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Pharmacokinetics and limited sampling strategies of mycophenolic acid in lung transplant recipients Ting, Lillian S.L. 2005

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PHARMACOKINETICS AND LIMITED SAMPLING STRATEGIES OF MYCOPHENOLIC ACID IN LUNG TRANSPLANT RECIPIENTS by LILLIAN S. L. TING B.Sc., The University of  British Columbia, 2001 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES (Pharmaceutical Sciences) THE UNIVERSITY OF BRITISH COLUMBIA August 2005 © Lillian S L Ting, 2005 Abstract Mycophenolic acid (MPA), the active metabolite of  mycophenolate mofetil  (MMF), an immunosuppressive agent used in organ transplantation, exhibits wide inter-patient variability in its pharmacokinetic (PK) parameters in various transplant groups. However, there are few  studies in the lung transplant population. The objectives of  this study were to investigate the pharmacokinetics and limited sampling strategies (LSSs) of  MPA in lung transplant recipients. Twenty-one lung transplant recipients on steady-state MPA therapy were recruited. Patients were also receiving either cyclosporine (CSA; n=ll) or tacrolimus (TAC; n=9) as co-medications. Blood samples were collected at 0, 20, 40, 60, 90 minutes, and at 2, 4, 6, 8, 10, 12 hours post-dose. Concentrations of  MPA, free  MPA, 7-O-mycophenolate glucuronide (MPAG) and acyl mycophenolic acid glucuronide (AcMPAG) in the plasma samples were measured by a high performance  liquid chromatography-ultraviolet detection assay. Conventional PK parameters were determined using non-compartmental methods. There was large inter-patient variability in all PK parameters of  MPA, MPAG and AcMPAG. Similar variability was observed after  stratifying  patients into concomitant medication groups, CSA and TAC. The CSA group had lower MPA, higher MPAG and AcMPAG levels than the TAC group. The mean MPA free  fraction  was 7.0%, more than twice the expected value. Nineteen of  the twenty-one subjects were included in the LSS study. Multiple regression analysis was used to develop the LSSs. Subjects were randomly divided into the index (n=10) and the validation groups (n=9). Index group data were used to develop the LSSs, which were tested with the validation group data. Accuracy and precision of  the LSSs were determined by calculating the mean prediction error and the root mean square error, respectively. Two single-concentration LSSs, eight 2-concentration LSSs, and eight 3-concentration LSSs met predetermined criteria. However, considering both cost and clinical feasibility,  the recommended LSSs were: LogAUC=0.241 LogCO+0.406 LogC2+1.140 LogAUC=0.202 LogC0+0.41 lLogCl.5+1.09 Because of  the large inter-patient variability in the PK parameters of  MPA, MPAG, and AcMPAG, therapeutic drug monitoring of  MPA and its metabolites in lung transplant recipients may be beneficial.  Adoption of  the LSSs developed in this study should provide convenient and cost-effective  estimation of  MPA exposure in lung transplant recipients. Table of  Contents Title i Abstract ii Table of  Contents iv List of  Tables vii List of  Figures viii List of  Abbreviations ix Acknowledgements xii Chapter 1 - Introduction 1.1 Transplantation 1 1.1.1 Introduction 1 1.1.2 Lung Transplantation 1 1.1.3 Rejection 2 1.1.3 a Hyperacute Rejection 3 1.1.3 b Acute Rejection 4 1.1.3 c Chronic Rejection 4 1.1.3 d Rejection in Lung Transplantation 4 1.1.4 Immunosuppressive Therapy 5 1.1.4 a Cyclosporine 8 1.1.4b Tacrolimus 9 1.1.4 c Corticosteroids 10 1.2 Mycophenolate Mofetil  11 1.2.1 Overview 11 1.2.2 History 12 1.2.3 Physical-Chemical Properties 13 1.2.4 Pharmacology and Mechanism of  Action 13 1.2.5 Pharmacokinetics 16 1.2.5 a Absorption 16 1.2.5 b Distribution 16 1.2.5 c Metabolism 16 1.2.5 d Excretion 17 1.2.6 Toxicity 17 1.2.7 Therapeutic Drug Monitoring of  MPA 18 1.2.8 UDP-Glucuronosyltransferases  20 1.2.8 a Activities 20 1.2.8 b Classification  21 1.2.8 c Polymorphisms 22 1.3 Limited Sampling Strategies 23 1.3.1 Methods in Establishing Limited Sampling Strategies 24 1.3.1 a Multiple Regression Analysis 25 1.3.1 b Bayesian Analysis 1.3.2 Validation of  Predictive Performance  of  LSSs 26 29 1.4 Significance  of  Research 30 1.5 Hypotheses 31 1.6 Specific  Aims 31 1.7 References  32 Chapter 2 - Pharmacokinetics of  Mycophenolic Acid and its Glucuronidated Metabolites in Lung Transplant Recipients 2.1 Specific  Aim #1 44 2.2 Introduction 44 2.3 Methods 47 2.3.1 Patient Population 47 2.3.2 Plasma Concentrations of  MPA, MPAG and AcMPAG 48 2.3.3 Stability of  AcMPAG 51 2.3.4 Free MPA Extraction 51 2.3.5 Assessment of  Pharmacokinetic Parameters 52 2.3.6 Statistical Analysis 52 2.4 Results 53 2.4.1 Patient Characteristics 53 2.4.2 MPA Pharmacokinetics 54 2.4.3 Free MPA 60 2.5 Discussion 60 2.5.1 Inter-Patient Variability in PK Parameters 60 2.5.2 Intra-Patient Variability 65 2.5.3 Concomitant Immunosuppressants - CSA and TAC 65 2.5.4 Cystic Fibrosis 66 2.6 Summary of  MPA PKs 67 2.7 References  67 Chapter 3 - Limited Sampling Strategies of  Mycophenolic Acid for  Estimation of  Area Under the Concentration-Time Curve in Lung Transplant Recipients 3.1 3.2 Specific  Aim #2 Introduction 73 73 3.3 Methods 75 3.3.1 Patient Population and MPA Concentrations 75 3.3.2 Pharmacokinetic Parameters Assessment 75 3.3.3 Limited Sampling Strategy Determination 75 3.3.4 Validation of  LSSs 76 3.4 Results 77 3.4.1 Study Subjects'Characteristics 77 3.4.2 LSS With Best Correlation With AUC 78 3.4.3 LSSs Using Single Concentration 78 3.4.4 LSSs Using Two Concentrations 78 3.4.5 LSSs Using Three Concentrations 79 3.5 Discussion 81 3.5.1 Current Status of  MPA LSSs 81 3.5.2 Characteristics of  Index and Validation Groups 82 3.5.3 Concomitant Immunosuppressants - CSA and TAC 84 3.5.4 Log-transformation  85 3.5.5 Recommended LSSs 85 3.6 Summary of  MPA LSSs 86 3.7 References  87 Chapter 4 - Overall Summary and Conclusion 4.1 Overall Discussion and Conclusion 93 4.2 Strengths and Weaknesses 95 4.3 Current Knowledge and New Ideas 95 4.4 Status of  Working Hypotheses 96 4.5 Overall Significance  of  the Thesis Research 96 4.6 Future Research 96 4.7 References  97 Appendix 1 - UBC Clinical Research Ethics Board Approved Consent Form 100 Appendix 2 - Example Nomogram for  Anti-Log Conversion of  104 Concentrations and AUCs List of  Tables Table 1-1 Summary of  UGT isoenzymes 22 Table 2-1 Intra-day and inter-day coefficient  of  variation (CV) of  MPA, 51 MPAG and AcMPAG measurements at three concentrations Table 2-2 Characteristics of  lung transplant recipients who participated in this 54 study Table 2-3 PK parameters and metabolic ratios of  MPA in lung transplant 56 recipients Table 2-4 Selected MPA pharmacokinetic studies from  other research groups 62 Table 3-1 Characteristics of  lung transplant recipients in the index and 77 validation groups Table 3-2 Predictive performance  of  one and two-concentration limited 79 sampling strategies Table 3-3 Predictive performance  of  three-concentration limited sampling 80 strategies Table 3-4 Selected MPA LSSs developed by multiple regression analysis from  83 other research groups List of  Figures Figure 1-1 Immunosuppressive agents and their sites of  action 7 Figure 1 -2 Chemical structures of  MMF, MPA, MPAG and AcMPAG 15 Figure 2-1 HPLC chromatogram of  MPA, MPAG and AcMPAG 50 Figure 2-2 HPLC chromatogram of  blank sample 50 Figure 2-3 MPA PK profiles  (mean ± SD) 57 a. All subjects b. Subjects stratified  into CSA and TAC groups Figure 2-4 MPAG PK profiles  (mean ± SD) 58 a. All subjects b. Subjects stratified  into CSA and TAC groups Figure 2-5 AcMPAG profiles  (mean ± SD) 59 a. All subjects b. Subjects stratified  into CSA and TAC groups < > °c ng (j,L |.imol AcMPAG AUC CO CF Cl/F cm Cmax Cmin CSA Ctrough CV Cx CYP DN List of  Abbreviations Greater than Less than Greater than or equal to Degree Celsius Microgram Microliter Micromole Acyl glucuronide of  mycophenolic acid Area under the concentration-time curve Concentration taken at 0 hours post-dose/ trough concentration Cystic fibrosis Apparent clearance Centimeter Maximum concentration Minimum concentration Cyclosporine Trough concentration Coefficient  of  variation Concentration taken at x hours post-dose Cytochrome P450 Dose-normalized e.g. For example etc. Etcetera fMPA  free  fraction  of  mycophenolic acid g Gram GI Gastrointestinal HPLC High performance  liquid chromatography hr Hour i.e. That is IL Interleukin IMPDH Inosine monophosphate dehydrogenase IS Internal standard Kg Kilogram L Liter LSS Limited sampling strategy ME Mean prediction error mg Milligram min Minute mL Milliliter MMF Mycophenolate mofetil MPA Mycophenolic acid MPAG 7-O-mycophenolic acid glucuronide MRA Multiple regression analysis N/A Not available OB Obliterative bronchiolitis PK Pharmacokinetic r Correlation coefficient r2 Coefficient  of  determination RMSE Root mean square error SD Standard deviation TAC Tacrolimus TDM Therapeutic drug monitoring Tmax Time when maximum concentration occurs UBC University of  British Columbia UGT UDP-glucuronosyltransferase yrs Years Acknowledgments I would like to thank my supervisors, Drs. Mary H. H. Ensom and K. Wayne Riggs. I greatly appreciate their guidance, support and friendship  during the past two years. My research experience would never have been as wonderful  without them. They are simply awesome. Special thanks to Ms. Diane Decarie, Mr. Julius Chala, and Ms. Naomi Johnson for their technical and clinical support. I would also like to acknowledge Drs. Nilu Partovi and Bob Levy for  their ideas, suggestions and advice. Thanks t o m y o ther c ommittee m embers, D rs. Stelvio B andiera, T om Chang, and Brian Rodrigues for  their insightful  comments. My heartfelt  thanks to Dr. Chantal Guillemette and her lab members, especially Olivier Bernard, for  allowing my visit to their research lab at Universite Laval (Quebec City, Quebec). The lab training, cultural experience and Quebec cuisine will never be forgotten. I would like to recognize the summer and PharmD students in our lab, Martha Kinnear, Stephanie Tsang, and Judith Marin, for  their friendship  and the laughs that we all enjoyed. Finally, my deepest gratitude to my family  - my parents and my brothers, for  their continuous support and encouragement. Chapter 1 - Introduction 1.1 Transplantation 1.1.1 Introduction Solid organ transplantation has become more feasible  in the past decade as a result of advances in immunology, organ harvesting and organ pathology research. Many children and adults suffering  from  major organ failure  benefit  from  this medical breakthrough, which greatly improves quality of  life  and extends 1 ife  expectancy. The total number of  organ transplants was about 25,000 in the United States in 2003, and survival rates have been improving1. For example, patients' 1-year survival rate has increased from  75% to 80% for lung transplantation from  1993 to 2002 in the United States2. In Canada, 1836 organ transplants were performed  in year 2003, and about 3966 Canadians remain on the waiting list . Kidney transplants are the most common, comprising about one-third of  the total transplantations3. Other solid organ transplantations include lung, heart, liver, pancreas, and intestine4. 1.1.2 Lung Transplantation Lung transplantation has been an effective  but aggressive treatment for  end stage lung diseases, and success rates have improved in the past 20 years with advances in surgical techniques and availability of  various immunosuppressive agents1. Major indications for  lung transplantation are emphysema, chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis,  alpha-1-antitrypsin deficiency,  primary pulmonary hypertension, and cystic fibrosis  (CF)5. Indications for  single-lung transplantation include chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis  and emphysema. Bilateral-lung transplantations are usually performed  in patients with CF and primary pulmonary hypertension1. Despite improvements in immunosuppression, surgical procedures and patient care, survival rates are still relatively low, with a 5-year survival rate of  only 50%. Chronic rejection is a prominent problem that is responsible for  patient morbidity and mortality5. Various factors  may contribute to the challenges in managing lung transplant complications. The lung is one of  the largest organs in the human body, with numerous antigen-presenting cells in the donor organ. In addition, the lung differs  from  other major organs in that it is continuously in contact with the external environment, which increases chances of infections 6. 1.1.3 Rejection Although success rates of  solid organ transplantation have improved over the years, graft  rejection remains a major hurdle. Acute rejection and infection  are the two main causes of  post-transplant morbidity and mortality. To combat rejection requires suppression of  the immune system, yet this exposes patients to various undesirable infections.  In fact, the delicate balance of  modulating the immune system to minimize rejection and infection  is the axis of  clinical transplantation and research. Studies have shown that about 50% of  renal transplant patients experience at least one acute rejection, and these acute rejection episodes are significantly  associated with lower long-term survival rate7. Acute rejection rate is similar in lung transplantation, at 54% at 1 year post-transplant6. The use of immunosuppressive agents is crucial, and the evolution of  such agents continues in efforts  to maximize the success rate of  organ transplantations. The host immune response is triggered by foreign  antigens from  the allograft,  and leads to a series of  cellular and humoral reactions8'9. In the case of  allograft  rejection, the immune response mainly involves stimulation of  T-cell activities, which include activation, differentiation  and proliferation.  Other components such as B-cells, macrophages and O 1 A natural killer cells may also contribute to the complex immune response ' . In brief,  when the body is presented with a foreign  antigen from  the allograft,  T-cell antigen-recognizing receptors are activated. The T-cells may then differentiate  into effector  cells, which mediate synthesis and secretion of  cell signaling agents such as cytokines and interleukins. These molecules, such as interleukin-2 (IL-2), trigger lymphocyte proliferation  by signaling the cell to initiate gene transcription, replication and cell division. Activated T-cells may also differentiate  into memory cells or natural killer cells that target and destroy the foreign antigen-containing cells. The whole cascade of  the T-cell activation pathway results in proliferation  of  lymphocytes, stimulation of  cytotoxic T-cells, activation of  B-cells and production of  antibodies against the allograft 8. 1.1.3 a Hyperacute Rejection Hyperacute rejection refers  to the attack of  the transplanted organ within minutes to days of  transplantation by circulating antibodies in the recipient's bloodstream. This rapid immune response may cause tissue damage and loss of  graft  function,  manifested  by hemorrhage and thrombosis in the graft".  However, cross-match testing for  anti-donor antibodies has greatly reduced this type of  rejection1'12. Acute rejection usually occurs in the first  3 - 1 2 months post-transplant, and is very common. It is mediated by lymphocyte infiltrations  into the transplanted organ. Acute rejections are also associated with chronic rejection and graft  loss1. 1.1.3 c Chronic Rejection Chronic rejection occurs over time after  transplantation. Graft  damage is gradual, and it usually involves vascular damage in the organ, resulting in chronic ischemia, and 18 13 eventually loss of  graft  function  ' ' . 1.1.3 d Rejection in Lung Transplantation Hyperacute rejection is very rare nowadays due to the common practice of  blood type matching and cross-matching for  anti-donor antibodies before  transplantation. However, acute rejection is very common in lung transplant recipients, and about half  of  the patients will experience it at least once in the first  few  months post-transplant1'13. Clinical symptoms include fever,  cough, shortness of  breath, and infiltrate  on chest X-ray; however, some patients are asymptomatic. Diagnosis of  rejection is challenging in lung transplantation, since it is often  difficult  to distinguish rejection from  infection 1'8'13. Transbronchial biopsies are used to confirm  diagnosis. Treatment of  acute rejection includes short courses of  high-dose intravenous corticosteroids, augmenting doses of maintenance immunosuppressive agents, and switching immunosuppressive medications1'13'14. The biggest challenge in lung transplantation is chronic rejection, manifested  by 13 15 • fibrotic  destruction of  the airways, known as obliterative bronchiolitis (OB) ' . It is 8 13 15 commonly seen after  one year of  transplantation, with a prevalence of  about 50% ' ' within 5 years post-transplant, and a 60-80%) prevalence after  5 years16. It is the most common cause of  death in lung transplantation after  one year"'13. OB is difficult  to diagnose, and the pathogenesis is still unclear. It is characterized by progressive decrement of  expiratory flow,  recurrent infections,  dyspnea and cough1 '8 '13 '16. Since these symptoms are non-specific,  diagnosis of  OB relies on monitoring of  expiratory flow  by routine spirometry and lung biopsy. A decline in forced  expiratory volume provides a sign of  OB, while lung biopsy gives a definitive  diagnosis. However, due to the invasive nature of biopsy, this technique i s generally avoided16. T here is no cure for  OB, which is usually irreversible. Treatment strategies are similar to the management of  acute rejection, which include increasing doses of  immunosuppressive agents and addition of  anti-lymphocyte antibodies1'13'16. 