3 and 220 cells/mm3 in patients receiving NVP, EFV, LPV/r and ATV-based HAART, respectively (p=0.03), all differences being related to those already present at baseline (Table 7.2).  7.4 Discussion In this study, a median 30.8% and 13.1% increase in methadone dosage was required to maintain the therapeutic benefit of opiate substitution in patients receiving NVP- and EFV-based HAART, as compared to no median change required in patients receiving regimens containing LPV/r or ATV. While our data would seem to suggest that patients switching from NVP or EFVbased HAART to LPV/r- or ATV-based HAART should require decreases in required methadone dose, those patients initiating HAART for the first time, the outcome in patients switching from protease inhibitor (PI)-based therapy, or those previously on NVP or EFV with a significant washout period, are much more difficult to predict. Moreover, extreme variability in dose adjustments was observed and could not be effectively predicted prior to treatment. This source of variability may have been due to the potential differential effects of ARV drugs on the active (R isomer) and inactive (S isomer) forms of methadone (12).  Pharmacokinetic investigations into the effects of NNRTIs suggest that clinically relevant differences in methadone drug levels do occur in patients on such regimens. Some anecdotal clinical data exist to support these findings. In two such studies, NVP and EFV regimens were found to reduce methadone levels by 46% and 50%, respectively (13, 14). In one study, 82% of patients starting EFV required an average methadone dosage increase of 22% to maintain clinical stability (14). In another study, patients starting NVP required an average methadone dosage increase of 45% to prevent clinical withdrawal (15). In our study, we noted statistically significant differences in the median change in methadone dose between NVP and EFV (p=0.017); however, this may be simply a result of the multiple pair-wise comparisons. With respect to PIs, there were no methadone dose adjustments from baseline in patients receiving LPV/r or ATV-based HAART. Pharmacokinetic studies of LPV/r have reported significant acute decreases in methadone AUC by 36% (6), but as in other clinical investigations, the majority of 141  patients did not appear to require any methadone dose adjustments (16) suggesting that, ultimately, the inhibitory effect of ritonavir on methadone metabolism will predominate. Although we did not directly measure cocaine use in out patients, we expect that more than 85% of our IDUs were using cocaine during the study period based on previous results published by our group (11). Thus, despite ongoing cocaine use, an increase in CD4 cell count and acceptable virologic suppression was observed, suggesting an equal benefit of NVP, EFV, LPV/r and ATVbased DOT within a methadone maintenance program. A major limitation of this study was the non-blinding of the methadone-prescribing physicians, with the possibility that the knowledge of the ARV regimens might have influenced the physician’s methadone dosing choices. However, this was probably not significant in affecting our results as the decision to increase (or decrease) methadone dosing was made on clinical grounds at each mandated visit (every two weeks) to renew methadone prescriptions. Another possible weakness may be the non-random assignment of HAART regimens, with physicians selecting a specific drug combination based on their concern regarding drug interactions. This is unlikely to have had a significant impact on our results as, until quite recently, there was significant controversy regarding the effect of PI-based regimens on methadone levels, at least acutely. Finally, there may be some concern that the 15 patients who were excluded from the study would represent a group with more dramatic effects of HAART on methadone levels, thereby affecting our conclusions in a significant way. This is quite unlikely, as all of the withdrawals were linked to a functional loss to follow up rather than symptoms of methadone withdrawal or related phenomena. In conclusion, our data support other clinical studies demonstrating that NVP- and EFVmethadone interactions almost always require moderate increases in methadone dosage. In patients receiving LPV/r or ATV, our data support the conclusion that little (if any) adjustment in methadone dosing will be required in this setting, in contrast with what we may have expected based on acute pharmacokinetic studies. This suggests that the steady state interaction is the most clinically relevant one. While our study demonstrates that methadone-based DOT can be a very successful tool for the co-administration of HAART in relevant patients, careful monitoring is required to ensure that methadone withdrawal does not adversely affect the goals