1.1.4 Immunosuppressive Therapy Immunosuppressive agents work to inhibit one or more of  the immunological processes that lead to graft  rejection. By blocking part of  the T-cell activation pathway, the immune response is suppressed to minimize rejection. Immunosuppressive drugs can be classified  into several groups according to their sites of  inhibition. These include: inhibitors of  cytokine synthesis, such as cyclosporine (CSA) and tacrolimus (TAC); inhibitors of cytokine action, such as sirolimus; inhibitors of  de  novo purine synthesis and cell proliferation,  such as azathioprine and mycophenolate mofetil  (MMF); and anti-inflammatory  agents, such as prednisone and methylprednisolone. Figure 1-1 summarizes the common immunosuppressive drugs and their modes of  action8. The first  successful  solid organ transplantation was a kidney transplant in a pair of identical twins in 19541. This was possible because of  the matching genetic make-up of  the donor and recipient, despite a lack of  immunosuppressive agents available at that time. Research in immunosuppression then emerged in the 1960s, when Schwartz and Dameshek discovered that the compound 6 -mercaptopurine was able to b lock antibody responses to foreign  p roteins i n experimental a nimals7. A n i midazole d erivative o f  6 -mercaptopurine, azathioprine, was soon developed and showed promising anti-proliferation  of  lymphocytes in dogs that received renal allografts 7. This breakthrough led to further  research in immunosuppressive agents, and a combination therapy of  6-mercaptopurine, azathioprine and prednisone was available in the late 1960s that rendered renal transplantation successful 7. In the mid-1970s, the progress of  solid transplantation took a great leap forward with the discovery of  cyclosporine, which greatly decreased morbidity rate in solid organ transplant recipients. The number of  successful  transplantations increased dramatically with the incorporation of  cyclosporine into the standard immunosuppressive therapy, as cyclosporine improved rejection rates and graft  survival at 1-year post-transplant1. Subsequent development of  immunosuppressive agents such as MMF, TAC, sirolimus and Muromonab-CD3 in the 1980s provided a wider selection of  drugs used to combat graft rejection to date. Current management of  immunosuppression involves immunosuppression induction and maintenance, and the management of  acute rejection episodes. More than one immunosuppressive agent is often  employed at all stages. The first  two weeks after  the transplant is considered the critical period when the risk of  allograft  rejection is highest, and thus the induction phase of  immunosuppression must be managed carefully  to avoid acute rejection but also to minimize infection  of  surgical wounds . High doses of  potent anti-T-cell antibodies (such as Muromonab-CD3), cyclosporine, and IL-2 receptor blockers are commonly used at this stage10. Maintenance of  immunosuppression requires a smaller dosage, and usually is comprised of  a regimen with a calcineurin inhibitor, an antimetabolite, and a corticosteroid. Common combinations include CSA, MMF and prednisone, or TAC, MMF and prednisone. Other combinations are also possible depending on the specific  patient and availability of  other immunosuppressive agents". Figure 1-1. Immunosuppressive agents and their sites of  action T-cell activation pathway Site of  action of  common immunosuppressive drugs Modified  diagram from  Hammond E.8 Solid Organ Transplantation Pathology. 1994. p. 218. 1.1.4 a Cyclosporine (Sandimmune®, Neoral®) Cyclosporine is a lipid-soluble cyclic polypeptide that originates from  the fungus Tolypocladium  inflatum  Gams. In fact,  the cyclosporine family  consists of  many compounds, and cyclosporine A is one that exhibits inhibitory action against immune response9. Cyclosporine works by selectively inhibiting the synthesis of  IL-2 from  T-cells by binding to the protein cyclophilin in the cells. The CSA-protein complex then binds to, and as a result, inactivates calcineurin, which is an enzyme critical in cytokine gene transcription in lymphocytes. Production of  interleukins, especially IL-2, is hindered and this subsequently inhibits proliferation  of  lymphocytes1'9. Cyclosporine is formulated  in liquid and gel c aps for  oral administration, the first formulation  being Sandimmune®. Sandimmune® has very variable oral bioavailability (10% - 90%) and unpredictable absorption rate1. A new and improved microemulsion formulation of  CSA, Neoral®, was later developed and has a more consistent pharmacokinetic profile, with a bioavailability of  30% - 50%9. Cyclosporine is mainly absorbed in the upper intestine with a zero-order absorption rate and is subject to presystemic metabolism by cytochrome P450 (CYP) 3A4 enzymes in the intestine1'9. It is also a substrate of  the efflux protein P-glycoprotein. The maximum concentration of  CSA is reached at around 1.5 hours after  oral administration of  Neoral®. It is highly bound (90 - 98%) to high-density lipoproteins, low-density lipoproteins, and erythrocytes, and is extensively distributed into tissues, giving a steady-state volume of  distribution of  3 to 5 L/Kg after  an intravenous 1 7 dose . Generally, a biphasic elimination profile  is observed for  CSA. The drug is metabolized by hepatic CYP3A4 enzymes into 30 or more metabolites, and both parent drug and metabolites are mainly excreted via the biliary route17. Since CYP3A4 enzymes are involved in metabolism of  many other drugs, there is great potential for  drug-drug interactions of  cyclosporine with other medications at the metabolism stage. The most serious adverse effects  of  cyclosporine are nephrotoxicity, hepatotoxicity, and malignancy. Other adverse effects  include hypertension, gastrointestinal (GI) toxicity such as nausea and vomiting, and hyperlipidemia9. 1.1.4 b Tacrolimus (Prograf  ®) Tacrolimus (TAC) is a macrolide antibiotic compound found  in the fungus Streptomyces  tsekubaensis.  It has a similar mechanism of  action to CSA, and is classified with CSA as a calcineurin inhibitor. TAC inhibits transcription of  cytokines such as IL-2 and t umor n ecrosis factor  a lpha i n T -lymphocytes b y b inding t o i ntracellular F K-binding proteins. This TAC-FK-binding protein complex then binds and inhibits calcineurin, which is essential in subsequent cell signaling to initiate synthesis of  cytokines1'9'13. TAC is found to be 10 to 100 times more potent than CSA in vitro, but in vivo data are lacking13. Like CSA, tacrolimus is available in intravenous and oral (0.5, 1, 5 mg) formulations.  Absorption of  TAC after  oral administration is incomplete and variable, with an oral bioavailability of  20-25%'; the extent of  absorption differs  greatly among patients with a variability of  5-67%9. Food also impairs absorption of  TAC1,18. Tacrolimus is highly bound (-99%) to high-density lipoproteins, low-density lipoproteins, alpha-l-acid-glycoproteins, and erythrocytes19. In fact,  TAC concentration measured in whole blood may be 15-30 times higher than in plasma1'18. TAC is extensively metabolized by CYP3A4 enzymes to multiple (>15) metabolites in the liver and GI tract. The metabolites are excreted mainly in bile and feces  (>92%), and only trace amount of  unchanged drug is detected in bile and feces 1'18. TAC is susceptible to pharmacokinetic drug-drug interactions through the CYP system, and inducers and inhibitors of  CYP3A4 may affect  its metabolism1. Recent studies suggest that TAC may also be a substrate of  UDP-glucuronosyltransferases  (UGTs), the enzymes responsible for  MPA metabolism (section 20 21 1.2.8). Therefore,  TAC may interfere  with MPA metabolism ' . Common adverse effects  associated with TAC include nephrotoxicity and neurologic toxicity such as headache and tremor. In addition, GI disturbances, hyperglycemia and hypertension are also observed1'9. 1.1.4c Corticosteroids Corticosteroids such as prednisone and methylprednisolone have played a major role in the management of  the immune response. These non-specific  anti-inflammatory  agents have been used in immunotherapy since the early 1960s for  the prevention and treatment of * *113 rejection ' . They inhibit the gene transcription of  cytokines, including IL-1, IL-2 and IL-6, which are signaling molecules that mediate lymphocyte proliferation.  Corticosteroids bind to glucocorticoid receptors in the cytoplasm, which then translocate to the cell nucleus and bind to deoxyribonucleic acid (DNA) to regulate gene expression. In lymphocytes, corticosteroids repress transcription of  cytokines, thus reducing T-lymphocyte production1'4'13. Prednisone, prednisolone and methylprednisolone are the most commonly used corticosteroids in organ transplant immunosuppression. Prednisone and methylprednisolone are metabolized to prednisolone, the active metabolite. Prednisone is administered orally and is well-absorbed, while methylprednisolone is administered intravenously13. These corticosteroids are highly protein-bound (about 70-90%), and are rapidly metabolized by CYP3A4 enzymes. The elimination half-life  of  prednisone and methylprednisolone is 2.6 -3 hours; however, once-daily dosage is sufficient  for  the immunosuppressive effect.  Most of the metabolites are excreted in the urine. Hyperlipidemia, glucose intolerance and hypertension are common adverse effects  of corticosteroids. Since corticosteroids act systemically, side effects  are numerous, especially with chronic use. These undesirable effects  include weight gain, osteoporosis, glaucoma, and water retention, and may exacerbate with comorbidities1'4. 1.2 Mycophenolate Mofeti l 1.2.1 Overview Mycophenolate mofetil  (MMF) is a fairly  new immunosuppressive agent that is now commonly used in combination with prednisone and CSA or TAC in maintenance immunosuppression1'7'23. MMF is a prodrug, a morpholinoethyl ester of  the active metabolite mycophenolic acid (MPA) that is responsible for  the immunosuppressive • 8 24 actions ' . MPA functions  to inhibit proliferation  of  T-cells by selectively, reversibly and non-competitively inhibiting the enzyme inosine monophosphate dehydrogenase type 2 (IMPDH-2)21'25. Proliferation  of  lymphocytes relies heavily on the de  novo pathway to synthesize purines for  DNA and cell replication, and IMPDH is a key enzyme in the process to convert inosine to guanosine. In addition, MPA also inhibits B-lymphocytes from producing antibodies that would otherwise attack allograft  antigens24. MMF is marketed under the brand name Cellcept®, and is available in capsules, tablets, oral suspensions, and intravenous solutions (mycophenolate mofetil hydrochloride)24. Recently, an enteric-coated mycophenolate sodium formulation  was developed by Novartis Pharmaceuticals Corporation; approval by regulatory agencies in the United States and Canada is pending26. 1.2.2 History MMF was developed by Nelson, Allison and Eugui of  Roche Laboratories (previously Syntex Research), who recognized a need for  new immunosuppressive drugs that have selective and reversible antiproliferative  effects 9'25'27. The investigation was sparked by observation of  children with hereditary disorders of  purine metabolism. The purines, guanosine and adenosine, are synthesized via two pathways: the de  novo pathway and the salvage pathway. It was noted that children with a deficiency  of  adenosine deaminase, an enzyme involved in the de  novo pathway of  purine synthesis, suffer  from immunodeficiency  but not neurological impairment. Conversely, children with Lesch-Nyhan syndrome who h ave d eficient  h ypoxanthine guanine phosphoribosyltransferase,  an enzyme in the salvage pathway, demonstrate neurological impairment but normal immune 25 • • response . This observation led to the postulation that lymphocytes depend heavily on the de  novo pathway of  purine synthesis, but not the salvage pathway. The search for  new immunosuppressive agents was then focused  on inhibition of  the de  novo purine synthesis pathway. MPA was subsequently identified 25'27. MPA is a fermentation  product from several Penicillium  species found  in corn mold, and was first  isolated in 1898. However, the immunosuppressive p roperties o f  M PA w ere n ot d iscovered u ntil t he 1 970s26. T he e ster derivative of  MPA, i.e. MMF, was found  to have better bioavailability than MPA due to better solubility28. 1.2.3 Physical-Chemical Properties The chemical name of  MMF is 2-morpholinoethyl-(E)-6-(l,3-dihydro-4-hydroxy-6-methoxy-7-methyl-3-oxo-5-isobenxofuranyl)-4-methyl-4-hexanolate.  It has a molecular weight of  433.5, and an empirical formula  of  C23H31NO7. It is a white to off-white crystalline powder24. The chemical structure of  MMF is presented in Figure 1-2. 1.2.4 Pharmacology and Mechanism of  Action Mycophenolate mofetil  suppresses the immune response by inhibiting proliferation of  T- and B-lymphocytes. It is a selective, reversible and non-competitive inhibitor of IMPDH-2, an enzyme crucial in guanosine synthesis. MPA is not a purine analogue, and does not compete for  the active site of  IMPDH27. Nucleotides are essential in cell proliferation,  as they are the building blocks of DNA, the genetic material in cell nuclei. Synthesis of  nucleotides is therefore  the first  step involved in an immune response. Guanosine and adenosine are synthesized via the de  novo pathway and the salvage pathway. In the salvage pathway, previously synthesized purine bases are recycled to manufacture  new nucleotides. This pathway does not utilize the enzyme IMPDH. In the de  novo pathway, purines are synthesized from  the precursor ribose 5-phosphate. An intermediate compound, inosine monophosphate, is then converted to guanosine monophosphate and adenosine monophosphate by the enzymes IMPDH and adenosine deaminase, respectively. There are two isoforms  of  IMPDH, types 1 and 2, and 9 23 25 26 type 2 is more abundant in proliferating  lymphocytes ' ' ' . MMF is a potent and selective immunosuppressive agent because the T- and B-lymphocytes depend heavily on the de  novo pathway for  purine synthesis, while other cells can utilize the salvage pathway. In addition, IMPDH-2 is five  times more susceptible to inhibition by MPA than type 1 is; synthesis of  guanosine is therefore  effectively  hindered in lymphocytes when IMPDH is inhibited by MPA. Furthermore, guanosine nucleotides provide positive feedback  to the de  novo purine synthesis pathway. Inhibiting guanosine production also indirectly decreases adenosine synthesis. The supply of  guanosine and adenosine is gradually depleted, and thus proliferation  of  lymphocytes is hindered4'25. MMF is indicated for  prophylaxis of  organ rejection in induction and maintenance immunosuppressive therapy for  renal, cardiac, lung and hepatic transplant recipients. MMF is usually used in conjunction with corticosteroids and a calcineurin inhibitor. MMF is typically dosed twice daily, with dosages ranging from  1.5 g to 3 g per day24. Figure 1-2. Chemical structures of  MMF, MPA, MPAG and AcMPAG OH C% o \ - ^ CHb CH, o o„ o MMF o CH3 of  j j S CH 3 MPA hiO^  C^OCH COOH O CH„ CH, 7-O-MPA-glucuroriide (MPAG) MPAG AC YL GLUCURONIDE (AcMPAG) AcMPAG 1.2.5 a Absorption Mycophenolate mofetil  is almost completely absorbed when given orally, and has a bioavailability of  >90%''8 '24. Absorption mainly occurs in the stomach and upper GI tract26. MMF rapidly undergoes presystemic conversion to the active compound MPA by esterases n 25 in the plasma, liver and kidney ' . Food does not affect  total absorption [measured by the area under the concentration-time curve (AUC)], but the maximum concentration (Cmax) is decreased by 40% when MMF is taken with food 24. Re-absorption of  MPA occurs at 6-12 hours post-dose as a result of  enterohepatic re-circulation of  the main metabolite, mycophenolic acid glucuronide (MPAG)9'25. 1.2.5 b Distribution Both MPA and MPAG are highly bound to albumin, about 97% and 82%, respectively24. The apparent volume of  distribution of  MPA is about 4 L/Kg in healthy volunteers24. It has been shown that salicylates increase the free  fraction  of  MPA, but other highly protein-bound drugs such as CSA, TAC, prednisone and warfarin  do not9 '24. 1.2.5 c Metabolism Biotransformation  from  MMF to MPA occurs in the GI tract within 5 minutes after ingestion4'24. Over 90% of  MPA is metabolized in the liver, GI tract and kidney by UGTs to the inactive metabolite MPAG (Figure 1-2), which is then excreted into urine and bile. MPAG is also subject to enterohepatic re-circulation, subsequent deglucuronidation by (3-glucuronidase and r eabsorption a s M PA, w hich often  r esults i n a s econd p eak i n t he P K profile  of  MPA at about 6 to 12 hours post-dose4'29. This re-circulation contributes considerably to the total exposure of  MPA; the AUC of  MPA has been shown to decrease by 40% when MMF is co-administered with cholestyramine, a bile acid sequestrant that binds to bile acids (in this case MPA) and prevents reabsorption into the gut4 '24. The apparent half-life  of  MPA is 16-18 hours4 '24 '25. Apart from  MPAG, other metabolites of  MPA h ave been identified,  including the "J A acyl mycophenolic acid glucuronide (AcMPAG, Figure 1-2) and the phenolic glucoside . AcMPAG is pharmacologically active and has shown proinflammatory  activities in vitro. It has been suggested that AcMPAG, by inducing the release of  various cytokines such as IL-6 and tumor necrosis factor  alpha, may be the culprit for  some of  MPA's adverse reactions, 31 35 such as leucopenia and GI toxicity 1.2.5 d Excretion Essentially the entire MMF dose is excreted in the urine (93%) and feces  (6%)24. MPAG is comprised of  87% of  the excreted dose, and almost all the MPAG (>90%) is found in urine and feces,  with less than 1% of  the dose excreted as unchanged MPA4. 1.2.6 Toxicity Mycophenolate is widely used now because it is effective  and is relatively specific  in its inhibitory action. It is generally well tolerated and has a much lower incidence of nephrotoxicity, neurotoxicity and hepatotoxicity than other immunosuppressive agents36. Major adverse effects  of  MMF are gastrointestinal and hematologic. Common adverse reactions attributed to MMF include GI bleeding, ulcers, diarrhea, vomiting, and leucopenia4'24. The recent development of  enteric-coated mycophenolate sodium aims to alleviate the side effects  in the upper GI tract caused by MPA, as it is absorbed in the small intestine instead of  the stomach26. 