of treatment, particularly when NNRTI-based regimens are used. 142  Table 7.1 Methadone dose adjustments  Median follow-up methadone  Atazanavir ± ritonavir 80 (60-123)  p-value  85 (70-100)  Lopinavir + ritonavir 90 (70-135)  +20 (10-40)  +7.5 (0-20)  0 (0-5)  0 (-2.5-0)  <0.001  +30.8  +13.1  0  0  <0.001  32/37 (86)  11/18 (61)  9/33 (27)  7/32 (22)  <0.001  0/37 (0)  0/18 (0)  8/33 (24)  8/32 (25)  <0.001  5/37 (14)  7/18 (39)  16/33 (49)  17/32 (53)  0.003  Nevirapine  Efavirenz  100 (80-125)  0.42  dose (mg/day, IQR) Median change in methadone dose (mg/day, IQR) Median change in methadone dose (%) Requiring increases in methadone dose (%) Requiring decreases in methadone dose (%) Requiring no change in methadone dose (%) NOTE. IQR indicates interquartile range  143  Table 7.2 Antiretroviral efficacy at most recent follow-up p-value  250 (170-464)  Atazanavir ± ritonavir 220 (138-320)  +85 (10-130)  +120 (20-210)  +95 (48-143)  0.70  <50 (<50-  56 (<50-  <50 (<50-  <50 (<50-  0.89  load (copies/mL, IQR)  5610)  13810)  10230)  7082)  Plasma viral load <400  26/37 (70)  12/18 (67)  25/33 (76)  24/32 (75)  Nevirapine  Efavirenz  Lopinavir + ritonavir  360 (220-580)  330 (150-458)  +90 (-70-210)  Median follow-up viral  Median follow-up CD4 cell  0.029  3  count (cells/mm , IQR) Median change in CD4 cell 3  count (cells/mm , IQR)  0.89  copies/mL (%) NOTE. IQR indicates interquartile range  144  60 40 20 0 --20  -40  Change in daily methadone dose from baseline (mg/day)  Figure 7.1 Change from baseline in methadone dose by regimen type  N evirap in e  E fav iren z  Lo pinav ir + rito na vir  Ata zana vir ± rito navir  R eg im en T ype  145  7.5 References 1. McCance-Katz EF, Gourevitch MN, Arnsten J, Sarlo J, Rainey P, Jatlow P. Modified directly observed therapy (MDOT) for injection drug users with HIV disease. Am J Addict 2002;11:2718. 2. Gourevitch MN, Friedland GH. Interactions between methadone and medications used to treat HIV infection: a review. Mt Sinai J Med 2000;67:429-36. 3. Stocker H, Kruse G, Kreckel P, Herzmann C, Arastéh K, Claus J, Jessen H, et al. Nevirapine significantly reduces the levels of racemic methadone and (R)-methadone in human immunodeficiency virus-infected patients. Antimicrob Agents Chemother 2004;48:4148-53. 4. Altice FL, Friedland GH, Cooney EL. Nevirapine induced opiate withdrawal among injection drug users with HIV infection receiving methadone. AIDS 1999;13:957-62. 5. McCance-Katz EF, Rainey PM, Friedland G, Jatlow P. The protease inhibitor lopinavirritonavir may produce opiate withdrawal in methadone-maintained patients. Clin Infect Dis 2003;37:476-82. 6. Clarke S, Mulcahy F, Bergin C, Reynolds H, Boyle N, Barry M, Back DJ. Absence of opioid withdrawal symptoms in patients receiving methadone and the protease inhibitor lopinavirritonavir. Clin Infect Dis 2002;34:1143-5. 7. Friedland G, Andrews L, Schreibman T, Agarwala S, Daley L, Child M, Shi J, et al. Lack of an effect of Atazanavir on steady-state pharmacokinetics of methadone in patients chronically treated for opiate addiction. AIDS 2005;19:1635-41. 8. Sorensen JL, Mascovich A, Wall TL, DePhilippis D, Batki SL, Chesney M. Medication adherence strategies for drug abusers with HIV/AIDS. AIDS Care 1998;10:297-312. 9. Stenzel MS, McKenzie M, Mitty JA, Flanigan TP. Enhancing adherence to HAART: a pilot program of modified directly observed therapy. AIDS Reader 2001;11:317-28. 10. Mitty JA, Macalino GE, Bazerman LB, Loewenthal HG, Hogan JW, MacLeod CJ, Flanigan TP. The use of community-based modified directly observed therapy for the treatment of HIVinfected persons. J Acquir Immune Defic Syndr 2005;39:545-50. 11. Conway B, Prasad J, Reynolds R, Farley J, Jones M, Jutha S, Smith N, et al. Directly observed therapy for the management of HIV-infected patients in a methadone program. Clin Infect Dis 2004;38:S402-8. 12. Eap CB, Buclin T, Baumann P. Interindividual variability of the clinical pharmacokinetics of methadone: implications for the treatment of opioid dependence. Clin Pharmacokinet 2002;41:1153-93. 146  13. Clarke SM, Mulcahy FM, Tjia J, Reynolds HE, Gibbons SE, Barry MG, Back DJ. Pharmacokinetic interactions of nevirapine and methadone and guidelines for use of nevirapine to treat injection drug users. Clin Infect Dis 2001;33:1595-7. 14. Clarke SM, Mulcahy FM, Tjia J, Reynolds HE, Gibbons SE, Barry MG, Back DJ. The pharmacokinetics of methadone in HIV-positive patients receiving the non-nucleoside reverse transcriptase inhibitor efavirenz. Br J Clin Pharmacol 2001;51:213-7. 15.  Staszewski  S,  Haberl  A,  Gute  P,  Nisius  G,  Miller  V,  Carlebach  A.  Nevirapine/didanosine/lamivudine once daily in HIV-1-infected intravenous drug users. Antivir Ther 1998;3:S55-6. 16. Stevens RC, Rapaport S, Maroldo-Connelly L, Patterson JB, Bertz R. Lack of methadone dose alterations or withdrawal symptoms during therapy with lopinavir/ritonavir. J Acquir Immune Defic Syndr 2003;33:650-1.  