1.2.7 Therapeutic Drug Monitoring of  MPA When MMF was first  approved in the United States and Canada, there were no guidelines r egarding m onitoring o f  M PA c oncentrations, a nd t herapeutic d rug m onitoring (TDM) was not deemed necessary by the manufacturer 21'24. Unlike other immunosuppressive agents such as CSA and TAC, MMF typically is given at fixed  doses twice daily. Although there are still controversial study results as to whether TDM of  MPA is beneficial,  increasing evidence shows that MPA exposure correlates with treatment response and toxicities37""41. For example, a statistically significant  relationship between the AUC of  MPA and acute rejection was established by van Gelder et al., and trough concentration of  MPA was also correlated with acute rejection42. Shaw et al. reviewed the TDM of  MPA in clinical transplantation and also concluded that the AUC and trough concentration of  MPA correlate with acute rejection and hematological toxicities43. A therapeutic range of  30 - 60 ug*h/mL for  MPA AUC and >1 ug/mL for  trough (CO or Ctrough) was suggested. Although these guidelines were proposed for  the general transplant population, they were established from  renal transplant reciepients42'44'45. Many studies have also shown that there is wide inter-patient variability in the PK parameters of  MPA38 '46 ~5 0 . For example, Cattaneo et al.51 reported highly variable MPA AUC in renal transplant recipients receiving a fixed  dose of  MMF even after  dose normalization. The AUC ranged from  10.1 - 99.8 ^g*h/mL, and trough concentration (CO) from  0.24-7.04 ug/mL51. In another study of  renal transplant patients, Pillans et al. reported an AUC range of  18.5-54.1 Hg*h/mL38. Other similar results were observed by Bullingham et al. and Shaw et al31 '52. In our previous studies in lung and heart transplant recipients, we also observed large variation in MPA PK parameters. The dose-normalized (DN) MPA AUC in lung transplant recipients ranged from  5.5 - 51.2 |ig*h/mL, and Cmin from  undetectable to 4.8 ng/mL49 '50. To date, MPA P K s tudies i n t he 1 ung t ransplant p opulation a re s carce; M PA P Ks a re t hus p oorly characterized in this patient group. In recent years, there has been growing concern that MPA pharmacokinetics can be altered by other immunosuppressive agents, especially CSA and TAC, since co-administration o f  t hese drugs i s c ommonly e mployed i n m aintenance i mmunosuppressive therapy. A study by Zucker et al. in 199751 reported that renal transplant recipients taking TAC with MMF have significantly  higher MPA exposure (higher AUC and higher CO) than those taking MMF with CSA, despite the same administered dosage of  MMF. Other studies conducted by different  research groups found  similar results54-55. These pharmacokinetic studies suggest that CSA decreases MPA exposure, and TAC possibly increases MPA levels. However, the mechanism and degree of  effect  remain controversial. Studies are often  difficult  to compare due to different  cohorts, small numbers of  subjects, presence of other medications and different  experimental methods. Nonetheless, it is important to consider such interactions when interpreting MPA pharmacokinetic parameters. There are numerous factors  contributing to the variability in MPA pharmacokinetics, including concomitant medications (such as CSA and TAC), time since transplant, kidney function  and UGT activity. Other factors  such as gender, age and ethnicity may also play a role41. Pharmacokinetic (PK) profiling  of  MPA is therefore  a potential tool for  determining drug exposure and predicting treatment response. 1.2.8 UDP-Glucuronosyltransferases 1.2.8 a Activities UGTs are a class of  phase II metabolizing enzymes. Phase II metabolic reactions generally involve conjugation of  hydrophilic molecules to lipophilic compounds so as to increase their polarity for  elimination in bile or urine. Common phase II reactions include sulfation,  acetylation, methylation, glucuronidation, and conjugation with amino acids. UGTs metabolize a wide range of  endogenous and exogenous compounds, including androgens, estrogens, bilirubin, morphine, acetaminophen, salicylate, and mycophenolate56. In fact,  approximately 35% of  drugs that are metabolized by phase II reactions are metabolized by UGTs57. UGTs not only detoxify  xenobiotics for  elimination, but can also activate compounds via glucuronidation. For example, morphine-6-O-glucuronide is not only pharmacologically active, but is also 50 times more potent than the parent compound morphine56'57. UGTs are mainly found  in the liver, kidney and GI tract; however, they are also expressed in other tissues such as the brain, uterus and breast57. The UGT enzymes transfer  the glucuronic acid group from  uridine diphosphoglucuronic acid to the target compound to form  a glucuronide derivative, and by doing so, increase the polarity of  the target compound for  excretion in bile and/or urine57. The conjugation usually occurs at a functional  group with oxygen, nitrogen, and sulphur56. For some carboxylic acids, such as acetylsalicylic acid and mycophenolic acid, glucuronidation by UGTs also forms  acyl glucuronides. These metabolites may be reactive due to the acyl group, and are potentially toxic56. 1.2.8 b Classification To date, at least 16 functional  proteins of  UGT have been identified  in humans, and they are encoded by two gene families:  UGTl and UGT2. These are further  classified  into three subfamilies:  UGT1A, UGT2A and UGT2B. Please see Table 1-1 for  a summary of UGT isoenzymes. The entire UGTl family  is encoded by a single gene locus on chromosome 2 (2q37), and consists of  nine isoenzymes: UGT1A1, UGTl A3 - 1A10. The 2q37 locus contains 13 first  exons, each having their own proximal promoters, whose transcripts are separately spliced to 4 downstream common exons to yield messenger ribonucleic acids encoding UGT1A1 to UGT1A13 with unique amino-terminal domains responsible for  substrate specificity.  The UGTl A isoenzymes are found  in liver and extrahepatic tissues (kidney, GI tract)56'57. Two isoenzymes (UGT2A1 and UGT2A2) are catergorized in the UGT2A subfamily.  The rest of  the isoenzymes (UGT2B7, 2B11, 2B28, 2B10, 2B15, 2B17, 2B4) are in the UGT2B subfamily 56'57. The UGT2B subfamily  members contain a unique set of  6 exons, and 7 proteins have been characterized. UGTs share a high degree of  sequence homology, sometimes reaching 97% between isoforms,  and often  showing overlapping substrate specificity  but distinct expression patterns. However, limited information  is available in the literature on the relative expression levels of  UGTs in different  organs57. Recent studies show that MPA is glucuronidated to MPAG primarily by the UGTl A subfamily,  particularly UGT1A1, 1A7, 1A8, 1A9 and 1A1060 '64 '65. Among these enzymes, UGT1A9 and UGT1A8 have the most significant  activity, and are mainly expressed in the liver and extrahepatic tissues, respectively. r 'able 1-1. Summary of  UGT isoenzymes UGT family UGT Subfamily Isoenzymes Major site of  location61 MPA conjugation • • 59 activity 1A1 Liver, GI tract Yes 1A3 Liver, biliary tissue No 1A4 Liver, biliary tissue No 1A5 N/A No UGTl 1A 1A6 Liver, biliary tissue No 1A7 Esophagus, stomach Yes 1A8 1A9 Kidney, GI tract Liver Yes, High Yes, High 1A10 GI tract Yes 2A 2A1 Brain, fetal  lung Not tested 2A2 GI tract Not tested 2B4 Adipose tissue, breast No 2B7 Brain, GI tract No UGT2 2B 2B10 2B11 2B15 2B17 Breast, esophagus, kidney Adipose, breast, kidney Adipose, breast, esophagus Adrenals, breast, kidney No No No No 2B28 Breast, liver No UGT = UDP-glucuronosyltransferase;  GI = gastrointestinal; N/A = not available 1.2.8 c Polymorphisms Polymorphisms in the UGT genes have been observed, and these polymorphisms have 1 ed t o a Iteration i n e nzyme a ctivities t hat are c linically s ignificant.  For example, a TATA box mutation in the UGT1A1 gene leads to decreased enzyme expression, thereby reducing bilirubin metabolism and causing hyperbilirubinemia62. The TATA box is in the promoter region, and increasing repeats of  TA have been shown to reduce transcription in UGT1A1. This has a direct effect  on the rate of  glucuronidation of  SN-38, the metabolite of the anti-cancer agent, irinotecan. The low promoter activity allele (UGT1A1*28) is correlated with severe toxicity63. Different  alleles in UGT1A6 and 1A7 have also been discovered, and are associated with decreased benzo(a)pyrene phenol metabolism, which protects the body from  harmful  phenol toxins62. Of  interest, polymorphisms in UGT1A8 and 1A9, which metabolize MPA, have been identified  as research in UGTs blooms59 '64 '65. All of  these isozymes are potential targets for  pharmacogenetic research. Indeed, research is much needed to fully  understand the clinical effects  of  these polymorphisms, as in the case of  MPA metabolism. 1.3 Limited Sampling Strategies The AUC is generally agreed to be the best parameter that characterizes total drug exposure. Since many blood samples (often  more than 8) over the dosing period are required to determine AUC, it is not practical in a clinical setting due to high cost and inconvenience. Although the Ctrough or CO is sometimes used to indicate drug exposure, it is a poor predictor of  AUC and treatment outcomes of  MPA. A limited sampling strategy (LSS), which allows prediction of  PK parameters from  just a few  blood samples, is therefore a useful  tool for  clinical assessment of  drug exposure. The benefits  of  LSSs include reduced cost, labor and inconvenience, potentially shorter hospital stay for  patients, and faster turnaround time for  results. In addition to TDM for  individualization of  therapy, LSSs are often  applied to other clinical and research settings, such as in bioequivalence studies. Dose individualization becomes important when medications with a narrow therapeutic index, such as immunosuppressive agents, are used. For example, TDM may help diminish both short- and long-term side effects  of  MMF66 and a correlation between mycophenolic acid AUC and rejection has been reported67. In addition, LSSs for cyclosporine have already been reviewed extensively, and the concentration taken at hour 2 post-dose (C2) and the truncated AUC from  0 to 4 hours post-dose have been shown to provide accurate and precise estimation of  12-hour AUC68 ~73 . LSSs for  other drugs, such as busulfan 74, etoposide75'76, and vancomycin77'78 have also been developed and used to improve patient care. Limited sampling strategies of  MPA have been suggested by several groups66 '67 '47 '79 — 8 2 . H owever, a 111 hese s tudies w ere conducted i n t he k idney t ransplant p opulation, a nd some suggested sampling times are not clinically convenient (e.g., beyond two hours post-dose). To date, there is no LSS established for  the lung transplant population, even though about 50% of  the lung transplant population has MMF as part of  their immunosuppressive therapy14. An LSS established in a specific  population is not always suitable for  other populations, especially when the transplanted organs have different  impact on drug metabolism. The purpose of  this study was to establish clinically convenient LSSs for  the lung transplant population which utilize blood samples within two hours post-dose. 1.3.1 Methods in Establishing Limited Sampling Strategies There are two main approaches to developing LSSs for  prediction of pharmacokinetic parameters: multiple regression analysis (MRA) and Bayesian 83 84 85 analysis ' ' . In general, full  PK profile  data (with multiple samples) from  study subjects are obtained and divided into two groups, the index set and the validation set. The data from the index set are used to obtain limited sampling model parameters, and the validation set is used to test and assess precision and bias of  the developed LSS model. 1.3.1 a Multiple Regression Analysis Multiple regression analysis involves correlating the dependent variable (usually AUC) to the independent variables (concentrations at different  time points) via stepwise regression analysis. This determines the relationship between AUC and the various timed concentrations. The relationship is described as a function  in the form  of: AUC = b + M,C„ + M2Q2 + M3Q3 +... MjCu where AUC is the estimated area under the concentration-time curve; b is a constant (y-intercept); Cti, Ct2...Cti are concentrations obtained at times t,, t2...tj; and M], M2...Mj are fitted  constants associated with each timed concentration83'84'85. Equations with a high coefficient  of  determination (r2) would be logical candidates for  an LSS, and are subject to testing using a validation dataset to assess the precision and bias of  the prediction of  AUC. For MRA to be useful,  models should be limited to a maximum of  three, and ideally convenient (e.g., within 2 hours post-dose), timed concentrations69'86. There are several advantages of  MRA that make it a popular method in LSS 85 development . F irstly, MRA i s s imple a nd e asy to d evelop a nd u se. It r equires o nly a simple statistical program to perform  the regression analysis; and once the equation is obtained, AUC can be estimated by straightforward  calculations that can even be done manually. Secondly, MRA is not dependent on the PK model of  the drug, and thus does not require knowledge or assumptions regarding the pharmacokinetics of  the compound. Thirdly, it can be easily incorporated into the routine clinical setting, where health care personnel can utilize this tool without extensive training in the concept of  MRA. 83 84 85 Conversely, there are certain limitations to MRA that should not be ignored ' ' . Since MRA depends on timed concentrations for  prediction of  AUC, sampling time is critical. Deviation from  the target sampling times may significantly  affect  the accuracy of AUC prediction, especially in models utilizing early post-dose sampling times, due to rapid concentration change during the absorption and distribution phases. Also, the application of the LSS is restricted to the dosage regimen and patient population that was used in the MRA. Unless different  dosages were considered and incorporated into the equation, the predictive power of  the LSS cannot be guaranteed for  different  drug regimens (where drug-drug interactions may be an issue) or patient subpopulations. In addition, depending on how homogeneous the index group is, the LSS model may not be useful  in people with abnormal pharmacokinetics (i.e., outliers). Finally, it is imperative that the LSS is properly 87 validated , as described m section 1.3.2 below. 1.3.1 b Bayesian Analysis The Bayesian approach, or maximum a posteriori  Bayesian method, applies Bayes' theorem t o p redict i ndividual P K p arameters83'85. In b rief,  m aximum a posteriori  u ses a priori population PK parameters, or priors, for  estimation of  individual PK parameters initially. After  demographic and concentration data from  the patient are obtained, they are incorporated into the system to refine  the prediction for  the specific  individual. The Bayesian approach utilizes both typical population data and individual data, and also considers the variability of  the PK parameters in the population. As the amount of individual data accumulates, the population data contribute less to the overall prediction, and parameter prediction is individualized eventually. Prediction of  parameters is achieved by minimizing the Bayesian function: ( • P P o P - p > 2 ( Q , . - Q 2 var(i>) ^ var(C) where Ppop is the population average of  parameter P; P is the individual expected average of  parameter P; var(P) is the variance of  the estimated parameter P; C0bS is the observed concentration value; C is the predicted concentration value; and var(C) is the 85 variance of  the predicted concentration . To establish a limited sampling strategy using the Bayesian approach, both population and individual data are needed. Population PK parameters for  commonly used drugs are available in popular Bayesian software  programs (e.g., NONMEM, ADAPT II, PKS); however, if  such information  is unavailable, the index dataset can be used to determine the a priori parameters. Selected timed concentrations (one or more) from  the validation dataset are then entered as individual data to predict desirable parameters (e.g., AUC, Cmax, etc.). Again, validation of  the model is an essential step and is discussed in further  detail in section 1.3.2 below. 69 83 The Bayesian-derived LSS has a number of  advantages that prompts its success ' . First, there is flexibility  in the sampling time where collection of  samples at specific  times is not necessary. This accommodates clinical constraints in that precise sampling is not always possible. Second, the LSS can be continuously updated by incorporating new data to the population parameters, thus refining  parameter prediction and improving performance. Additional factors  such as lifestyle,  weight, age and co-medication can also be included in Bayesian Function = V the analysis. Third, Bayesian forecasting  allows prediction of  several PK parameters simultaneously (e.g., AUC, clearance, volume of  distribution, etc.). Finally, the Bayesian method is included in many popular PK software  programs that allow visual comparison of predicted concentrations with patients' PK profiles.  Simulation of  PK response and dose calculation can be readily performed. The Bayesian approach to LSSs is not without limitations68'85. In order to perform Bayesian analysis, which entails complicated calculations and algorithms, a computer with a specialized software  program for  such analysis is required. Users also require training in operating the programs and interpreting results. The cost for  such set-up is considerable. In addition, Bayesian analysis involves tedious data entry, and may not be efficient  in the routine clinical setting. Moreover, a PK model needs to be specified  for  the drug (e.g., 1-compartment, 2-compartment, etc.) and prediction of  parameters is dependent on how well the chosen model describes the pharmacokinetics of  the drug. This may be problematic in assessing the LSS when the pharmacokinetic properties of  the drug are not known, or when there is more than one candidate PK model. Finally, Bayesian analysis requires population PK parameters (such as bioavailability, absorption rate constant, clearance, etc.), or estimation of  priors, for  the initial prediction. This information  may not always be available. Although the prior population estimates can be obtained from  the index dataset, the uncertainty of  the prediction depends heavily on these priors, and errors could be large if  the index set is small. 1.3.2 Validation of  Predictive Performance  of  LSS It i s e ssential t hat t he d eveloped LSS, r egardless o f  t he m ethod u sed, i s v alidated properly to ensure reliable predictions. In general, the developed LSS is tested on a separate set o f  p atient d ata, t he validation o r t esting g roup. T he p redicted p arameter v alue, o ften AUC, is then compared to the observed value. There are no set rules regarding evaluation of the prediction; however, there are widely accepted guidelines suggested by Sheiner and 87 Beal for  this purpose. There are two main criteria for  the assessment of  prediction: bias and precision. Bias is the systematic error, the tendency to consistently over- or under-estimate the parameter. Precision is the random error, and reflects  the magnitude of  variation in the prediction. 87 Sheiner and Beal suggest that absolute bias can be measured by the mean prediction error (ME), and absolute precision is measured by the root mean squared prediction error (RMSE): Equation 1: Equation 2: An alternate way to estimate precision is to calculate mean absolute error: Equation 3: MAE = — Y\Pe, N ' where Pe = prediction error = predicted value - actual value N = number of  data points The relative bias and precision can be easily calculated by converting ME and RMSE into percentages. The lower the ME and RMSE, the more accurate and precise the predictions are. A common acceptable range of  relative ME and RMSE values in clinical studies is 15 - 20%68. In addition, confidence  intervals can be calculated for  the ME and RMSE. During LSS development using the MRA method, it is apparent that the correlation coefficient  (r) or coefficient  of  determination (r2) is an important indicator that shows how well the predicted values correlate with observed values. However, r or r2 shows only association, and provides no information  regarding bias or precision of  the prediction. The predicted values may be consistently three times higher than the actual values, for  example, but still have excellent correlation. Thus, studies that validate the LSS using only r or r2 should be evaluated with caution. 