147  CHAPTER VIII  Conclusions and Recommendations for Future Work  8.1 Introduction The introduction of highly active antiretroviral therapy (HAART) has revolutionized the treatment of HIV infection by dramatically suppressing HIV RNA to undetectable levels, restoring CD4 cell counts to higher levels and prolonging the life of individuals infected with HIV/AIDS (1, 2). Despite these survival benefits, the clinical management of HIV infection presents multiple challenges. Most importantly, high levels of adherence are constantly required to achieve and maintain virologic suppression (3), with incomplete adherence being associated with virologic failure and rapid emergence of antiretroviral drug resistance (4). This is all the more significant among injection drug users (IDUs) who are less likely to demonstrate high level of adherence to antiretroviral medications (5). In general, IDUs derive less benefit from antiretroviral therapy despite the fact that HAART is as effective in treating HIV in IDUs as in other populations (6). Lower access and adherence to HAART generally translate into inferior clinical outcomes among this population (7-9). This is attributed to an array of co-morbid conditions that involve addiction, co-infections, psychiatric illnesses and social instability. Thus, the provision of optimal care to HIV-infected IDUs continues to be a major challenge (10). In the face of all these challenges, several interventions and novel HAART delivery modalities have been developed to address the barriers of treatment of HIV infection in IDUs (10). Strategies aimed at addressing the social barriers include outreach programs that help to identify HIV-infected IDUs and refer them to appropriate care (11, 12). Strategies aimed at addressing barriers that arise from health care providers include interventions that help to increase physician education in order to improve evidence-based HIV care in this population (13). The main strategies, however, include interventions that help to increase adherence to HAART and monitor the multiple health issues associated with HAART and illicit drug use (14). One of these interventions is based on modified directly observed therapy (DOT) programs (15-18). Within a DOT setting, an additional important strategy is based on linking the treatment of HIV to the treatment of addiction since methadone maintenance therapy has been been associated with increased adherence to HAART and improved clinical outcomes (16-18). However, concerns about increased drug resistance with DOT as well as skepticism about sustainability and 148  durability of responses resulting from the widespread use of this strategy raise several doubts about its feasibility (19, 20). Such strategies have been demonstrated to be effective in addressing the barriers to HIV treatment but have nevertheless failed in a number of settings. The outcome of HIV-infected IDUs in the era of HAART is strictly dependent on the scaling-up and the rigorous evaluation of these strategies. This thesis, therefore, focused on evaluating the treatment of HIV infection in IDUs within the context of an established DOT program. Taken together, these data add significantly to the body of knowledge of treatment of HIV infection in IDUs.  8.2 General Conclusions In assessing initial and long-term responses to HAART within real-life settings, the results of our longitudinal study demonstrated that the treatment of HIV infection in IDUs can be achieved with superior virologic responses with regimens taken as DOT compared to regimens taken based on self-administered therapy (SAT). We reported lower rates of virologic suppression (2339%) compared to other DOT studies (15, 17, 21) simply because we used the intent-to-treat analysis approach. However, in analyzing similar data generated in another study involving the on-treatment analysis, the rate of virologic suppression was in the range of 67-76% in IDUs receiving HAART. Equally important, our results demonstrated statistically significantly higher rates of retention on HAART with DOT compared to the standard of care over a period of 2 years. Therefore, we postulate that the success of DOT does not depend only on increasing adherence to HAART but also on retaining patients on treatment and in care for longer periods of time, in our case within a multidisciplinary community clinic. The rate of HAART discontinuation was high in our population of IDUs and was driven mainly by self-discontinuation and adverse events. This is consistent with results from previous studies conducted in this population (22, 23). The high rate of treatment discontinuation in our study might be interpreted by some researchers as having non-durable responses to HAART (over