1.4 Significance  of  Research Although success rates of  solid organ transplants have improved over the years, the main hurdle of  graft  rejection remains. Acute rejection (leading to chronic rejection) and infection  are the two main causes of  post-transplant morbidity and mortality. The use of immunosuppressive agents is crucial in organ transplantation, yet the unpredictable drug response presents a great challenge in optimizing immunosuppressive therapy. The large inter-patient variability in the PKs of  MPA prompts the TDM of  MPA; however, determining AUC for  each patient is not clinically feasible.  To date, there are few  published studies that investigate the PKs of  MPA (and none that study its metabolites) in lung transplant recipients. The PK and LSS results from  this study will add valuable knowledge to lung transplantation management, and can be directly incorporated into patient care. These results bring us closer to the ultimate goal - to understand and predict such PK variability in order to optimize immunosuppressive therapy for  each individual. 1.5 Hypotheses In this research study, two hypotheses were tested: 1. There is large variability in the pharmacokinetic parameters of  mycophenolic acid and its glucuronidated metabolites in lung transplant recipients. 2. A limited sampling strategy for  MPA provides an accurate and precise estimation of  area under the concentration-time curve in lung transplant recipients. 1.6 Specific  Aims This study addressed the following  specific  aims: 1. To characterize the inter-patient variability in MPA pharmacokinetics in lung transplant recipients. This was achieved by serial blood sampling over the mycophenolate mofetil  dosing period at steady state. The pharmacokinetic parameters and AUC ratios of  MPAG/MPA and AcMPAG/MPA were determined (where MPAG = 7-O-mycophenolic acid glucuronide and AcMPAG = acyl glucuronide conjugate of  mycophenolic acid) 2. To develop a limited sampling strategy for  estimation of  MPA AUC in lung transplant recipients. 1.7 Reference s 1. Cupples SA, Ohler L, editors. Solid organ transplantation. 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Curr Opin Investig Drugs. 2004; 5: 489-98. 16. Chan A and Allen R. Bronchiolitis obliterans: an update. Curr Opin Pulm Med. 2004; 10: 133-41. 17. Dunn CJ, Wagstaff  AJ, Perry CM, Plosker GL, Goa KL Cyclosporin, an updated review of  the pharmacokinetic properties, clinical efficacy  and tolerability of  a microemulsion-based formulation  (Neoral®) in organ transplantation. Drugs 2001; 61: 1957-2016 18. Prograf  ® (Tacrolimus) product monograph. Fujisawa Canada Inc. Feb 2004. 19. Zahir H, McCaughan G, Gleeson M, Nand RA, McLachlan AJ. Changes in tacrolimus distribution in blood and plasma protein binding following  liver transplantation. Ther Drug Monit. 2004; 26: 506-15. 20. Barten MJ, Shipkova M, Bartsch P, Dhein S, Streit F, Tarnok A, Armstrong VW, Mohr FW, Oellerich M, Gummert JF. Mycophenolic acid interaction with cyclosporine and tacrolimus in vitro and in vivo. Ther Drug Monit. 2005; 27: 123 — 31. 21. HoltDW. Monitoring mycophenolic acid. Ann Clin Biochem. 2002; 39: 173 - 83. 22. Rapamune ® (Sirolimus) product monograph. Wyeth Pharmaceuticals Inc. 2005. 23. van Gelder T, Klupp J, Barten MF, Christians U, Morris RE.. Comparison of  the effects  of  tacrolimus and cyclosporine on the pharmacokinetics of  mycophenolic acid. Ther Drug Monit 2001; 23: 119-28. 24. Cellcept ® (Mycophenolate Mofetil)  Drug monograph. Roche Laboratory Inc. 2003. 25. Srinivas T, 'Kaplan B, Meier-Kriesche H. Mycophenolate mofetil  in solid-organ transplantation. Expert Opin Pharmacother 2003; 4:2325-45. 26. Garbardi S, Tran JL and Clarkson MR. Enteric-coated mycophenolate sodium. Ann Pharmacother. 2003; 37: 1685-93. 27. Allison AC and Eugui EM. Mycophenolate mofetil  and its mechanism of  action. Immunopharmacotherapy. 2000; 47: 8 5 - 118. 28. Lee WA, Gu L, Miksztal AR, Chu N, Leung K, Nelson PH. Bioavailability improvement of  mycophenolic acid through amino ester derivatization. Pharm Res. 1990; 7: 161-6. 29. Cox VC, Ensom MHH. Mycophenolate mofetil  for  solid organ transplantation: does the evidence support the need for  clinical pharmacokinetic monitoring? Ther Drug Monit 2003; 25: 137-57 30. Shipkova M, Strassburg CP, BraunF, StreitF, Grone HJ, Armstrong VW, Tukey RH, Oellerich M, Wieland E. Glucuronide and glucoside conjugation of mycophenolic acid by human liver, kidney and intestinal microsomes. Br J Pharmacol 2001; 132: 1027-34 31. Bullingham RES, Nicholls AJ, Kamm BR. Clinical pharmacokinetics of mycophenolate mofetil.  Clin Pharmacokinet 1998; 34: 429-55. 32. Shaw LM, Korecka M, DeNofrio  D, Brayman KL. Pharmacokinetic, pharmacodynamic and outcome investigations as the basis for  mycophenolic acid therapeutic drug monitoring in renal and heart transplant patients. Clin Biochem 2001;34:17-22. 33. Wieland E, Shipkova, Shellhaas U, Schutz E, Niedmann PD, Armstrong VW, Oellerich M. Induction of  cytokine release by the acyl glucuronide of  mycophenolic acid: a link to side effects?  Clin Biochem 2000; 33: 107-13. 34. Schutz E, Shipkova M, Armstrong VW, Wieland E, Oellerich M. Identification  of  a pharmacologically active metabolite o f  mycophenolic acid in p lasma o f  transplant recipients treated with mycophenolate mofetil.  Clin Chem 1999; 45: 419-22. 35. Shipkova M, Wieland E, Schutz E, Wiese C, Niedmann PD, Oellerich M, Armstrong VW. The acyl glucuronide metabolite of  mycophenolic acid inhibits the proliferation  of  human mononuclear leukocytes. Transplant Proc 2001; 33: 1080-1. 36. Kuo PC, Schroeder RA, Johnson LB, editors. Clinical management of  the transplant patient. New York: Arnold, 2001. 37. Takahashi K, Ochiai T, Uchida K, Yasumura T, Ishibashi M, Suzuki S, Otsubo O, Isono K, Takagi H, Oka T, et al. Pilot study of  mycophenolate mofetil  (RS-61443) in the prevention of  acute rejection following  renal transplantation in Japanese patients. Transplant Proc 1995; 27: 1421-4. 38. Pillans PI, Rigby RJ, Kubler P, Willis C, Salm P, Tett SE, Taylor PJ. A retrospective analysis of  mycophenolic acid and cyclosporine concentrations with acute rejection in renal transplant recipients. Clin Biochem 2001; 34: 77-81. 39. Krumme B, Wollenbery K, Kirste G, Schollmeyer P. Drug monitoring of mycophenolic acid in the early period after  renal transplantation. Transplant Proc 1998; 30: 1773-4. 40. Mourad M, Malaise J, Chaib Eddour D, De Meyer M, Konig J, Schepers R, Squifflet JP, Wallemacq P. Correlation of  mycophenolic acid pharmacokinetic parameters with side effects  in kidney transplant patients treated with mycophenolate mofetil. Clin Chem 2001; 47: 88-94. 41. Shaw LM, Nawrocki A, Korecka M, Solari S, Kang J. Using established immunosuppressive therapy effectively:  lessons from  the measurement of mycophenolic acid plasma concentrations. Ther Drug Monit. 2004; 26(4): 347 -51. 42. van Gelder T, Hilbrands LB, Vanrenterghem Y, Weimar W, de Fijter JW, Squifflet JP, Hene RJ, Verpooten GA, Navarro MT, Hale MD, Nicholls AJ. A randomized double-blind, m ulticenter p lasma c oncentration c ontrolled s tudy f  or t he safety  a nd efficacy  of  oral mycophenolate mofetil  for  the prevention of  acute rejection after kidney transplantation. Transplantation. 1999; 68: 261 - 6 . 43. Shaw LM, Korecka M, Venkataramanan R, Goldberg L, Bloom R, Brayman KL. Mycophenolic acid pharmacodynamics and pharmacokinetics provide a basis for rational monitoring strategies. Am J Transplant. 2003; 3: 534 - 42. 44. Shaw LM, Holt DW, Oellerich MO, Meiser B, van Gelder T. Current issues in therapeutic drug monitoring of  mycophenolic acid: report of  a roundtable discussion. Ther Drug Monit. 2001; 23: 305- 15. 45. Hale MD, Nicholls AJ, Bullingham RES, Hene R, Hoitsma A, Squifflet  JP, Weimar W, Vanrenterghem Y, Van de Woude FJ, Verpooten GA. The pharmacokinetic-pharmacodynamic relationship for  mycopehnolate mofetil  in renal transplantation. Clin Pharmacol Ther. 1998; 64 : 672 - 83. 46. Wollenberg K, Krumme B, Pisarski P, Schollmeyer P, Kirste G. Pharmacokinetics of  mycophenolic acid in the early period after  kidney transplantation. T ransplant Proc 1998; 30: 4090-1. 47. Johnson AG, Rigby RJ, Taylor PJ, Jones CE, Allen J, Franzen K, Falk MC, Nicol D. The kinetics of  mycophenolic acid and its glucuronide metabolite in adult kidney transplant recipients. Clin Pharmacol Ther 1999; 66: 492-500. 48. Shum B, Duffull  SB, Taylor PJ, Tett SE. Population pharmacokinetics analysis of mycophenolic acid in renal transplant recipients following  oral administration of mycophenolate mofetil.  Br J Clin Pharmacol 2003; 56: 188-97. 49. Ensom MHH, Partovi N, Decarie D, Ignaszewski AP, Fradet GJ, Levy RD. Mycophenolate pharmacokinetics in early period following  lung or heart transplantation. Ann Pharmacother 2003; 37: 1761-7. 50. Ensom MHH, Partovi N, Decarie D, Dumont RJ, Fradet G, Levy RD. Pharmacokinetics and protein binding of  mycophenolic acid in stable lung transplant recipients. Ther Drug Monit 2002; 24: 310-4. 51. Cattaneo D, Gaspari F, Ferrari S, Stucchi N, Del Priore L, Perico N, Gotti E, Remuzzi G. Pharmacokinetics help optimizing mycophenolate mofetil  dosing in kidney transplant patients. Clin Transplant. 2001; 15: 402-9 . 52. Shaw LM, Nicholls A, Hale M. Therapeutic drug monitoring of  mycophenolate acid. A consensus panel report. Clin Biochem 1998; 31:317 - 22. 53. Zucker K, Rosen A, Tsaroucha A, de Faria L, Roth D, Ciancio G, Esquenazi V, Burke G, Tzakis A, Miller J. Unexpected augmentation of  mycophenolic acid pharmacokinetics in renal transplant patients receiving tacrolimus and mycophenolate mofetil  in combination therapy, and analogous in vitro findings. Transpl Immunol 1997; 5: 225-32. 54. PouL, Brunet M, Cantarell C, Vidal E, Oppenheimer F, Monforte  V, Vilardell J, Roman A, Martorell J, Capdevila L. Mycophenolic acid plasma concentrations: influence  of  comedication. Ther Drug Monit 2001; 23: 35-8. 55. Filler G, Lepage N, Delisle B, Mai I. Effect  of  cyclosporine on mycophenolic acid area under the concentration-time curve in pediatric kidney transplant recipients. Ther Drug Monit 2001; 23:514-9. 56. King CD, Rios GR, Green MD, Tephly TR. UDP-glucuronosyltransferases.  Curr DrugMetab. 2000; 1: 143-61. 57. Guillemette C. Pharmacogenomics of  human UDP-glucuronosyltransferase enzymes. Pharmacogenomics J. 2003; 3: 136-58. 58. Guillemette C, Ritter JK, Auyeung DJ, Kessler FK, Housman DE. Structural heterogeneity at the UDP-glucuronosyltransferase  1 locus: functional  consequences of  three novel missense mutations in the human UGT1A7 gene. Pharmacogenetics 2000; 10: 629-44. 59. Bernard 0, Guillemette C. The main role of  UGTl A9 in the hepatic metabolism of mycophenolic acid and the effects  of  naturally occurring variants. Drug Metab Dispos. 2004; 32: 775 - 8 . 60. Mojarrabi B, Mackenzie PI. The human UDP glucuronosyltransferase,  UGT1A10, glucuronidates mycophenolic acid. Biochem Biophys Res Commun. 1997; 238: 775 - 8 . 61. Desai AA, Innocenti F and Ratain MJ. UGT pharmacogenomics: implications for cancer risk and cancer therapeutics. Pharmacogenetics 2003; 13: 517-23. 62. Bock KW. Vertebrate UDP-glucuronosyltransferases:  functional  and evolutionary / aspects. Biochem Pharmacol. 2003; 66: 691 - 6 . 63. Marsh S and McLeod HL. Pharmacogenetics of  irinotecan toxicity. Pharmacogenomics. 2004; 5: 835 -43 . 64. Picard N, Ratanasavanh D, Premaud A, Le Meur Y, Marquet P. Identification  of  the UDP-glucuronosyltransferase  isoforms  involved in mycophenolic acid phase II metabolism. Drug Metab Dispos. 2005; 33:139-46. 65. Basu NK, Kole L, Kubota S, Owens IS. Human UDP-glucuronosyltransferases show atypical metabolism of  mycophenolic acid and inhibition by curcumin. Drug Metab Dispos. 2004; 32: 768-73. 66. Filler G and Mai I. Limited sampling strategy for  mycophenolic acid area under the curve. Ther Drug Monit 2000; 22:169-73. 67. Schutz E, Armstrong V W, Shipkova M, Weber L, Niedmann PD, Lammersdorf  T, Wiesel M, Mandelbaum A, Zimmerhackl LB, Mehls O, Tonshoff  B, Oellerich M. Limited sampling strategy for  the determination of  mycophenolic acid area under the curve in pediatric kidney recipients. Transplant Proc 1998; 30:1182-4. 68. David OJ and Johnston A. Limited sampling strategies for  estimating cyclosporin area under the concentration-time curve: Review of  current algorithms. Ther Drug Monit 2001; 23: 100-14. 69. Dumont R J and Ensom M H H . Methods for  clinical monitoring of  cyclosporin in transplant patients. Clin Pharmacokinet 2000; 38:427-47. 70. Monchaud C, Rousseau A, Leger F, David OJ, Debord J, Dantoine T, Marquet P. Limited sampling strategies using Bayesian estimation or multilinear regression for cyclosporin AUC(0-12) monitoring in cardiac transplant recipients over the first  year post-transplantation. Eur J Clin Pharmacol 2003; 58:813-20. 71. del Prado J M A, Rubio A, Aranzana M C, Lopez Malo de Molina MD, Segura Saint-Gerons J, Lopez Granados A, Rodriguez Esteban E, Ruiz Ortiz M, Romo Penas E, Munoz Carvajal I, Gonzalez Rodriguez JR, Segura Saint-Gerons C, Valles Belsue F, Concha Ruiz M. et al. New strategies of  cyclosporine monitoring in heart transplantation: Initial results. Transplant Proc 2003; 35:1984-7. 72. Jaiswal J, Gupta S K, Dash S C, Tiwari SC, Mehta SN, Gupta YK, Velpandian T. Neoral monitoring by limited sampling area under the concentration time curve in stable Indian renal transplant recipients. Transplant Proc 2003; 35: 1298-9. 73. Balram C, Sivathasan C, Cheung Y B, Tan SB, Tan YS. A limited sampling strategy for  the estimation of  12-hour neoral systemic drug exposure in heart transplant recipients. J Heart Lung Transplant 2002; 21:1016-21. 74. Chattergoon DS, Saunders EF, Klein J, Calderwood S, Doyle J, Freedman MH, Koren G. An improved limited sampling method for  individualised busulphan dosing in bone marrow transplantation in children. Bone Marrow Transplant. 1997; 20: 347-54. 75. Panetta JC, Wilkinson M, Pui CH, Relling MV. Limited and optimal sampling strategies for  etoposide and etoposide catechol in children with leukemia. J Pharmacokinet Pharmacodyn. 2002; 29: 171-88. 76. Tranchand B, Amsellem C, Chatelut E, Freyer G, Iliadis A, Ligneau B, Trillet-Lenoir V, Canal P, Lochon I, Ardiet CJ. A limited-sampling strategy for  estimation of  etoposide pharmacokinetics in cancer patients. Cancer Chemother Pharmacol. 1999;43:316-22. 77. Pea F, Bertolissi M, Di Silvestre A, Poz D, Giordano F, Furlanut M. TDM coupled with Bayesian forecasting  should be considered an invaluable tool for  optimizing vancomycin daily exposure in unstable critically ill patients. Int J Antimicrob Agents. 2002; 20: 326-32. 78. Andres I, Lopez R, Pou L, Pinol F, Pascual C. Vancomycin monitoring: one or two serum levels? Ther Drug Monit. 1997; 19: 614-9. 79. Le Guellec C, Bourgoin H, Buchler M, Le Meur Y, Lebranchu Y, Marquet P, Paintaud G. Population pharmacokinetics and Bayesian estimation of  mycophenolic acid c oncentrations i n s table r enal t ransplant p atients. C lin P harmacokinet. 2 004; 43: 253-66. 80.. Pawinski T, Hale M, Korecka M, Fitzsimmons WE, Shaw LM. Limited sampling strategy for  the estimation of  mycophenolic acid area under the curve in adult renal transplant patients treated with concomitant tacrolimus. Clin Chem. 2002; 48: 1497-504. 81. Weber L T, Schutz E, Lamersdorf  T, Shipkova M, Niedmann PD, Oellerich M, Zimmerhackl LB, Staskewitz A, Mehls O, Armstrong VW, Tonshoff  B. Therapeutic drug monitoring of  total and free  mycophenolic acid (MPA) and limited sampling strategy for  determination of  MPA-AUC in paediatric renal transplant recipients. Nephrol Dial Transplant. 1999; 14: 34-5. 82. Yeung S, Tong K L, Tsang W K, Tang HL, Fung KS, Chan HW, Chan AY, Chan L. Determination of  mycophenolate area under the curve by limited sampling strategy. Transplant Proc. 2001; 33: 1052 - 3. 83. Rousseau A and Marquet P. Application of  pharmacokinetic modelling to the routine Ther Drug Monit of  anticancer drugs. Fundam Clin Pharmacol 2002; 16: 253-62. 84. van W armerdam LJ, t en B okkel H uinink W W, M aes R A, B eijnen JH. Limited-sampling models for  anticancer agents. J Cancer Res Clin Oncol 1994; 120: 427-33. 85. Mahmood I and Miller R. Comparison of  the Bayesian approach and a limited sampling model for  the estimation of  AUC and C-max: a computer simulation analysis. Int J Clin Pharmacol Ther 1999; 37: 439-45. 86. Sallas W M. Development of  limited sampling strategies for  characteristics of  a pharmacokinetic profile.  Journal of  Pharmacokinetics and Biopharmaceutics 1995; 23: 515-29. 87. Sheiner L B and Beal S L. Some suggestions for  measuring predictive performance. J Pharmacokinet Biopharm. 1981; 9:503-12. Chapter 2 - Pharmacokinetics of  Mycophenolic Acid and its Glucuronidated Metabolites in Lung Transplant Recipients A version of  this chapter will  be submitted  for  publication 2.1 Specific  Aim #1 The objective of  this study was to characterize pharmacokinetic parameters and metabolic ratios of  MPA in stable lung transplant recipients. In addition, free  MPA was determined to characterize protein binding of  MPA in the study subjects. 