the period of 2 years). However, given the intensive nature of HAART, the effect of concurrent illicit drug use and the unstable chaotic lifestyle of the patients, these rates may actually be considered acceptable. It is worth noting that patients who were not kept on their regimens for periods of 2 years or more were not terminating HIV treatment or dropping out of the program. In most cases, such patients were switching to other regimens, which may themselves have been 149  successful. However, these issues constitute a major challenge to DOT and need to be properly addressed in the future. Additional research is also required to improve sustainability of treatment responses using modified DOT. Modifications during antiretroviral therapy, defined as a change made to one of the backbone nucleoside reverse transcriptase inhibitor (NRTI) components of antiretroviral therapy without changing the other components of HAART, were significantly associated with virologic suppression, retention on HAART as well as with fewer emergences of drug resistance mutations. Such modifications were generally made to avoid adverse events, initiate hepatitis C treatment (use of some NRTIs contraindicated with ribavirin) and to simplify HAART regimens. Thus, another mechanism by which DOT can act is by more closely monitoring patients for clinical manifestations of drug toxicity or signs of methadone withdrawal and when necessary by simplifying regimens to convenient once-daily or fixed-dose combinations of HAART which seem to work best in this hard-to-treat population. In our study, some patients highly adherent to treatment were switched to weekly or monthly DOT as they were considered “graduates” of the DOT program. Such an approach could be a solution to the question that arises regarding the feasibilility of having DOT programs be used to engage vulnerable populations in HIV treatment, as those who respond well to HAART as well as to other health care interventions could be considered for less intensive (and less costly) models of care over time, as it is likely that HAART will be continued for many years, if not their entire lives. Further studies are necessary to evaluate the long-term impact of switching patients from daily to weekly or monthly drug dispensing models. The ultimate goal of treating HIV infection in IDUs is to enable patients to consistently self-administer their medications over time, but in the meantime, the ideal approach may be to keep administrating HAART within a flexible DOT setting until they are ready to do so. Despite the raised concerns about the increased risk of development of drug resistance mutations with DOT (19, 20, 24), the results of our longitudinal study showed that this risk was truly negligible. This supports the findings of another published study by Maru et al. which has shown no clinically meaningful increase in antiretroviral drug resistance with the use of modified DOT (25). Although DOT does not seem to prevent the development of drug resistance mutations, at the very least, we can clearly state that it is not associated with an enhanced rate of accumulation 150  of such mutations. Therefore, our results provide further support for the use of DOT as a tool for the initial administration of HAART to IDUs. Furthermore, protease inhibitor (PI)-based regimens, taken within a DOT setting, seemed to have less tendency of developing drug resistance mutations. Moreover, factors associated with less emergence of drug resistance mutations included the use of boosted or unboosted PIs, < 3rd line of HAART, modifications during therapy and baseline CD4 cell count > 200 cells/mm3. As reported by several other studies, boosted PI-based regimens are generally associated with lower rates of emergence of drug resistance mutations because of their higher genetic barrier and having more forgiving profiles in term of having the ability to achieve virologic suppression with less than optimal levels of adherence (26, 27). Thus, the use of a PI-based regimen would probably be a better option regarding the development of drug resistance mutations in IDUs who are suspected of being not fully compliant to HAART. To address the concerns of having higher rates of primary drug resistance within the high risk IDU community, we estimated the prevalence of mutations associated with drug resistance in a cohort of antiretroviral-naive IDUs with established chronic infection (since the date of HIV infection was not available and detuned assays were not performed to determine whether the patients had recent infection or not). Interestingly, the prevalence of primary genotypic and phenotypic drug resistance was relatively low (4.7%) in this IDU cohort. On the other hand, polymorphisms in the reverse transcriptase (RT) and protease genes were very common and certain specific