2.2 Introduction Mycophenolate Mofetil  (MMF), marketed as Cellcept®, has become one of  the mainstay immunosuppressive agents used in kidney, liver, heart and lung transplantation to help combat allograft  rejection1-4. It is often  used in combination with cyclosporine (CSA) or tacrolimus (TAC) and/or a corticosteroid in maintenance immunosuppression. MMF is a morpholinoethyl ester of  the active metabolite mycophenolic acid (MPA), which is responsible for  the immunosuppressive activity. MPA functions  to inhibit proliferation  of T-cells by selectively, reversibly and non-competitively inhibiting the enzyme inosine monophosphate dehydrogenase type 2 (IMPDH-2)5'6'7. Proliferation  of  lymphocytes relies heavily on the de  novo pathway to synthesize purines for  DNA and cell replication, and IMPDH is a key enzyme in the process to convert inosine to guanosine. In addition, MPA also inhibits B-lymphocytes from  producing antibodies that would otherwise attack allograft antigens8. Mycophenolate mofetil  is administered orally and is rapidly and completely absorbed8'9,10. It i s t hen h ydrolyzed c ompletely to M PA i n t he g astrointestinal (GI) t ract o within five  minutes of  ingestion . MPA is highly protein-bound, about 97% bound to albumin. Over 90% of  MPA is metabolized by UDP-glucuronosyltransferase  (UGT) enzymes in the liver, GI tract and kidney to the inactive metabolite 7-O-mycophenolic acid glucuronide (MPAG). Recently other metabolites have been identified,  including an acyl glucuronide of  MPA (AcMPAG), which is pharmacologically active and has shown pro-inflammatory  activity in vitro. It was suggested that AcMPAG may be the culprit for  some adverse reactions by inducing release of  cytokines10"14. It is also known that MPA goes through enterohepatic recirculation; the MPAG released into the bile is de-glucuronidated in the GI tract by p-glucuonidases, releasing MPA for  re-absorption, which occurs between 6 -12 hours after  drug administration. Although MPA is very effective  in its immunosuppressive action, side effects  such as GI disturbances and bone marrow suppression are not uncommon4'8. Such toxicities may be caused by excessive levels of MPA and AcMPAG14. Unlike other immunosuppressive agents such as CSA and TAC, MMF is typically administered twice daily at a fixed  dose of  1 to 1.5 g. Although therapeutic drug monitoring of  MPA was deemed unnecessary initially by the manufacturer,  increasing evidence shows that MPA exposure correlates with treatment response and toxicities15'16. Pharmacokinetic (PK) profiling  of  MPA is therefore  a potential tool for  determining drug exposure and predicting treatment outcomes. Recent studies also identified  PK drug-drug interactions between MPA and CSA, such that CSA administration decreases MPA levels and exposure. Evidence also suggests that TAC augments MPA levels17 '18 '19. Most of  the PK studies were conducted in renal transplant patients, and there is large inter-patient variability in the PK parameters of  MPA1 1 '1 5 '2 0 '2 1 . There is great need to characterize and explain this significant variability since it leads to unpredictable MPA exposure, causing difficulty  in determining optimal dosing strategies. Pharmacokinetic studies in the lung transplant group are still scarce, despite the fact that long-term survival rate is much lower in this group than in renal transplantation (5-year survival rate 50% in lung versus 80 - 90% in kidney)22 '23 '24. The lung is one of  the largest organs in the body, is not involved in the metabolism of  MPA, and is constantly in contact with the external environment18. It is not surprising that lung transplant recipients experience more complications such as infections  and acute rejections. Our research group was the first  to investigate the pharmacokinetics and protein binding of  MPA in lung transplant recipients25. Although the sample size was small, the large inter-patient variability observed in the area under the concentration-time curve (AUC) and protein binding of  MPA could not be explained by known factors  (e.g., disease state, concomitant medication). This prompted the need for  a larger clinical study to investigate MPA metabolites as well. This represents the first  study investigating MPA and its metabolites in stable lung transplant recipients. 2.3.1 Patient Population This was an open-label clinical study. Participants were recruited via the Vancouver General Hospital lung transplant program and the British Columbia Transplant Society. Inclusion criteria consisted of  the following:  at least 16 years of  age; provision of  informed consent; and not taking interacting medications (e.g., antacids, cholestyramine). Study subjects were asked to fast  overnight before  reporting to the British Columbia Transplant Society clinic on the study day, but there were no restrictions on activity or food  intake during the study day. After  obtaining written informed  consent (Appendix 1, UBC Clinical Research Ethics Board #C02-0568), an indwelling intravenous catheter was placed in a forearm  vein for  serial blood collection. A total of  eleven blood samples were collected in 3-mL tubes with ethylenediaminetetraacetatic acid-anticoagulant (BD Vacutainer, K3EDTA) over a 12-hour dosing period. After  sample collection, the collection tubes were inverted several times and refrigerated  until processing in the laboratory. The catheter line was then flushed  with sodium chloride solution (U.S.P. 0.9%, Abbott Laboratory Bacteriostatic injection USP), followed  by heparin lock flush  solution (100 U.S.P. units/mL, heparin sodium injection BP, LEO Pharma Inc.). About 1 mL of  blood was collected in the same manner and discarded immediately before  the next sample collection to ensure no contamination with the heparin flush  solution. A blood sample was drawn before  the study subjects took their medication (time 0), then again at 20, 40, 60, and 90 minutes, and at 2, 4, 6, 8, 10 and 12 hours after  the MMF dose. Separation of  plasma was achieved by centrifugation  at 3000 rotations per minute for  5 minutes. Plasma was collected immediately and acidified  to pH 2 - 4 with phosphoric acid (85% solution; 10|uL per 500|j.L of  plasma) to preserve the AcMPAG, which is unstable at physiological pH (section 2.3.3). Samples were stored at -80°C until analysis for  MPA, MPAG and AcMPAG concentrations. 2.3.2 Plasma Concentrations of  MPA, MPAG and AcMPAG The concentrations of  MPA, MPAG, and AcMPAG were determined quantitatively in patient plasma samples by high performance  liquid chromatography (HPLC) with ultraviolet detection. The analytical assay published previously25'26 was modified  to include quantification  of  the metabolites. The HPLC instrumentation (Waters Alliance System, Waters Ltd., Mississauga, ON) consisted of  a delivery pump, an automatic injector equipped with a 200 (iL injector loop, a Symmetry C8 4.6mm (internal diameter) X 250 mm column and an ultraviolet detector (2487 Dual Wavelength Absorbance Detector, Waters Ltd. Mississauga, ON)se t a t210nm. An integrator w as u sed t o m easure p eak a reas. S tock solutions of  1 mg/mL of  MPA were prepared in HPLC-grade methanol. Indomethacin (1 mg/mL) in HPLC-grade methanol was used as the internal standard (IS). A stock solution containing 75 )J.g/mL of  MPA was prepared in extracted acidified  serum. Stock solutions also contained 5 |j.g/mL of  IS. All standards were stored at 4°C. The mobile phase consisted of  a gradient of  0% - 60% : 100% - 40% (v/v) acetonitrile : 0.01M phosphate buffer  pH 3.0 at a flow  rate of  2 to 2.2 mL/min. All solvents and water were HPLC-grade. The gradiant is programmed as follows: 0 to 1.7 min: 0% acetinitrile, 100% potassium phosphate 1.7 to 3.0 min: 45% acetonitrile, 55% potassium phosphate 3.0 to 7.0 min: 60% acetonitrile, 40% potassium phosphate 7.0 to 10.0 min: 0% acetonitrile, 100 % potassium phosphate Plasma samples were kept on ice for  the duration of  the extraction for  total MPA, MPAG and AcMPAG. Cold acetonitrile (1.2 mL, at -20°C) was added to 300^L of  plasma sample and vortex-mixed. The plasma-supernatant was transferred  to a clean tube and was evaporated for  15 min at 37°C under 25 psi nitrogen flow.  The samples were re-suspended in 300 (iL of  20% acetonitrile (in HPLC-grade water) containing 5 (xg/mL IS, filtered (Gelman 0.45 )o,m microfilter,  Acrodisc GHP 13, Waters Ltd.,Milford,  MA) and injected (50 |xL) into the column. The HPLC validation involved a calibration curve for  MPA with 7 solutions, which were diluted from  the stock solution and prepared in acidified  serum to obtain the following concentrations: 12.50, 6.25, 3.13, 1.56, 0.78, 0.39 and 0.20 |xg/mL for  MPA; 25.00, 12.50, 6.25, 3.13, 1.56, 0.78, 0.39 (ig/mL for  MPAG and AcMPAG. A blank (extracted acidified plasma) was included at the beginning of  each run. Calibration curves were generated by least-squares regression of  the peak areas versus concentration of  each stock solution. The retention time was 3.4 min for  MPAG, 3.9 min for  AcMPAG, and 4.6 min for  MPA, while the retention time of  the internal standard indomethacin was 6.4 minutes. An example » chromatogram of  a calibration curve standard is provided in Figure 2-1, and a chromatogram of  a blank sample is provided in Figure 2-2. Precision and reproducibility of  the assay were evaluated by running the standards of 3.13, 1.56, 0.78 |xg/mL for  MPA and 6.25, 3.13, 1.56 ng/mL for  MPAG and AcMPAG in quadruplicates daily for  4 days. Validation results are presented in Table 2-1. The assay's inter- and intra-day coefficient  of  variation ranged from  0.93 - 13.2% for  all compounds. The lower limit of  quantification  was 0.20 ng/mL for  MPA, 0.39 |xg/mL for  MPAG and AcMPAG; these were the lowest concentrations that had an inter- and intra-day variability of  <15%. The lower limit of  detection was 0.10 |ig/mL for  MPA, and 0.195 ng/mL for MPAG.and AcMPAG; these were the lowest concentrations that gave integratable peaks. Figure 2-1. HPLC chromatogram of  a calibration curve standard (prepared in extracted acidified  plasma) containing MPA, MPAG, AcMPAG and indomethacin A i B E C I J . II 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 5.50 6.00 6.50 7.00 Mnutes A: MPAG; B: AcMPAG; C: MPA; D: Indomethacin HPLC = high performance  liquid chromatography Figure 2-2. HPLC chromatogram of  a blank sample (extracted acidified  plasma) 1 . 8 0 -1 . 6 0 -1.40-1 . 2 0 -1 . 0 0 -0.80-I; 0 .60-0.40-0 . 2 0 -0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 5.50 6.00 6.50 7.00 Minutes Table 2-1. Intra-day and inter-day coefficient  of  variation (CV) of  MPA, MPAG and AcMPAG measurements at three concentrations Concentration of  standard used Intra-day CV (%)* Inter-day CV (%)* for  validation (|j.g/mL) MPA 3.13 2.29 2.18 1.56 2.63 4.79 0.78 0.93 2.16 MPAG 6.25 2.29 5.32 3.13 3.40 3.73 1.56 1.38 2.29 AcMPAG 6.25 2.22 2.31 3.13 3.11 6.09 1.56 ' 13.17 6.79 * n = 4 MPA = mycophenolic acid; MPAG = 7-O-mycophenolic acid glucuronide; AcMPAG = acyl glucuronide of  mycophenolic acid; CV = coefficient  of  variation 2.3.3 Stability of  AcMPAG Since AcMPAG was reported to be unstable under neutral and alkaline conditions, all plasma samples were acidified  with phosphoric acid27. When plasma is acidified,  the AcMPAG concentration has been shown to retain 90 - 100% of  the original value for  24 hours at room temperature, and 30 days at 4°C and -20°C27. In this study, the stability of AcMPAG stored at 4°C and -20°C was determined by analyzing an aliquot weekly. AcMPAG was found  to maintain >88% of  the original concentration for  up to 3 weeks. 2.3.4 Free MPA Extraction Free MPA samples were prepared according to previously published procedures25'28. Since the free  fraction  of  MPA is known to be low (-3%), and the fMPA  is concentration-independent, the plasma samples were spiked to ensure adequate free  MPA concentrations within the analytical range of  the HPLC assay. Two plasma samples from  each subject were pooled to obtain 800 joL of  plasma. The plasma was spiked with 12 (iL of  MPA stock solution (1 mg/mL) and mixed thoroughly. The concentration of  MPA in the spiked sample was at least 15 |xg/mL, and would result in a free  MPA concentration of  at least 0.45 ng/mL, assuming a free  fraction  of  3%. An aliquot of  300 |o,L of  this spiked plasma was reserved for total MPA concentration quantification  according to the procedure described in section 2.3.2. The remaining 500 (xL was placed in a Microcon YM-30 filter  (Amicon, Beverly, MA), and filtered  in a temperature-controlled centrifuge  (at 4°C) for  15 minutes at 1380 x g. The filtrate  was then collected, and an aliquot of  300 jxL was prepared for  analysis of  free MPA concentration as described in section 2.3.2. Free fraction  was calculated by dividing free  MPA concentration by total MPA concentration in the spiked plasma. 2.3.5 Assessment of  Pharmacokinetic Parameters Conventional pharmacokinetic parameters, including AUC ( 0 - 1 2 hrs), maximum concentration (Cmax), minimum concentration (Cmin), time when Cmax occurs (Tmax), apparent clearance (Cl/F), and apparent volume of  distribution, were calculated for  each patient by non-compartmental analysis using the PK program WinNonlin Professional version 4.1 (Pharsight, Mountain View, C A). D ose-normalized AUC and AUC ratios of MPAG/MPA and AcMPAG/MPA were also calculated. Subjects were further  stratified  into concomitant immunosuppressant (i.e., CSA and TAC) groups. 2.3.6 Statistical Analysis Descriptive statistics were used for  the demographic and pharmacokinetic data. Mean comparisons were performed  by Student's t-test for  continuous data, and Fisher's exact test for  categorical data. 2.4 Results 2.4.1 Patient Characteristics Twenty-one stable adult lung transplant recipients receiving MMF therapy (twice-daily) were investigated. Of  the 21 participants, 2 had incomplete blood sampling (less than 12 hours), and PK parameters were extrapolated from  the available data. Table 2-2 summarizes the patient characteristics. In addition to MMF and prednisone, 11 subjects were also taking CSA, while 10 subjects had TAC as part of  their immunosuppressive therapy. All but one (native American) subject were Caucasians. There were no significant differences  in patient characteristics between the CSA and TAC groups. Patient diagnoses included chronic obstructive pulmonary disease/emphysema (n=4), cystic fibrosis  (n=5), pulmonary fibrosis  (n=4), alpha 1 -antitrypsin deficiency  (n=3), fibrosing  alveolitis (n=l), sarcoidosis (n=l), lymphangioleiomyomatosis (n=l), bronchiolitis obliterans (n=l), and pulmonary hypertension (n=l). Table 2-2. Characteristics of  lung transplant recipients who participated in this study All subjects (n=21) CSA group (n=ll) TAC group (n=10) p-value* Male (%) 52.4 63.6 40.0 0.39# Age (yrs) 48.1 ± 14.2 52.3 ± 13.2 43.5 ± 14.4 0.07 (20.7-74.5) (20.7 - 69.2) (27.4-70.5) Years since 4.6 ±4.2 3.5 ±4.5 5.7 ±3.6 0.24 transplant (0.2-14.0) (0.2-14.0) (1.9-12.3) Height (cm) 168 ± 11 169 ± 10 166 ± 12 0.57 (151 - 185) (154- 180) (151 - 185) Weight (kg) 71.0± 17.5 75.3 ± 17.9 66.4 ± 16.7 0.21 (46.0- 109.1) (57.0- 109.1) (46.0- 100.0) MMF daily 2524± 536 2545 ± 522 2500± 577 0.85 dosage (mg) (1500-3000) (2000 - 3000) (1500-3000) Serum 122 ±42 134 ± 53 109 ± 17 0.13 creatinine** (70-218) (70-218) (86- 135) (nmol/L) Albumin** 38 ± 5 39 ± 4 37 ± 5 0.49 (g/L) (26 - 44) (33 - 44) (26 - 44) Results expressed as mean ± standard deviation (range) * Comparison between CSA and TAC groups, Student's t-test unless otherwise indicated # Fisher's exact test ** From patient charts CSA = cyclosporine; TAC = tacrolimus; MMF = mycophenolate mofetil 2.4.2 MPA Pharmacokinetics Table 2-3 summarizes the PK parameters of  MPA, the metabolic ratios (MPAG/MPA and AcMPAG/MPA), and the free  fraction  of  MPA (fMPA)  in the study participants. There were no statistically significant  differences  in most PK parameters of MPA between the two groups of  subjects (CSA versus TAC). However, the TAC group tended to have a higher AUC, higher DN AUC, lower apparent clearance, and lower AUC ratio of  AcMPAG/MPA than the CSA group. In addition, the CSA group had a lower Cmin and higher AUC ratio of  MPAG/MPA than the TAC group, and these differences  were statistically significant  (p<0.05). No correlation was observed between time-since-transplant and MPA PK parameters. The PK profiles  of  MPA and its glucuronidated metabolites are presented in Figures 2-3, 2-4 and 2-5 [data points are expressed as mean ± standard deviation (SD)]. There was large variability in MPA, MPAG and AcMPAG concentrations throughout the sampling period. A re-absorption peak of  MPA was observed between 6-12 hours post-dose. Concentrations of  MPAG and AcMPAG remained relatively constant throughout the sampling period. The PK profiles  were further  stratified  into CSA and TAC co-medication groups (Figures 2-3b, 2-4b, 2-5b). In general, the CSA group had lower MPA concentrations, and higher MPAG and AcMPAG concentrations than the TAC group. The differences,  albeit not statistical significant,  were more prominent during the later sampling times (>4 hours post-dose) compared with the early hours ( 0 - 4 hours post-dose). Table 2-3. PK parameters and metabolic ratios of  MPA in lung transplant recipients All subjects (n=21) CSA group (n=ll) TAC group (n=10) p-value* MMF dosage 36.9 ±9.5 35.4 ± 10.5 38.7 ±9.2 0.45 (mg/kg/day) (18.3-54.0) (18.3-49.6) (26.5 - 54.0) AUC 28.6 ± 17.3 23.45 ± 13.55 34.16 ± 19.85 0.17 (jwg*h/mL) (4.9-72.2) (0.58-4.92) (12.45-72.16) DN MPA AUC 23.4 ± 13.8 19.25 ± 11.23 28.00 ± 15.43 0.16 (Hg*h/mL/g) (3.3-57.1) (3.28-35.11) (8.30-57.1) Cmax Qig/mL) 7.84 ±4.91 8.64 ±5.96 6.97 ±3.54 0.44 (0.95-23.3) (0.95-23.29) (2.26- 11.97) Tmax (hr) 1.7 ±2.2 1.6 ± 1.6 1.84 ±2.89 0.78 (0.3-10) (0.3 - 6) (0.3-10) Cmin (|wg/mL) 0.74 ± 0.48 0.53 ±0.35 0.97 ±0.50 0.03 (0-1.84) (0-1.05) (0.26- 1.84) Vd/F (L) 302.8 ±227.7 315.4 ± 255.7 287.6 ±202.3 0.79 (54.1 -799.2) (54.1 -799.2) (55.8-607.1) Cl/F (L/hr) 68.9 ±65.7 83.8 ± 82.5 50.6 ±32.6 0.29 (17.5-304.8) (28.5-304.8) (17.5-120.5) AUC ratio 22.53 ± 12.45 29.31 ± 13.18 15.08 ± 5.82 0.006 MPAG/MPA (7.40-55.19) (0.45 - 9.42) (7.40-25.81) AUC ratio 1.45 ±3.15 2.35 ±4.18 0.45 ± 0.72 0.17 AcMPAG/MPA (0-12.64) (0- 12.64) (0.03-2.39) Free fraction  of 7.0 ± 5.1 5.5 ±4.3 8.8 ±5.5 0.15 MPA (%) (0.97-17.4) (0.98-13.8) (2.2-17.4) Results expressed as mean±SD (range) * Comparison between CSA and TAC groups, Student's t-test PK = pharmacokinetic; CSA = cyclosporine; TAC = tacrolimus; AUC = area under the concentration-time curve; DN = dose-normalized; MMF = mycophenolate mofetil;  MPA = mycophenolic acid; MPAG = 7-O-mycophenolic acid glucuronide; AcMPAG = acyl glucuronide of  mycophenolic acid; Cmax = maximum concentration observed in the 12-hour PK profile;  Tmax = time when Cmax occurs; Cmin = minimum concentration observed in the 12-hour PK profile;  Vd/F = apparent volume of  distribution; Cl/F = apparent total body clearance MPA PK profile, lung transplant recipients (n=21) 10 — 9 I s 3 —' 7 c o re c a) o c o O < QL 6 -5 4 3 2 1 0 0 6 8 Time (hr) 10 12 14 b. Subjects stratified  into CSA (n=l 1) and TAC (n=9) groups MPA PK profiles, stratified by CSA or TAC 12 10 o> 3 c 8 o 'J S5 -b 6 c a) o O 4 O < 9 0. ^ S 0 • 0 • • • • CSA • TAC • • • 6 8 Time (hr) 10 12 14 MPA = mycophenolic acid; CSA = cyclosporine; TAC = tacrolimus; pharmacokinetic MPAG profile, all patients 100 -.—. 90 _ E O) 80 -3 C 70 -O •4-1 BO -(0 •t—l c 50 -0) o c 40 o o 30 -o < 20 Q. 10 -0 0 8 10 12 14 Time (hr) b. Subjects stratified  into CSA (n=l 1) and TAC (n=9) groups MPAG profiles, stratified by CSA or TAC 120 I 100 cn c o c V o c o o (D < a. 8 0 -60 40 -14; TO 2 0 -" 0 -b 0 • • • • 6 8 Time (hr) • 10 • CSA • TAC • 12 14 MPAG = 7-0 mycophenolic acid glucuronide; CSA = cyclosporine; TAC = tacrolimus; PK = pharmacokinetic AcMPAG profile, all patients 10 E 8 -I O) 3 £ O c Q> o c o o o < Q. s o < 6 -k. 4 2 -- 2 12 14 Time (hr) b. Subjects stratified  into CSA (n=l 1) and TAC (n=9) groups AcMPAG profiles, stratified by CSA or TAC 12 | 10 o> 3 O ra 0) o c o o O < Q. 8 -6 -4 2 0 - 2 ^ -4 - 6 J • • p P 10 Time (hr) • CSA PTAC • 12 14 AcMPAG = acyl glucuronide of  mycophenolic acid; CSA = cyclosporine; TAC tacrolimus; PK = pharmacokinetic The fMPA  is presented in Table 2-3. There was no statistically significant  difference between the CSA and TAC groups, and no significant  correlation between free  fraction  and albumin levels (r2= 0.14). The mean fMPA  was 7.0% (median 6.2%, range 0.97 - 17.4%), g more than twice the previously reported value of  3% . 