patterns (such as genetic changes at codon 135 in the RT gene) were observed with a much higher frequency than previously reported in untreated patient populations. Since mutations at RT codon 135 (mainly 135T) occurring at such a high frequency (more than 40% of cases) in our antiretroviral naïve IDU population may have a significant impact on response rates to non-nucleoside reverse transcriptase inhibitor (NNRTI)-based therapy, we evaluated treatment responses and the emergence of drug resistance mutations in patients with and without mutations at codon 135 prior to the initiation of HAART. Our results indicated no statistically significant differences in CD4 cell counts and HIV plasma viral load responses to NNRTI-based regimens as a function of baseline 135 genotype. However, in patients with baseline mutations at codon 135 and experiencing virologic breakthrough, there was more evolution of single and less evolution of multiple NNRTI resistance mutations. This may have 151  important implications with respect to the initial selection of patients to receive NNRTI-based therapy at baseline with a view to choosing NNRTIs using newer agents (such as etravirine) in this class in subsequent courses of therapy. Infection with hepatitis B virus (HBV) and hepatitis C virus (HCV) are known to be major independent risk factors for the development of hepatotoxicity in patients receiving HAART (28, 29). In our cohort of HIV/HCV co-infected IDUs, severe hepatotoxicity occured; however, the majority of patients did not develop such hepatotoxicity following the initiation of nevirapinebased antiretroviral therapy. The incidence of hepatotoxicity associated with nevirapine use was similar to that reported in non-IDUs during the first year of therapy. Thus, the regular contact between health care providers and IDUs, afforded by a DOT program, may add to the benefit of more frequent monitoring of liver function test abnormalities, as well as monitoring the other drug-associated adverse events. Early detection may allow for more timely intervention to prevent such adverse clinical outcomes. In other findings of this study, the risk of severe hepatotoxicity was eightfold higher among patients co-infected with HCV, four-fold higher in patients naïve to antiretroviral therapy and three-fold higher in patients having elevated alanine aminotransferase levels at baseline. Therefore, application of these criteria can allow us to define a population of IDUs and non-IDUs in whom nevirapine-based therapy can be safely prescribed. Finally, concerning methadone interactions with antiretroviral drugs, our results demonstrated that nevirapine and efavirenz-methadone interactions almost always required moderate increases in methadone dosage, with a median of 30.8% and 13.1% increase in methadone dosage required to maintain the therapeutic benefit of opiate substitution in patients receiving nevirapine and efavirenz-based HAART, respectively. This is in clear agreement with other clinical studies investigating the interactions of NNRTIs with methadone (30, 31). On the other hand, in patients receiving lopinavir or atazanavir-based HAART, our data showed that no meaningful adjustment in methadone dosage was necessary, in contrast with results from some acute pharmacokinetic studies (32). Therefore, although methadone-based DOT can be a very successful tool for the coadministration of HAART in relevant patients, our findings indicate that careful monitoring is required to ensure that methadone withdrawal does not adversely affect the goals of treatment, particularly when NNRTI-based regimens are used.  152  8.3 Overall Significance and Future Directions IDUs constitute an increasing proportion of HIV-infected individuals in several inner cities of North America such as in Baltimore, New York and Vancouver. Traditionally, treatment of HIV infection has been withheld from such patients because of concerns of low adherence to HAART and the increased risk of developing resistance to antiretroviral drugs. However, HIV-infected IDUs need to be effectively treated in order to manage and control the established HIV epidemic among this population and consequently to reduce the transmission of HIV infection to noninfected members within the community. In addition, the mode of treatment delivery must meet the demands of the complicated social needs of these patients. The research presented in this thesis shows that the presence of interventions such as DOT may offer a unique opportunity to effectively treat and manage HIV-infected IDUs without causing excessive accumulation of drug resistance mutations and even without resulting in high transmission of resistant viruses to non-infected members within the IDU community. Such interventions are especially successful if they are implemented within the context of a multidisciplinary clinic where comprehensive primary care is integrated with medical treatment of HIV, HBV, HCV, tuberculosis, psychiatric illness and addiction via systematic collaboration between primary care physicians, addiction specialists, infectious disease specialists, nurses, counselors and researchers (16). Modified DOT for HIV may provide several advantages for the treatment of individuals with active substance abuse disorders (33, 34). First, DOT may help to ensure adequate adherence to HAART (and other medications) and at the same time monitor the multiple health issues associated with HAART use (such as side effects and drug interactions with methadone) as well as with illicit drug use. Second, DOT may help to retain patients on therapy for longer periods of time and by doing so increase their engagement in care and establish a viable relationship between patients and health care providers. The trust that is developed between clinic staff and patients can also be used to help encourage a reduction in high-risk sexual and drug use behaviors. Third, DOT may provide a source of social support by enhancing linkages to existing community-based resources and help in addressing socio-structural barriers such as housing and food which in turn may help IDUs to maintain their engagement in care. All these aspects of DOT may altogether help to improve therapeutic outcomes in HIV-infected IDUs. It is worth mentioning, however, that DOT should accommodate the instability in the lives of these patients 153  because of illicit drug use, incarceration, unstable housing and cycling in and out of detoxification programs. Moreover, it should provide flexibility without overly constraining patients, be voluntary and ultimately empower patients to reliably self-administer their medications over time. Knowing the fact that addiction to illicit drugs is a chronic relapsing disorder, programs such as DOT do not eliminate the problem of drug addiction, since most drug addicts will still have their physiological and psychological drives to continue illicit drugs. In addition, such interventions do not necessarily alleviate the problems of homelessness, unemployment, or other medical and social problems in this vulnerable population. However, active drug use per se should not be considered a contraindication to initiate or receive HAART (14, 35). Our research demonstrates that treatment of HIV infecton in active IDUs can be very feasible and structured programs for the delivery of HAART may be successful despite ongoing illicit drug use, particularly when treatment of HIV is coupled with treatment of heroin addition within a methadone replacement therapy program. Although the majority of our study patients used drugs other than heroin, our results cannot be generalized to settings where cocaine or crystal methamphetamines are used as the main drugs of addiction. Therefore, future research should look into the effect of each illicit drug (such as cocaine, benzodiazepines, methamphetamines and heroin) on HIV treatment outcomes including virologic and immune responses as well as the development of drug resistance mutations, within DOT or similar settings. Innovative or modified strategies for the treatment of HIV infection among IDUs who abuse cocaine or other illicit drugs might be necessary to ensure optimal levels of virologic suppression are achieved in this population. In light of the successes achieved in treating HIV infection in IDUs, it remains necessary to continue to devote sufficient research and program resources to build on the successes achieved, address the persisting challenges and evaluate longer term outcomes such as mortality with the use of DOT strategy in this population. In the immediate future, we would respectfully submit at least three key areas of research that must be considered to optimally build on our work. First, there is a need to engage larger numbers of patients in care, especially those who do not yet even engage in the health care system in any way. This would need to be done through their 154  points of contact such as needle exchange sites, outreach centres and even Vancouver’s supervised injection site. The “hook” may be to encourage individuals at risk of HIV infection to simply be tested, and to use a positive result as a lever to establish a therapeutic relationship that may eventually lead to treatment not only of the HIV infection itself, but also the underlying addiction as well as other physical and psychiatric conditions. Second, the vast majority of patients are co-infected with HCV. We need to develop strategies to treat this other infection since if we do not do so, it will progress over time and lead to significant morbidity and mortality that would quickly negate any benefit that we would have gained by treating the HIV in the first place. It may be that the best approach would be a joint HIV/HCV program with the initial emphasis being on the more prevalent HCV infection to gain maximal impact of a community-based program. Third, we need to continue to show the long-term benefit of our program in the almost 250 individuals who have participated in it, to be able to answer the critics that would state that our approach is neither sustainable nor cost-effective. In this light, it will also be important to demonstrate ways in which the care of our patients can be optimized over time, as their health improves and their engagement increases, so as to allow as many people as possible to benefit, given our limited resources.  