2.5 Discussion 2.5.1 Inter-Subject Variability in PK Parameters Effective  immunosuppressive therapy is an essential aspect of  successful  organ transplantation. However, unpredictable pharmacokinetics of  immunosuppressive agents pose great challenges for  clinicians in the management of  graft  rejection and monitoring of adverse events. Wide inter-subject variability of  MPA PK parameters has been observed in various transplant populations, including kidney, liver, heart, and lung25 '26 '11 '15 '16 '20-21 . Unlike the kidney and liver, the lung is not usually involved in drug elimination. Pharmacokinetics of  MPA may therefore  be different  in this transplant group. In addition to MPA, we also observed wide variability in fMPA  and pharmacokinetics of  the metabolites, MPAG and AcMPAG (Figures 2-3 - 2-5, Table 2-3). Since there are no other similar PK studies on lung transplant recipients, direct comparison of  MPAG and AcMPAG PK characteristics was not possible. However, a brief comparison with recent studies done by various groups on the pharmacokinetics of  MPA +/-MPAG and AcMPAG in various transplant populations is presented in Table 2-4. The comparison provides only a crude overview of  MPA PKs in different  transplant groups as patient demographics and study methods may differ  between centers. In general, the inter-subject variability of  MPA PK parameters was similar across studies. The AUC, Cmin and Cmax of  MPA were lower in this study than in others. In fact,  the mean MPA AUC in both the CSA and TAC groups were below the recommended therapeutic range of  30 - 60 ug*h/mL15 '16 '29. However, there appeared to be less protein binding in our lung transplant recipients, as the average fMPA  was twice the expected value of  3%8. The reason for  this observation is still unknown, as no correlation was found  bet ween free  fraction  and albumin, MPAG or AcMPAG levels. There was also large inter-subject variability in fMPA in our subjects, with fMPA  ranging from  0.97 - 17.4%. Similar variability was observed by Atcheson et al., who reported an fMPA  range of  1.6 - 18.3% in 42 renal transplant • • 39 recipients . Nonetheless, the higher free  fraction  may explain why participants in this study had relatively low MPA AUC, high MPA clearance, and were still in stable condition. Specifically,  the recommended therapeutic range of  fMPA  AUC, assuming a free  fraction  of 3%, would b e 0.9 - 1.8 ug*h/mL and our study subjects had an average fMPA  AUC o f about 2 ug*h/mL (i.e., 7% x 28.6 ug*h/mL). The fMPA  in other studies was at the expected value, with the exception of  liver transplant recipients in the early post-transplant period27. Only a few  other studies included analysis of  MPAG and/or AcMPAG (Table 2-4). The MPAG/MPA AUC ratios varied from  8 to 64 between studies. Since the analytical assays of  MPA metabolites and their characterization were a recent development, information regarding expected metabolic ratios is lacking. Table 2-4. Selected MPA pharmacokinetic studies from  other research groups mean ± SD unless otherwise indicated Type of Transplant MMF dosage CSA or TAC DN MPA AUC (Hg*h/mL) MPA Cmin (Hg/mL) DN MPA Cmax (jug/mL) MPAG/MPA AUC ratio AcMPAG/MPA AUC ratio Free MPA (%) Comments This study Lung 1 - 1.5 g BID CSA 19.25 ± 11.23 0.53 ± 0.35 6.94 ± 4.28 29.31 ± 13.18 2.35 ±4.18 5.5 ± 4.3 n= 11 This study Lung 0.75-1.5 g BID TAC 28.00 ± 15.43 0.97 ± 0.50 5.63 ± 2.60 15.08 ± 5.82 0.45 ± 0.72 8.8 ± 5.5 n= 10 Ensom et al.25 Lung 0.75 -1.5 g BID CSA 23.57 ± 15.76 3.12 ± 1.41 9.30 ± 9.66 N/A N/A 2.90 ± 0.56 n = 7 David-Neto et al.30 Kidney 0 .5 -1 g BID CSA or TAC 49.5 ± 21.0* 2.6 ± 1.7* 12.6 ± 6.5* N/A N/A N/A n = 35 Cho et al.31 Kidney 0.75g BIC CSA 24.6 ±5.7 N/A 11.6 ± 6.2} N/A N/A 1.60 ± 0.24 In Korean patients, n = 10 Kuypers et al.32 V Kidney l g BID TAC 58.8** (27-111) 1.95 ** (0.35 -7.69) N/A 12.6# 0.076# 0.86 Only data from  12-month post-transplant are presented here, n = 33 Kuypers Kidney 0.5 g TAC 53.8 ±24.8 3.94 ± 22.19 ± N/A N/A N/A Only et al.33 or 1 g BID 1.94 11.00 results from patients on 1 g BID at 12 months post-transplant are presented here, n = 100 Jain et Liver l g TAC 40.0 ±30.9 1.1 ± 10.6 ± 63.7 ±83.6 N/A 3.9 ± Patients al.3 BID 1.4 7.5 1.6 were within 1 month post-transplant, n = 8 Pisupati Liver 0 . 5 - 1 TAC 118 ± N/A 36.7 ± 8.0±3.3 N/A 1.9 ± Patients et al.34 g BID 57.6* 15.6* 1.0 were <6 weeks post-transplant, n = 10 DeNofrio Heart l g CSA 44.5 ± 16.1 1.2 ±- N/A N/A N/A 1.9 ± n = 38 et al.35 BID 0.6 0.4 MMF = mycophenolate mofetil;  AUC = area under the concentration-time curve; CSA = cyclosporine; TAC = tacrolimus; DN = dose normalized; MPA = mycophenolic acid; Cmin = minimum concentration; Cmax = maximum concentration; MPAG = 7-O-mycophenolic acid glucuronide; AcMPAG = acyl glucuronide of  MPA; BID = twice daily; SD = standard deviation. N/A = not available * Not dose-normalized ** Median (range) # Calculated by dividing mean MPAG and AcMPAG AUC with MPA AUC % Calculated by dividing value with 0.75 g dose (to normalize to 1 g) Since this was not a longitudinal study, intra-subject variability of  MPA PK parameters was not determined. However, we are planning to investigate the MPA intra-subject variability in lung and heart transplant recipients in future  studies. Large intra-subject variability in MPA PKs has been reported by various groups30 '34 '36. For example, Pisupati et al. reported significant  changes in Cmax, AUC, and clearance of  MPA in liver transplant recipients studied at three different  periods. The intra-subject variability in intrinsic clearance of  MPA was determined to be 20%. Of  note, all subjects were studied shortly after  transplantation (within one month), so the intra-subject variability may be due to the recovery of  liver function  over time. A study by David-Neto et al. reported an intra-subject variability of  46%, 33%, and 20% for  CO (trough concentration), C2 (concentration at 2 hours post-dose) and AUC of  MPA, respectively. However, the changes were not considered clinically significant  by the authors, and frequent  TDM of  MPA was not deemed 30 necessary . Changes m diet, GI function,  biliary secretion, and liver and/or kidney function may all contribute to the intra-subject variability observed. 2.5.3 Concomitant Immunosuppressant - CSA and TAC Although concomitant immunosuppressive agents such as CSA and TAC have been shown to alter pharmacokinetics of  MPA, the extent of  and mechanisms underlying such interactions are still being investigated. When our data were stratified  by TAC and CSA, variability of  MPA PK parameters did not decrease considerably. This suggests that known immunosuppressive drug-drug interactions are only one of  the many factors  that contribute to MPA inter-subject variability. However, our stratified  data were consistent with the current knowledge of  drug-drug interactions of  CSA and MPA, and TAC and MPA. It is postulated that CSA decreases MPA exposure via inhibition of  biliary excretion of  MPAG into the intestines, thus reducing MPA re-circulation and increasing MPAG plasma concentrations37. Other evidence also suggests that CSA inhibits the de-glucuronidation of TO MPAG to MPA by inactivating the (3-glucuronidases in the GI tract . Conversely, in vitro studies show that TAC is an inhibitor of  UGTs, therefore  potentially hindering the metabolism of  MPA and increasing MPA levels18'38. 2.5.4 Cystic Fibrosis Comorbidities, such as CF, may also affect  the PKs of  MPA2. Patients with CF are prone to GI complications that impede absorption of  nutrients and drugs. A study by Gerbase et al.2 showed that lung transplant recipients with CF required a 30% higher weight-adjusted dose of  MMF. In the current study, five  subjects had CF, and their MMF dosage was not different  from  the other lung transplant recipients. Four out of  the 5 CF patients were also taking TAC, which may have precluded the need to boost MMF dosage. The DN AUC of  MPA and the metabolic AUC ratio of  AcMPAG/MPA were lower in the CF patients than the rest of  the group; however, these differences  were not statistically significant  (mean DN AUC: 19.4 versus 24.7 ug*h/mL; mean AcMPAG/MPA: 0.68 versus 1.68, in CF and non-CF patients, respectively). Conversely, the AUC ratio of  MPAG/MPA in CF patients was significantly  lower than the rest of  the group (14.3 versus 25.1, in CF and non-CF patients respectively; p-value = 0.007). The lower MPA exposure and metabolic ratios suggested that in addition to decreased absorption, the metabolism of  MPA may also be hindered in CF patients. 2.6 Summary of  MPA PKs The large inter-subject variability in MPA, MPAG and AcMPAG pharmacokinetics in lung transplant recipients suggests that routine therapeutic drug monitoring of  MPA is highly desirable in this unique transplant population. Factors such as concomitant medication, disease state, patient demographics, lifestyles,  and genetic polymorphisms in the UGTs responsible for  MPA's metabolism may all play a role in determining the disposition of  MPA. Research in other aspects, such as pharmacogenetic studies on the UGT genes, is much needed to help explain and predict such variability. Further analyses to characterize population PKs of  MPA and to develop limited sampling strategies for  convenient estimation of  MPA AUC are underway. 2.7 Reference s 1. Eisen H and Ross H. 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The pharmacokinetic-pharmacodynamic relationship for  mycopehnolate mofetil  in renal transplantation. Clin Pharmacol Ther. 1998; 64 : 672 - 83. 30. David-Neto E, Pereira LM, Kakehashi E, Sumita NM, Mendes ME, Castro MC, Romano P, Mattos RM, Batista VR, Nahas WC, Ianhez LE. The need of mycophenolic acid monitoring in long-term renal transplants. Clin Transplant. 2005; 19: 19-25. 31. Cho EK, Han DJ, Kim SC, Burckart GJ, Venkataramanan R, Oh JM. Pharmacokinetic study of  mycophenolic acid in Korean kidney transplant patients. J Clin Pharmacol. 2004 ; 44: 743 - 50. 32. Kuypers DR, Vanrenterghem Y, Squifflet  JP, Mourad M, Abramowicz D, Oellerich M, Armstrong V, Shipkova M, Daems J. Twelve-month evaluation of  the clinical pharmacokinetics of  total and free  mycophenolic acid and its glucuronide metabolites in renal allograft  recipients on low dose tacrolimus in combination with mycophenolate mofetil.  Ther Drug Monit. 2003 ; 25 : 609 - 22. 33. Kuypers DR, Claes K, Evenepoel P, Maes B, Coosemans W, Pirenne J, Vanrenterghem Y. Long-term changes in mycophenolic acid exposure in combination with tacrolimus and corticosteroids are dose dependent and not reflected  by trough plasma concentration: a prospective study in 100 de novo renal allograft  recipients. J Clin Pharmacol. 2003 ; 43 : 866 - 80 34. Pisupati J, Jain A, Burckart G, Hamad I, Zuckerman S, Fung J, Venkataramanan R. Intraindividual and interindividual variations in the pharmacokinetics of mycophenolic acid in liver transplant patients. J Clin Pharmacol. 2005 ; 45 : 34 -41. 35. DeNofrio  D, Loh E, Kao A, Korecka M, Pickering FW, Craig KA, Shaw LM. Mycophenolic acid concentrations are associated with cardiac allograft  rejection. J Heart Lung Transplant. 2000; 19: 1071 - 6. 36. Dauden E, Sanchez-Peinado C, Ruiz-Genao D, Garcia-F-Villalta M, Onate MJ, Garcia-Diez A. Plasma trough levels of  mycophenolic acid do not correlate with efficacy  and safety  of  mycophenolate mofetil  in psoriasis. Br J Dermatol. 2004 ; 150: 132 - 5. 37. Deters M, Kirchner G, Koal T, Resch K, Kaever V. Influence  of  cyclosporine on the serum concentration and biliary excretion of  mycophenolic acid and 7-0-mycophenolic acid glucuronide. Ther Drug Monit. 2005 ; 27: 132 - 8. 38. Holt DW. Monitoring mycophenolic acid. Ann Clin Biochem. 2002; 39: 173 - 83. 39. Atcheson BA, Taylor PJ, Mudge DW, Johnson DW, Hawley CM, Campbell SB, Isbel NM, Pillans PI, Tett SE. Mycophenolic acid pharmacokinetics and related outcomes early after  renal transplant. Br J Clin Pharmacol. 2004; 59: 271 - 8. Chapter 3 - Limited Sampling Strategies of  Mycophenolic Acid for Estimation of  Area Under the Concentration-Time Curve in Lung Transplant Recipients A version of  this chapter will  be submitted  for  publication 3.1 Specific  Aim #2 The purpose of  this study was to establish clinically convenient limited sampling strategies that utilize blood samples within two hours post-dose for  the prediction of  MPA AUC in the lung transplant population. 3.2 Introduction Mycophenolate mofetil  (MMF), the prodrug of  mycophenolate acid (MPA), is now commonly used in solid organ transplantation. It exerts its immunosuppressive effects  by inhibiting the enzyme inosine monophosphate dehydrogenase, which is essential in the de novo synthesis of  purines for  DNA replication when cells (especially lymphocytes) proliferate 1'2'3. MPA is metabolized by UDP-glucuronosyltransferases  (UGTs) mainly to the inactive metabolite mycophenolate glucuronide (MPAG). A minor but pharmacologically active metabolite, the acyl glucuronide of  MPA (AcMPAG), has been recently identified 4'5'6. A second absorption peak of  MPA is often  observed due to its enterohepatic recirculation as MPAG is de-glucuronidated in the intestine. Adverse effects  of  MPA are mainly gastrointestinal (GI), including diarrhea, vomiting, ulcers and GI bleeding.2'7 The specific actions on lymphocytes and lack of  nephro- and hepato-toxicity of  MPA has led to its success in immunosuppressive therapy in kidney, heart, lung, and other transplantation populations.2'8'9'10 MMF is typically administered orally twice a day, at a dosage of  2 to 3 g daily. It is the only immunosuppressive agent that is not dosed by body weight.7'"'12 Although no recommendations on therapeutic drug monitoring (TDM) were made initially, recent study findings  indicate the benefits  of  MPA TDM. Positive clinical outcomes (lower rates of rejection, infection  and adverse effects)  have been established with adequate MPA exposure guided by monitoring plasma MPA levels.9'13'14 It is generally agreed that the area under the concentration versus time curve (AUC) of  MPA is a good predictor of  treatment response. Other more convenient predictors, such as the trough concentration (Ctrough), have also been investigated; however, its correlation with AUC and treatment outcomes is poor.11 '15 '16 Since obtaining a full  12-hour pharmacokinetic (PK) profile  for  estimation of  AUC is inconvenient and costly, a limited sampling strategy (LSS) that provides accurate and precise estimation of  AUC would greatly simplify  TDM and benefit  patients. Limited sampling strategies for  MPA have been suggested by several groups.17 ~ 2 3 However, all these studies were c onducted in the kidney transplant population, and some suggest sampling times that are not clinically convenient (e.g., beyond two hours post-dose). To date, there is no LSS established for  the lung transplant population, even though about 50% of  the lung transplant population has MMF as part of  their immunosuppressive therapy24. Typically a LSS established in a specific  population is not always suitable for other populations, especially when function  of  the transplanted organs may have a different impact on drug metabolism. We therefore  strived to establish clinically convenient LSSs that utilized blood samples within two hours post-dose for  the lung transplant population. 3.3 Methods 3.3.1 Patient Population and MPA Concentrations Please refer  to sections 2.3.1 and 2.3.2 for  descriptions of  patient population, sample processing and drug level assay for  determination of  MPA concentrations. Due to incomplete sampling, two of  the subjects were excluded from  this study. Data from  19 subjects were used. 3.3.2 Pharmacokinetic Parameters Assessment The AUC of  MPA was determined for  each participant by non-compartmental analysis using WinNonlin Professional  version 4.1 (Pharsight, Mountain View, CA). 3.3.3 Limited Sampling Strategy Determination Multiple regression analysis was used to determine LSSs for  MPA. The Bayesian method was not chosen because there were no population PK data (the priors) for  MPA available, without these Bayesian analysis would not give the best prediction (sections 1.3.1a and 1.3.1b). Ten subject profiles  were randomly assigned as the index group to establish the limited sampling strategy. Of  the 10 subjects, 5 were also taking TAC, and 5 were taking CSA. Multiple regression analysis was performed  using Statistica® statistics software (StatSoft  Inc., 97 edition, Tulsa, OK). The AUC was the dependent variable while the timed concentrations were the i ndependent variables. T he AUC and timed c oncentrations were log-transformed  before  regression analysis. Only concentrations taken on or before  2 hours post-dose were considered for  the LSS, and a maximum of  3 concentrations were used. The backward elimination method was used to determine the best initial regression equation. All timed concentrations were used initially for  the regression analysis; concentrations were then removed one by one from  the equation and the r2 was re-calculated. Timed • * 2 concentrations that did not affect  the r were omitted from  the regression equation. In addition, different  combinations of  timed concentrations (3 concentrations maximum) on or before  2 hours post-dose were manually entered for  regression analysis to determine the correlation with AUC. Only equations with an r2 > 0.75 were considered for  validation. 3.3.4 Validation of  LSS The remaining 9 MPA PK profiles  (5 subjects also on TAC, 4 on CSA) were used to validate the LSSs developed. The predicted AUC obtained from  the LSS was compared to the observed AUC. The accuracy and precision of  the LSS was determined according to guidelines proposed by Sheiner and Beal25. Bias was measured by the mean prediction error (ME), and precision was measured by the root mean square prediction error (RMSE), according to equations 1 and 2 (section 1.3.2). Acceptable accuracy and precision were deemed to be ±15%26. 3.4 Results 3.4.1 Study Subjects Characteristics Characteristics of  the index and validation groups are summarized in Table 3-1. All parameters, except the years since transplant, were not significantly  different.  There was a balanced number of  subjects taking TAC or CSA as co-medication and similar numbers of patients with and without cystic fibrosis. Table 3-1. Characteristics of  lung transplant recipients in the index and validation | r o u £ S All subjects (n=19) Index group (n=10) Validation group (n=9) p-value* Male (%) 57.9 50.0 67.6 0.65 J Age (yrs) 48.3 ± 15.0 49.0 ± 16.6 47.6 ± 14.0 0.83 Years since transplant (yrs) 4.2 ±3.7 2.4 ±3.0 6.1 ±3.6 0.03 Height (cm) 168.6 ± 11.1 166.1 ±9.9 171.3 ± 12.4 0.45 Weight (Kg) 72.3 ± 17.9 68.9 ± 12.8 76.1 ±22.6 0.42 MMF daily dosage (mg) 2526 ±539 2600± 568 2444 ± 527 0.54 Co-med CSA 9 5 4 1.00$ Co-med TAC 10 5 5 1.00$ Cystic fibrosis 4 1 3 0.30$ MMF = Mycophenolate mofetil;  CSA = cyclosporine; TAC = tacrolimus; yrs = years; n = number of  subjects Data expressed as mean ± SD * Comparison between index and validation groups, Student's t-test £ Fisher's exact test The best correlation between AUC and timed concentrations utilized five concentrations (Cx = MPA concentration at time x): CO, CO.6, CI, CI.5 and C2, with an r2 of  0.945. The equation for  predicting AUC was: Log AUC = 0.361 Log CO - 0.121 Log C0.6 + 1.247 Log CI - 2.236 Log C1.5 + 1.862 Log C2 + 0.969 Although this LSS required many timed concentrations, the % bias and % precision were -15.3% and 18.2 % respectively, which exceeded our limitation of  ±15%. This LSS is therefore  not recommended. 