155  8.4 Closing Remarks IDUs are considered to be one of the most socially and medically marginalized populations in the world. The clinical care of IDUs who have HIV is very challanging and stressful and many clinicians avoid treating such patients based on perceptions that treatment of IDUs is futile or even contraindicated on medical grounds. In the work I have presented in this thesis I have demonstrated that responses to HAART as well as retention to therapy in IDUs can be as good as reported in other populations without causing excessive accumulation of drug resistance mutations. Several guidelines and principles developed for the treatment of HCV in IDUs (36, 37) can be also applied to the treatment of HIV infection. These include principles of developing a professional relationship between IDUs and health care providers with showing mutual respect and avoiding blame and judgement, acknowledging that success requires several attempts, establishing realistic commitments and avoiding unrealistic expectations, reducing barriers to accessing the health care system, educating drug users about health care and involving them in decision-making and, as much as my research shows, establishing a multidisciplinary-based treatment approach to effectively treat HIV/AIDS and at the same time address the multiple and complex health barriers associated with illicit drug use and HIV-related comorbidities in this population. Personally, having been given the opportunity to evaluate the treatment of HIV infection in IDUs over a period of 6 years, it is very rewarding to have contributed to the growing knowledge of treating HIV infection in IDUs and also to have reached the conclusion that working with innercity IDUs can be feasible and treatment of their HIV infection can be remarkably successful despite their ongoing injection of illicit drugs.  156  8.4 References 1. Hammer SM, Katzenstein DA, Hughes MD, Gundacker H, Schooley RT, Haubrich RH, Henry WK, et al. A trial comparing nucleoside monotherapy with combination therapy in HIVinfected adults with CD4 cell counts from 200 to 500 per cubic millimeter. AIDS Clinical Trials Group Study 175 Study Team. N Engl J Med 1996;335:1081-90. 2. Pallela FJ, Delaney KM, Moorman AC, Loveless MO, Fuhrer J, Satten GA, Aschman DJ, et al. Declining morbidity and mortality among patients with advanced human immunodeficiency virus infection. 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Int J Drug Policy 2004;15:81-91.  160  APPENDIX LIST OF PUBLICATIONS AND ABSTRACTS  Material from this dissertation has been published in: •  Tossonian HK, Raffa JD, Grebely J, Viljoen M, Mead A, Khara M, McLean M, Krishnamurthy A, DeVlaming S, Conway B. Primary Drug Resistance in Antiretroviral Naïve Injection Drug Users. International Journal of Infectious Diseases (In Press).  •  Tossonian HK, Raffa JD, Grebely J, Trotter B, Viljoen M, Mead A, Khara M, McLean M, Duncan F, Fraser C, DeVlaming S, Conway B. Methadone Dosing Strategies in HIVInfected Injection Drug Users Enrolled in a Directly Observed Therapy Program. Journal of Acquired Immune Deficiency Syndrome 2007;45:324-7.  •  Tossonian HK, Raffa JD, Grebely J, Viljoen M, Mead A, Khara M, McLean M, Krishnamurthy A, DeVlaming S, Conway B. Clinical Implications of Mutations at Reverse Transcriptase Codon 135 on Response to NNRTI-Based Therapy. The Open Virology Journal 2007;1:8-13.  •  Conway B, Grebely J, Tossonian H, Lefebvre D, DeVlaming S. A Systematic Approach to the Treatment of HIV and HCV Infection in the Inner City: A Canadian Perspective. Clinical Infectious Diseases 2005;41:S73-8.  Material from this dissertation has been presented in oral or poster format at the following conferences or meetings: •  Tossonian H, Raffa J, Grebely J, Viljoen M, Khara M, Mead A, McLean M, Krishnamurthy A, DeVlaming S, Conway B. Directly Observed Therapy Reduces the Rate of Accumulation of Drug Resistance Mutations in Injection Drug Users [Abstract P3.4/10]. In: Program and Abstracts of the 11th European AIDS Conference, Madrid, Spain, October 24-27, 2007.  •  Tossonian H, Raffa J, Grebely J, Rashidi B, Viljoen M, Khara M, Mead A, McLean M, Krishnamurthy A, DeVlaming S, Conway B. Natural History of Treatment of HIV-1 Infection in Injection Drug Users [Abstract WEPEB103]. In: Program and Abstracts of the 4th IAS Conference on HIV Pathogenesis and Treatment, Sydney, Australia, July 22-25, 2007.  161  •  Tossonian H, Raffa J, Grebely J, Viljoen M, Khara M, Mead A, McLean M, Krishnamurthy A, DeVlaming S, Conway B. Directly Observed Therapy Reduces the Rate of Accumulation of Drug Resistance Mutations in Injection Drug Users [Abstract O033]. Canadian Journal of Infectious Diseases and Medical Microbiology 2007;18:22B.  •  Tossonian H, Raffa J, Grebely J, Viljoen M, Khara M, Mead A, McLean M, Krishnamurthy A, DeVlaming S, Conway B. Natural History of Treatment of HIV-1 Infection in Injection Drug Users [Abstract P198]. Canadian Journal of Infectious Diseases and Medical Microbiology 2007;18:70B.  •  Tossonian H, Raffa J, Grebely J, Viljoen M, Khara M, Mead A, McLean M, Duncan F, Krishnamurthy A, DeVlaming S, Conway B. Follow-Up at 72 Months of HIV-Infected Injection Drug Users (IDUs) Receiving HAART within a Directly Observed Therapy (DOT) Program [Abstract 946]. In: Program and Abstracts of the 44th Annual IDSA/HIVMA, Toronto, Canada, October 12-15, 2006.  •  Tossonian H, Raffa J, Grebely J, Viljoen M, Khara M, Mead A, McLean M, Duncan F, Krishnamurthy A, DeVlaming S, Conway B. Clinical Implications of Mutations at Codon 135 on Response to NNRTI-Based Therapy [Abstract 972]. In: Program and Abstracts of the 44th Annual IDSA/HIVMA, Toronto, Canada, October 12-15, 2006.  •  Tossonian H, Raffa J, Grebely J, Hofmann C, Mistry A, Winther A, Viljoen M, DeVlaming S, Conway B. Hepatotoxicity in Injection Drug Users (IDUs) and Non-IDUs Receiving Nevirapine-Based HAART [Abstract WEPE0176]. In: Program and Abstracts of the XVI International AIDS Conference, Toronto, Canada, August 13-18, 2006.  •  Tossonian H, Raffa J, Grebely J, Viljoen M, Khara M, Mead A, McLean M, Duncan F, DeVlaming S, Conway B. Directly Observed Therapy (DOT) for the Treatment of HIVInfection in Injection Drug Users (IDUs): 2000-2005 [Abstract THPE0154]. In: Program and Abstracts of XVI International AIDS Conference, Toronto, Canada, August 13-18, 2006.  •  Tossonian H, Raffa J, Grebely J, Hofmann C, Mistry A, Winther A, DeVlaming S, Conway B. Hepatotoxicity in Injection Drug Users (IDUs) and Non-IDUs Receiving Nevirapine-Based HAART [Abstract 218]. Canadian Journal of Infectious Diseases and Medical Microbiology 2006;17:28A.  •  Tossonian H, Raffa J, Viljoen M, Khara M, Mead A, McLean M, Duncan F, Krishnamurthy A, DeVlaming S, Conway B. Prevalence and Impact of Primary Resistance  162  in Drug Naïve Injection Drug Users (IDUs) [Abstract 248P]. Canadian Journal of Infectious Diseases and Medical Microbiology 2006;17:37A. •  Tossonian H, Raffa J, Grebely J, Viljoen M, McLean M, Duncan F, Khara M, Culbert H, MacDonald S, DeVlaming S, Conway B. Development of Resistance in Injection Drug Users (IDUs) Receiving HAART within a Directly Observed Therapy (DOT) Program [Abstract PE3.4/6]. In: Program and Abstracts of the 10th European AIDS Conference, Dublin, Ireland, November 17-20, 2005.  •  Tossonian H, Raffa J, Grebely J, Trotter B, Tyndall M, Fraser C, DeVlaming S, Conway B. Methadone Dosing Strategies When Starting or Changing HAART Regimens in HIVInfected Injection Drug Users Enrolled in a Directly Observed Therapy Program [Abstract WePe3.3C10]. In: Program and Abstracts of the 3rd IAS Conference on HIV Pathogenesis and Treatment, Rio De Janeiro, Brazil, July 24-27, 2005.  •  Tossonian H, Raffa J, Rashidi B, Viljoen M, McLean M, Duncan F, MacDonald S, Khara M, Culbert H, DeVlaming S, Conway B. Development of Resistance in Injection Drug Users Receiving HAART within a Directly Observed Therapy Program [Abstract 36]. Antiviral Therapy 2005;10:S38.  •  Raffa J, Tossonian H, Grebely J, Viljoen M, McLean M, Duncan F, MacDonald S, Khara M, DeVlaming S, Conway B. No Changes Required in Methadone Dose When Starting or Changing Lopinavir- or Atazanavir-Based HAART Regimens in HIV-Infected Injection Drug Users (IDUs) [Abstract 272P]. Canadian Journal of Infectious Diseases and Medical Microbiology 2005;16:53A.  •  Tossonian H, Raffa J, Grebely J, DeVlaming S, Conway B. HIV-Infected Injection Drug Users (IDUs) on Lopinavir or Atazanavir-Based HAART Do Not Require Changes in Methadone Dose When Starting or Changing Regimens in a Directly Observed Therapy (DOT) Program [Abstract 202]. In: Program and Abstracts of the 2nd Therapeutics Congress, Vancouver, Canada, April 13-19, 2005.  Material from this dissertation has also been presented orally for the Graduate Student Seminars Series in the Department of Anesthesiology, Pharmacology and Therapeutics at the University of British Columbia.  163  

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