3.4.3 LSSs Using Single Concentration The correlations between AUC and single concentrations were generally poor (r2 ranging from  0.176 to 0.732), except for  C8 (r2 = 0.884) and C10 (r2 = 0.912). Although the single-concentration LSSs were beyond 2 hours post-dose, they were included in this report since they provided acceptable r2, precision and bias. Although highly desirable, the conventional sampling times at 0 or 2 hours post-dose did not yield satisfactory  correlation with AUC. The r2 was 0.714 and 0.663 for  the CO and C2 LSSs, respectively. A summary of  the validation results for  single concentration LSSs is included in Table 3-2. 3.4.4 LSSs Using Two Concentrations Eight 2-concentration LSSs had acceptable r2 (>0.75) and were considered further for  validation. A summary of  validation results for  2-concentration LSSs is presented in Table 3-2. Of  the eight combinations of  2-concentration LSSs, the combinations (CO, C2) and (CO, CI.5) yielded clinically convenient sampling times, and accurate and precise estimation of  AUC (Table 3-2). Other LSSs, such as the combinations of  (CO.3, CI.5) and (CO, CO.6) also had similar predictive performance;  however, sampling at 0.3 and 0.6 hours post-dose may be more challenging in a clinical setting. Table 3-2. Predictive performance  of  one- and two-concentration limited sampling strategies AUC Equations (Hg*h/mL for  AUC, ng/mL for  Cx) r2 % bias (% ME) % precision (% RMSE) Number of profiles*  within ±15% precision and bias Log AUC = 0.494 Log CIO + 1.350 0.912 -5.12 8.24 7 Log AUC = 0.624 Log C8 + 1.278 0.884 -4.65 8.23 8 Log AUC = 0.310 Log C0.3 + 0.409 Log C2 + 1.046 0.835 -5.56. 7.38 7 Log AUC = 0.502 Log C0.6 + 0.465 Log C2 + 0.860 0.833 -3.46 9.28 7 Log AUC = 0.241 Log CO + 0.406 Log C2 + 1.140 0.828 -5.82 5.97 8 Log AUC = 0.420 Log C0.6 + 0.470 Log CI.5 + 0.848 0.816 -3.46 7.21 7 Log AUC = 0.259 Log C0.3 + 0.409 Log CI.5 + 1.01 0.793 -5.54 5.88 8 Log AUC = 0.202 Log CO + 0.411 Log C1.5 + 1.09 0.791 -5.71 6.94 8 Log AUC = 0.245 Log CO + 0.412 Log C0.6+ 1.103 0.778 -4.52 7.20 8 Log AUC = 0.433 Log CI + 0.487 Log C2 +0.817 0.754 -9.18 10.37 7 *out of  nine validation pharmacokinetic profiles  tested AUC = area under the concentration-time curve; ME = mean prediction error; RMSE = root mean square prediction error; Cx = concentration at time x 3.4.5 LSSs Using Three Concentrations Table 3-3 summarizes the validation results of  LSSs using three timed concentrations. All 3-concentration LSSs yielded similar validation results, with a precision and bias within ±8%. The best combination of  precision, bias and r was represented by the LSS using (CO, CO.6, C2). The LSSs with the most convenient and conventional sampling times were (CO, CI, C2) and (CO, CI.5, C2), and in both cases the predictions of  AUC in all validation profiles  were within acceptable ranges (i.e., within ± 15%). T he LSS with the shortest sampling period was (CO, CO.6, CI); however, it had the lowest r2 value. Table 3-3. Predictive performance  of  three-concentration limited sampling strategies AUC Equations (|ig*h/mL for  AUC, jig/mL for  Cx) r* % bias (% ME) % precision (% RMSE) Number of profiles*  within ±15% precision and bias Log AUC = 0.153 Log CO + 0.327 Log C0.6 + 0.354 Log C2 + 1.000 0.873 -3.70 5.81 8 Log AUC = 0.128 Log CO + 0.186 Log CO.3 + 0.367 Log C2 + 1.102 0.836 -5.41 5.68 9 Log AUC = 0.192 Log CO + 0.213 Log CI + 0.355 Log C2 + 1.024 0.827 -6.88 6.88 9 Log AUC = 0.131 Log CO + 0.320 Log CO.6 + 0.333 Log CI.5 + 0.974 0.825 -3.76 4.88 8 Log AUC = 0.253 Log CO - 0.070 Log CI.5 + 0.460 Log C2 + 1.154 0.800 -5.90 6.03 9 Log AUC = 0.119 Log CO + 0.161 Log CO.3 = 0.347 Log CI.5 + 1.079 0.782 -5.49 5.49 8 Log AUC = 0.195 Log CO + 0.109 Log CI + 0.347 Log CI.5 + 1.054 0.761 -6.42 6.42 9 Log AUC = 0.224 Log CO + 0.323 Log CO.6 + 0.159 Log CI + 1.045 0.753 -5.80 7.55 8 * out of  nine validation pharmacokinetic profiles  tested AUC = area under the concentration-time curve; ME = mean prediction error; RMSE = root mean square prediction error; Cx = concentration at time x 3.5 Discussion 3.5.1 Current Status of  MPA LSSs Although concrete guidelines on the therapeutic range of  MPA AUC are lacking, 30 - 60 ug*h/mL has been suggested9'13'15. Since it is challenging to determine AUC on a routine basis, LSSs are a useful  tool to abbreviate PK profiling. In this study, we have developed LSSs for  estimation of  MPA AUC in stable lung transplant recipients. Successful  LSSs should be practical and c linically convenient, and still provide accurate and precise estimation of  AUC. Although LSSs for  estimation of MPA AUC have been established recently by various groups (Table ^-Af 1 '^ '20 '21 '2^29 , most of  the LSSs suggested were center-specific,  as suggested sampling times differed  between research groups. This is probably due to the different  sampling times used, and the varying number of  samples, ranging from  8 - 1 3 (Table 3-4). As MPA is absorbed mainly in the first  2 hours, and reabsorbed at 6 - 12 hours post-dose, an accurate characterization of  AUC depends on when and how frequently  samples were taken during these phases. In addition, the suggested sampling times in these studies were not always practical, and some utilized more than 3 blood samples. Since a clinically convenient sampling strategy is easier to implement and likely to encourage patient adherence, we limited ours to a maximum of  3 samples drawn within 2 hours post-dose. Furthermore, all other published LSSs were developed in kidney transplant recipients. Given that the liver and kidney, but not lungs, are involved in elimination of  MPA, the PKs of  MPA is likely to be different  in the lung transplant population. 3.5.2 Characteristics of  Index and Validation Groups In this study, study subjects were randomly divided into index and validation groups to balance parameters such as co-medication and disease state. Most of  the parameters, such as age, height, weight, and MMF dosage were similar in both groups. While the validation group had a longer time-since-transplant than the index group (Table 3-1, p = 0.03), a previous study in heart and lung transplant recipients did not observe differences  in MPA 1 9 PKs in different  periods of  time post-transplant . Since our subjects were in stable condition, the MPA PK parameters were assumed to be independent of  the time-since-transplant. Table 3-4. Selected MPA LSSs developed by multiple regression analysis from  other research groups Research group Type of Transplant Sampling times (hr) Suggested Concentrations LSS Equation Comments Willis C et al.27 Kidney 0, 0.25,0.75, 1.0, 1.25, 1.5,2.0,3.0, 4.0, 6.0, 8.0, 10.0, 12.0 CO, C1,C3, C6 AUC = 9.02+ 3.77 CO + 1.33 CI + 1.68 C3 + 2.96 C6 Adult patients, data from  1st month after transplant Filler G and Mai I. T7~ Kidney 23" 0, 0.5, 1, 1.5,2,3, 4, 6, 8, 12 C1,C2, C6 AUC = 10.75 + 0.98 CI + 2.38 C2 +4.86 C6 Pediatric patients Kuriata-Kordek M et al. Kidney 0, 0.6, 1,2, 4, 6, 8, 10, 12 C2, C6 and C4, C8, C12 For CSA group: AUC = 11.73 C6 + 2.92 C2 -0.247 For TAC group: AUC = 7.06 C4 + 6.77 C8-3.76 C12 + 15.3 Adult patients Le Guellec C et al. Kidney 0, 0.3,0.6, 1, 1.5,2, 3, 4, 6, 9 C0.3, CI, C3 AUC = 0.58 CO.3 + 0.97 CI +6.64 C3 + 3.48 Adult patients. C12 was extrapolated. LSS for  CSA also developed using the samefsampling times van Hest RM et al ir Kidney 70~ 0, 0.3,0.6, 1.25,2, 6, 8, 12 CO, CO.6, C2 AUC = 7.182+ 4.607 CO + 0.998 C0.6 +2.149 C2 Adult, diabetic patients Pawinski T et al. Kidney 0, 0.5, 1,2,3,4, 6, 8, 9, 10, 11, 12 CO, CO.5, C2 AUC = 7.75 + 6.49 CO + 0.76 CO.5 + 2.43 C2 Adult patients AUC = area under the concentration-time curve Cx = concentration at hour x post-dose 3.5.3 Concomitant Immunosuppressant - CSA and TAC We established and validated our LSSs in all lung transplant recipients who were on steady-state MMF therapy, regardless of  concomitant immunosuppressive agents. Data were not stratified  due to the small sample size; however, both index and validation groups had a balanced number of  subjects taking CSA or TAC. Since CSA is known to decrease MPA levels and AUC, while TAC may augment MPA exposure30"38, it would be ideal to develop LSSs for  each concomitant medication group. The data were thus re-analyzed by separating the PK profiles  into CSA and TAC groups to see if  there was less variability in the prediction. Original, non-transformed  data were used. Of  the nine profiles  in the CSA group, five  were used as the index group and the remaining four  as the validation group. One single-concentration LSS, nine two-concentration LSSs, and 19 three-concentration LSSs were tested. However, none of  the LSSs fulfilled  the criteria of  an acceptable LSS. The % bias of  two-concentration LSSs and three-concentration LSSs ranged from  30 -219% and 1.9 - 24%, respectively. The % precision ranged from  -7.0 - 213% and -30.0 -236% for  the two- and three-concentration LSSs, respectively. Of  the ten profiles  in the TAC group, five  were used as the index group, and five  as the validation group. Three two-concentration LSSs and ten three-concentration LSSs were validated. Similar to the CSA group, none of  the LSSs in the TAC group were acceptable. The % bias ranged from  10.2 - 38.7% and 25.1 - 54.3% for  the two- and three-concentration LSSs, respectively. The % precision ranged from  27.3 - 47.2% and 10.5 -40.1% for  the two- and three-concentration LSSs, respectively. The variability in MPA PK parameters, which potentially influences  the predictive performance  of  the LSSs tested, did not improve when the data were stratified  into CSA and TAC groups. None of  the LSSs from  the stratified  analysis provided acceptable LSSs. This was probably due to the small sample size, especially when the data were stratified. Nevertheless, as shown by the validation results from  unstratified  data, the LSSs developed were robust enough for  prediction of  MPA AUC in all lung transplant recipients, regardless of  co-medication. 3.5.4 Log-Transformation The concentrations and AUCs were log-transformed  in this study in order to normalize the data for  more reliable prediction. The Food and Drug Administration guidelines on statistical approaches to establishing bioequivalence recommend transformation  of  the AUC and concentration data if  sample size is small22, since a small sample size precludes normal distribution of  PK parameters, and variance in the timed concentrations may not be uniform.  In fact,  when LSSs were developed using the untransformed  data in this study, predictive performance  was significantly  worse, even when stratified  by co-medication (section 3.5.3). Since data transformation  may pose inconvenience for  health care personnel, an example nomogram is included to provide quick anti-log conversion of  the AUCs (Appendix 2). Clinicians can then determine MPA exposure and adjust MMF dosage accordingly. However, this nomogram would require validation in a large cohort before  routine use. 3.5.5 Recommended LSSs Although the LSSs using only C8 or CIO both yielded acceptable bias and precision in our analysis, these sampling times are not convenient for  patients and health care personnel, and r ely on t he p atients t o r eport t he d osing t ime accurately. S ince t iming i s crucial for  accurate predictions by LSSs developed via multiple regression analysis, and there is such great variability in the PK profiles  of  MPA9 '14 '33 '39"42, it is inappropriate to depend solely on one timed concentration for  reliable forecasting  of  total MPA exposure. All LSSs using 3 concentrations yielded similar predictive performance  characteristics. Although the LSS using (CO, CO.6, C2) is the best overall, sampling at 0.6 hours (i.e., 40 minutes) may be challenging. The LSSs (CO, CI, C2) and (CO, CI.5, C2) were also recommended as they provided more convenient sampling times. The LSSs using 2 concentrations provided the best combination of  cost, convenience, and predictive performance.  Adding a sample did not improve predictive performance  significantly  (Tables 3-2 and 3-3) but increased the burden of  sample collection, handling, and processing. In addition, the recommended sampling times of  (CO, C2) and (CO, CI.5) are both conventional and convenient, unlike sampling times at 0.3 (20 minutes) and 0.6 (40 minutes) hours. 3.6 Summary of  MPA LSSs To our knowledge, these are the first  precise and accurate LSSs for  predicting MPA AUC developed specifically  for  lung transplant recipients. Although a total of  18 LSSs fulfilled  our criteria of  an acceptable LSS, the LSSs utilizing two concentrations were superior. Specifically,  the LSSs (CO, C2) and (CO, CI.5) were the best candidates when convenience and cost were taken into consideration. Our study template provides a guide for other centers to develop accurate and precise LSSs specific  to their own patient population. 3.7 Reference s 1. Allison AC and Eugui EM. Purine metabolism and immunosuppressive effects  of mycophenolate mofetil  (MMF). Clin Transplant. 1996;10:77-84. 2. Sollinger HW. Mycophenolates in transplantation. Clin Transplant. 2004; 18: 485 -92 . 3. Srinivas T, Kaplan B, and Meier-Kriesche H. Mycophenolate mofetil  in solid-organ transplantation. Expert Opin. Pharmacother. 2003; 4(12): 2325-45 4. Shipkova M, Armstrong VW, Wieland E, Niedmann PD, Schutz E, Brenner-Weiss G, Voihsel M, Braun F, Oellerich M. 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Population pharmacokinetics and Bayesian estimation of  mycophenolic acid c oncentrations i n s table r enal t ransplant p atients. C lin P harmacokinet. 2 004; 43: 253-66. 20. Pawinski T, Hale M, Korecka M, Fitzsimmons WE, Shaw LM. Limited sampling strategy for  the estimation of  mycophenolic acid area under the curve in adult renal transplant patients treated with concomitant tacrolimus. Clin Chem. 2002; 48: 1497-504. 21. Schutz E, Armstrong VW, Shipkova M, Weber L, Niedmann PD, Lammersdorf  T, Wiesel M, Mandelbaum A, Zimmerhackl LB, Mehls O, Tonshoff  B, Oellerich M. Limited sampling strategy for  the determination of  mycophenolic acid area under the curve in pediatric kidney recipients. Transplant Proc. 1998; 30: 1182-4. 22. Weber L T, Schutz E, Lamersdorf  T, Shipkova M, Niedmann PD, Oellerich M, Zimmerhackl LB, Staskewitz A, Mehls O, Armstrong VW, Tonshoff  B. Therapeutic drug monitoring of  total and free  mycophenolic acid (MPA) and limited sampling strategy for  determination of  MPA-AUC in paediatric renal transplant recipients. Nephrol Dial Transplant. 1999; 14: 34-5. 23. Yeung S, Tong KL, Tsang WK, Tang HL, Fung KS, Chan HW, Chan AY, Chan L. Determination of  mycophenolate area under the curve by limited sampling strategy. Transplant Proc. 2001; 33: 1052 - 3. 24. Levine SM. A survey of  clinical practice of  lung transplantation in North America. Chest. 2004; 125: 1224-38. 25. Sheiner L B and Beal S L. Some suggestions for  measuring predictive performance. J Pharmacokinet Biopharm. 1981; 9:503 - 12. 26. David OJ and Johnston A. Limited sampling strategies for  estimating cyclosporin area under the concentration-time curve: Review of  current algorithms. Ther Drug Monit. 2001; 23: 100- 14. 27. Willis C, Taylor P, Salm P, Tett SE, Pillans PI. Evaluation of  limited sampling strategies for  estimation of  12-hour mycophenolic acid area under the plasma concentration-time curve in adult renal transplant patients. Ther Drug Monit. 2000; 22: 549-54. 28. Kuriata-Kordek M, Boraynska M, Falkiewicz K, Falkiewicz K, Porazko T, Urbaniak J, Wozniak M, Patrzalek D, Szyber P, Klinger M. The influence  of  calcineurin inhibitors on mycophenolic acid pharmacokinetics. Transplant Proc. 2003; 35: 2369-71. 29. van Hest RM, Mathot RAA, Vulto AG, Le Meur Y, van Gelder T. Mycophenolic acid in diabetic renal transplant recipients, pharmacokinetics and application of  a limited sampling strategy. Ther Drug Monit. 2004; 26: 620 - 5. 30. Barten MJ, Shipkova M, Bartsch P, Dhein S, Streit F, Tarnok A, Armstrong VW, Mohr FW, Oellerich M, Gummert JF. Mycophenolic acid interaction with cyclosporine and tacrolimus in vitro and in vivo. Ther Drug Monit. 2005; 27: 123 — 31. 31. Deters M, Kirchner G, Koal T, Resch K, Kaever V. Influence  of  cyclosporine on the serum concentration and biliary excretion of  mycophenolic acid and 7-0-mycophenolic acid glucuronide. Ther Drug Monit. 2005; 27: 132-8. 32. Filler G, Zimmering M and Mai I. Pharmacokinetics of  mycophenolic mofetil  are influenced  by concomitant immunosuppression. Pediatr Nephrol. 2000; 14: 100-4. 33. Filler G and Lepage N. To what extent does the understanding of  pharmacokinetics of  mycophenolate mofetil  influence  its prescription. Pediatr Nephrol. 2004; 19: 962 - 5 . 34. Hesselink DA, van Hest RM, Methot RAA, Bonthuis F, Weimar W, de Bruin RW, van Gelder T. Cyclosporine interacts with mycophenolic acid by inhibiting the multidrug resistance-associated protein 2. Am J Transplant. 2005; 5: 987-94. 35. Hubner GI, Eismann R and Sziegoleit W. Drug interaction between mycophenolate mofetil  and tacrolimus detectable within therapeutic mycophenolic acid monitoring in renal transplant patients. Ther Drug Monit. 1999; 21: 536 - 9. 36. PouL, Brunet M, Cantarell C, Vidal E, Oppenheimer F, Monforte  V, Vilardell J, Roman A, Martorell J, Capdevila L. Mycophenolic acid plasma concentrations: influence  of  comedication. Ther Drug Monit. 2001; 23: 35 - 8 . 37. Zucker K, Tsaroucha A, Olson L, Esquenazi V, Tzakis A, Miller J. Evidence that tacrolimus augments the bioavailability of  mycophenolate mofetil  through the inhibition of  mycophenolic acid glucuronidation. Ther Drug Monit. 1999. 21: 3 5 -43. 38. Zucker K, Rosen A, Tsaroucha A, de Faria L, Roth D, Ciancio G, Esquenazi V, Burke G, Tzakis A, Miller J. Augmentation of  mycophenolate mofetil pharmacokinetics in renal transplant patients receiving Prograf  and Cellcept in combination therapy. Transplant Proc. 1997; 29: 334-6. 39. Ensom MHH, Partovi N, Decarie D, Dumont RJ, Fradet G, Levy RD. Pharmacokinetics and protein binding of  mycophenolic acid in stable lung transplant recipients. Ther Drug Monit. 2002; 24: 310-4. 40. Shaw LM, Kaplan B, DeNofrio  D, Korecka M, Brayman KL. Pharmacokinetics and concentration-control investigations of  mycophenolic acid in adults after transplantation. Ther Drug Monit. 2000; 22: 14 - 9. 41. Kuypers DR, Vanrenterghem Y, Squifflet  JP, Mourad M, Abramowicz D, Oellerich M, Armstrong V, Shipkova M, Daems J. Twelve-month evaluation of  the clinical pharmacokinetics of  total and free  mycophenolic acid and its glucuronide metabolites in renal allograft  recipients on low dose tacrolimus in combination with mycophenolate mofetil.  Ther Drug Monit. 2003; 25: 609 - 22. 42. Mourad M, Wallemacq P, Konig J, de Frahan EH, Eddour DC, De Meyer M, Malaise J, Squifflet  JP. Therapeutic monitoring of  mycophenolate mofetil  in organ transplant recipients: is it necessary? Clin Pharmacokinet. 2002; 41: 319 - 27. Chapter 4 - Overall summary and conclusion 4.1 Overall discussion and conclusion There were two specific  aims in this study: to characterize the pharmacokinetics of MPA and its glucuronidated metabolites, and to develop limited sampling strategies for estimation of  MPA AUC in lung transplant recipients. Mycophenolic acid is a good candidate for  TDM and LSSs since it is initiated empirically at fixed  doses despite the variable PK response. Recent studies also reported an association between drug exposure and treatment outcomes1 ~6. Twenty-one stable lung transplant recipients who were on MMF therapy were recruited for  this 12-hour PK study. In addition to MMF, subjects were also taking prednisone and CSA or TAC. There was large inter-subject variability in all PK parameters of  MPA, MPAG and AcMPAG. Similar variability was observed after  stratifying  subjects into concomitant medication groups, CSA and TAC. Although not statistically significant, the CSA group generally had lower MPA and higher MPAG and AcMPAG levels than the TAC group. Subjects who had cystic fibrosis  had lower MPA exposure and metabolic ratios than the rest of  the group; however, only the MP AG/MP A AUC ratio was significantly different  (p< 0.05). In general, MPA exposure (indicated by AUC, Cmin and Cmax) was lower in our subjects than in other studies. The AUC of  MPA was also lower than the recommended 36 - 60 p.g*h/mL therapeutic range. However, the mean free  fraction  MPA in our subjects was also higher, which yielded comparable free  MPA AUC with other studies. Since obtaining AUC to characterize MPA exposure is impractical and costly, LSSs were therefore  developed to provide accurate and precise estimation of  AUC while minimizing the number of  samples required. Nineteen lung transplant recipients were included in this analysis. Ten subjects were assigned randomly to the index group and the remaining nine to the validation group. A maximum of  3 timed concentrations were used to develop the LSSs, and sampling times were restricted to within 2 hours post-dose. Criteria for  an acceptable LSS included bias and precision within ±15%, and r2 > 0.75. Two single-concentration LSSs, eight 2-concentration LSSs, and eight 3-concentration LSSs were validated. However, considering both cost and clinical feasibility, the recommended LSSs were: Log AUC = 0.241 Log CO + 0.406 Log C2 + 1.140 Log AUC = 0.202 Log CO+ 0.411 Log CI.5 + 1.09 Since the LSSs were validated in only 9 subjects, and there is such great variability in the PK response, single-concentration LSSs are not recommended at this time. Since log-transformation  of  the LSSs, as suggested in this study may be inconvenient for  clinicians, an example nomogram is included in Appendix 2. It is noteworthy that correct sampling and dose time is crucial for  accurate and precise estimation of  AUC using these LSS equations. A comparison with other recent LSS studies indicated that LSSs are center-specific 7 - l 2 , and should be re-developed or re-validated if  applied to a different  site or transplant group. Some LSSs reported used more than 3 c oncentrations, and a t otal s ampling t ime u p t o 6 hours p ost-dose. A n i deal L SS should use only convenient sampling times and a minimal number of  samples. 4.2 Strengths and weaknesses This study was the first  to characterize not only MPA, but also the glucuronidated metabolites and free  fraction  of  MPA, in lung transplant recipients. In addition, our results provided accurate and precise LSSs for  convenient estimation of  MPA AUC. The results obtained will be incorporated into patient care directly to improve immunosuppressive therapy management. Areas that could be improved in future  studies include: increasing sample size; characterizing intra-subject variability of  MPA PKs; investigating the combined effects  of multiple factors  on the PKs of  MPA; exploring pharmacokinetic-pharmacodynamic relationships; and including de  novo transplant recipients to characterize important clinical events that may not be present in stable transplant recipients. 4.3 Current knowledge and new ideas The lung transplant population has unique characteristics, worse survival rates and complications, yet studies in this group are still lacking13'14. The results obtained from  this study have provided a much better understanding of  the PK behavior of  MPA and its glucuronidated metabolites in this transplant population, and should result in improved patient care. This study demonstrated wide inter-subject variability in all PK parameters of MPA, MPAG and AcMPAG, prompting the PK monitoring of  MPA in routine clinical care. However, other aspects of  MPA PKs are still unknown. For example, the pharmacogenetics of  UGTs and pharmacodynamic interactions are important elements that contribute to the overall treatment response. In addition, the best PK parameter that predicts MPA toxicity remains to be elucidated. MMF dose, total MPA, free  MPA, and AcMPAG concentrations, are potential predictors of  toxicity. Exploration of  these areas will guide us closer to the ultimate goal - to individualize immunosuppressive regimens, even before treatment begins, for  each patient for  optimal treatment response and minimal toxicity. 4.4 Status of  working hypotheses As illustrated in this study, the large inter-subject variability in MPA PKs supports the therapeutic drug monitoring of  MPA for  lung transplant recipients in order to optimize MMF immunosuppressive therapy. In addition, LSSs f  or e stimation of  MPA AUC were determined to aid physicians in monitoring MPA drug exposure conveniently. These results will be suggested for  clinical use and will impact patient care directly. 4.5 Overall significance  of  the thesis research This appears to be the first  report on the PK characteristics of  MPA and its glucuronidated metabolites, and on convenient and practical 2-concentration LSSs developed in lung transplant recipients. Study results add valuable knowledge that bridges the gap in research on immunosuppression. These findings  are expected to improve lung transplantation management, which will benefit  transplant recipients significantly. 4.6 Future research Since the lung transplant population is relatively small, future  studies will include heart transplant recipients, as neither heart nor lung is involved in the elimination of  MPA. The PK-pharmacodynamic relationship of  MPA and minimun toxic concentration will also be evaluated in the thoracic transplant population, as such data are still scarce. This will help determine a therapeutic range of  MPA suitable for  the thoracic transplant group. In addition, the association between polymorphisms in the UGT gene and the PKs of  MPA is an important area of  research on the pharmacogenetics of  MPA. If  results are positive, this will allow genetic screening of  transplant recipients in the future  to determine the PK response before  MMF is prescribed; providing transplant reicipients the right dose of  MMF from  the beginning of  treatment would reduce episodes of  adverse events and maximizes efficacy.  Furthermore, the LSSs established in this current study will be re-validated or re-established to apply to the larger thoracic transplant group. Finally, collecting complete or scarce PK data from  a larger transplant population allows population PK analyses to characterize the MPA PK response in the population as a whole. The population PK data can then be used to predict individual PK response. All these future  research endeavors will greatly improve our knowledge of  MMF therapy in thoracic transplantation, and will ultimately benefit  the transplant recipients. 4.7 Reference s 1. Hale MD, Nicholls AJ, Bullingham RE, Hene R, Hoitsma A, Squifflet  JP, Weimar W, Vanrenterghem Y, Van de Woude FJ, Verpooten GA. The pharmacokinetic-pharmacodynamic relationship for  mycophenolate mofetil  in renal transplantation. Clin Pharmacol Ther. 1998; 64: 672-83. 2. Takahashi K, Ochiai T, Uchida K, Yasumura T, Ishibashi M, Suzuki S, Otsubo O, Isono K, Takagi H, Oka T, et al. Pilot study of  mycophenolate mofetil  (RS-61443) in the prevention of  acute rejection following  renal transplantation in Japanese patients. Transplant Proc 1995; 27: 1421-4. 3. Pillans PI, Rigby RJ, Kubler P, Willis C, Salm P, Tett SE, Taylor PJ. A retrospective analysis of  mycophenolic acid and cyclosporine concentrations with acute rejection in renal transplant recipients. Clin Biochem 2001; 34: 77-81. 4. Krumme B, Wollenbery K, Kirste G, Schollmeyer P. Drug monitoring of  mycophenolic acid in the early period after  renal transplantation. Transplant Proc 1998; 30: 1773-4. 5. Mourad M, Malaise J, Chaib Eddour D, De Meyer M, Konig J, Schepers R, Squifflet  JP, Wallemacq P. Correlation of  mycophenolic acid pharmacokinetic parameters with side effects  in kidney transplant patients treated with mycophenolate mofetil.  Clin Chem 2001;47:88-94. 6. Shaw LM, Nawrocki A, Korecka M, Solari S, Kang J. Using established immunosuppressive therapy effectively:  lessons from  the measurement of  mycophenolic acid plasma concentrations. Ther Drug Monit. 2004; 26: 347-51. 7. Filler G and Mai I. Limited sampling strategy for  mycophenolic acid area under the curve. Ther Drug Monit. 2000; 22: 169-73. 8. Le Guellec C, Bourgoin H, Buchler M, Le Meur Y, Lebranchu Y, Marquet P, Paintaud G. Population pharmacokinetics and Bayesian estimation of  mycophenolic acid concentrations in stable renal transplant patients. Clin Pharmacokinet. 2004; 43: 253 -66. 9. Pawinski T, Hale M, Korecka M, Fitzsimmons WE, Shaw LM. Limited sampling strategy for  the estimation of  mycophenolic acid area under the curve in adult renal transplant patients treated with concomitant tacrolimus. Clin Chem. 2002; 48: 1497 -504. 10. Willis C, Taylor P, Salm P, Tett SE, Pillans PI. Evaluation of  limited sampling strategies for  estimation of  12-hour mycophenolic acid area under the plasma concentration-time curve in adult renal transplant patients. Ther Drug Monit. 2000; 22: 549-54. 11. Kuriata-Kordek M, Boraynska M, Falkiewicz K, Falkiewicz K, Porazko T, Urbaniak J, Wozniak M, Patrzalek D, Szyber P, Klinger M. The influence  of  calcineurin inhibitors on mycophenolic acid pharmacokinetics. Transplant Proc. 2003; 35: 2369-71. 12. van Hest RM, Mathot RAA, Vulto AG, Le Meur Y, van Gelder T. Mycophenolic acid in diabetic renal transplant recipients, pharmacokinetics and application of  a limited sampling strategy. Ther Drug Monit. 2004; 26: 620-5. 13. Pierson RN 3rd, Barr ML, McCullough KP, Egan T, Garrity E, Jessup, M, Murray S. Thoracic organ transplantation. Am J Transplant. 2004;4 Suppl 9:93-105. 14. Rnoop C, Haverich A, Fischer S. Immunosuppressive therapy after  human lung transplantation. EurRespir J. 2004; 23: 159-71. transplanted organ(s). You would be prescribed mycophenolate as standard treatment even if  you did not participate in this study. Previous research studies have shown that different  people handle mycophenolate in their bodies differently.  Monitoring mycophenolate blood levels may help your doctor and pharmacist know what dose of  mycophenolate works best for  you. This means that knowing your mycophenolate blood levels may help determine whether there is enough drug in your body to prevent rejection but not too much to cause unwanted drug effects. Each person has a different  genetic make-up. Therefore,  one of  the possible explanations for why different  people handle mycophenolate differently  may be genetic variation in the enzymes used to break down this drug in the body. Purpose The purpose of  this study is to improve mycophenolate therapy in thoracic transplant subjects by finding  out how your body handles this drug. This study will determine if  a person's genetic make-up can explain why s/he may handle the drug differently  than another individual. To achieve this, 11 mycophenolate blood levels will be collected over a 12-hour period from  40 subjects. Study Procedures You will have the option of  participating in this study if  you are a thoracic transplant recipient, older than 16 years of  age, who is being treated with mycophenolate (Cellcept®). (See also Exclusions below). If  you choose to participate in the study, then the only procedure that will be different  than usual transplant care is obtaining blood samples once over a 12-hour period. You will be scheduled to visit the BC Transplant Society Office  research clinic as an outpatient for  your blood sampling. The study visit day will take approximately 13 hours. During the study visit day, you will be eating a standard breakfast,  lunch, and dinner. A study visit appointment will be made with you by a study nurse or one of  the investigators. You are asked not to eat anything after  midnight on the evening before  your morning appointment and report to the research clinic about an hour befor e your usual morning dose of  Cellcept®. When you arrive, the nurse will place a tiny catheter (identical to those used in the hospital after  your transplant) into a vein in your forearm.  This will allow easier blood collection and avoid having many "needle pokes" during the visit or you can choose to have "needle pokes" instead. If  you have a "central line", then all blood samples can be drawn from  the "central line" instead of  needing a catheter. You will have your first  blood sample collected right before  you take your usual morning dose of  Cellcept®. After  this dose of  Cellcept®, you will have 10 more blood samples taken later at 20, 40, 60, 90 minutes, and at 2, 4, 6, 8, 10, and 12 hours. All blood samples, except for  one, require only about 3 mL (or one-half teaspoonful)  each. An extra 20 mL (or 4 teaspoonsful)  for  the genetic analysis will be drawn during one of  the blood sampling times. Thus, a total of  about 53 mL (less than 2 ounces) of  blood will be collected during the study visit day. Physical activity will be limited to walking within the building. On the clinic visit day, a nurse will also complete a questionnaire with you to assess if  you have had any unwanted drug effects  with Cellcept®. Exclusions You must be excluded from  study participation if:  You refuse  to or are unable to give written informed  consent. a) You are younger than 16 years of  age. b) Your mycophenolate (Cellcept®) therapy is not at steady state. "Steady state" means that you must have taken Cellcept® for  at least 5 days without a dosage adjustment. c) You are taking other medications (e.g., antacids, cholestyramine, etc.) that can interact with mycophenolate. Risks The only risks associated with my participation in this study that are beyond  your risks if you were not to participate would be the risks related to blood collection and catheter placement. These risks are considered rare and mild but may include the following:  slight bruising, temporary feeling  of  faintness,  slight pain, and/or infection. There may also be other adverse reactions or risks that could arise which are not predictable. If  new information  arises during my involvement in this study which could affect  your desire to continue, you will be given such new information.  See New Findings. Benefits The direct benefits  to you as a participant of  this study cannot be guaranteed, but may include improving your doctor's understanding of  how your body handles Cellcept® when we provide your doctor with your study results. This information  may be used to provide dosage recommendations specific  for  you. Your participation is also expected to help find whether genetic variation in the enzymes used to break down mycophenolate in the body can explain differences  in handling of  this drug by different  people. Alternative Treatments If  you decide not to participate or to withdraw at some later date, you will continue to receive Cellcept® as standard treatment to prevent rejection. Confidentiality Your confidentiality  will be respected. No information  that discloses your identity will be released or published without your specific  consent to the disclosure. However, research records and medical records identifying  you may be inspected in the presence of  the investigator or his or her designate by representatives of  UBC F acuity o f  Pharmaceutical Sciences, Health Canada, and the UBC Research Ethics Board for  the purpose of  monitoring Appendix 2 - Example Nomogram for  Anti-log Conversion of Concentrations and AUCs The LSS using CO and C2 is used in this example: Log AUC = 0.241 Log CO + 0.406 Log C2 + 1.140 CO (Hg/mL) C2 (Hg/mL) Log CO Log C2 Calculated Log AUC Target: 30-60 Hg*h/mL Calculated AUC (jig*h/mL) 0.1 0.1 -1.000 -1.000 0.493 3.112 0.1 0.5 -1.000 -0.301 0.777 5.981 0.1 1 -1.000 0.000 0.899 7.925 0.1 2 -1.000 0.301 • 1.021 10.501 0.1 3 -1.000 0.477 1.093 12.380 0.1 4 -1.000 0.602 1.143 13.913 0.1 6 -1.000 0.778 1.215 16.403 0.1 8 -1.000 0.903 1.266 18.435 0.1 10 -1.000 1.000 1.305 20.184 0.1 15 -1.000 1.176 1.376 23.795 0.1 20 -1.000 1.301 1.427 26.743 0.5 0.1 -0.301 -1.000 0.661 4.586 0.5 0.5 -0.301 -0.301 0.945 8.815 0.5 1 -0.301 0.000 1.067 11.680 0.5 2 -0.301 0.301 1.190 15.476 0.5 3 -0.301 0.477 1.261 18.246 0.5 4 -0.301 0.602 1.312 20.506 0.5 6 -0.301 0.778 1.383 24.176 0.5 8 -0.301 0.903 1.434 27.171 0.5 10 -0.301 1.000 1.473 29.748 0.5 15 -0.301 1.176 1.545 35.071 0.5 20 -0.301 1.301 1.596 39.416 1 0.1 0.000 -1.000 0.734 5.420 1 0.5 0.000 -0.301 1.018 10.418 1 1 0.000 0.000 1.140 13.804 1 2 0.000 0.301 1.262 18.290 1 3 0.000 0.477 1.334 21.563 1 4 0.000 0.602 1.384 24.235 . 1 6 0.000 0.778 1.456 28.571 1 8 0.000 0.903 1.507 32.111 1 10 0.000 1.000 1.546 35.156 1 15 0.000 1.176 1.617 41.447 1 20 0.000 1.301 1.668 46.582 2 0.1 0.301 -1.000 0.807 6.405 2 0.5 0.301 -0.301 1.090 12.312 2 1 0.301 0.000 1.213 16.314 2 2 0.301 0.301 1.335 21.616 2 3 0.301 0.477 1.406 25.484 2 4 0.301 0.602 1.457 28.641 2 6 0.301 0.778 1.528 33.766 2 8 0.301 0.903 1.579 37.949 2 10 0.301 1.000 1.619 41.548 2 15 0.301 1.176 1.690 48.983 2 20 0.301 1.301 1.741 55.051 3 0.1 0.477 -1.000 0.849 7.063 3 0.5 0.477 -0.301 1.133 13.576 3 1 0.477 0.000 1.255 17.988 3 2 0.477 0.301 1.377 23.834 3 3 0.477 0.477 1.449 28.099 3 4 0.477 0.602 1.499 31.581 3 6 0.477 0.778 1.571 37.232 3 8 0.477 0.903 1.622 41.845 3 10 0.477 1.000 1.661 45.813 3 15 0.477 1.176 1.732 54.011 3 20 0.477 1.301 1.783 60.702 4 0.1 0.602 -1.000 0.879 7.570 4 0.5 0.602 -0.301 1.163 14.551 4 1 0.602 0.000 1.285 19.280 4 2 0.602 0.301 1.407 25.546 4 3 0.602 0.477 1.479 30.117 4 4 0.602 0.602 1.530 33.848 4 6 0.602 0.778 1.601 39.905 4 8 0.602 0.903 1.652 44.849 4 10 0.602 1.000 1.691 49.102 4 15 0.602 1.176 1.763 57.888 4 20 0.602 1.301 1.813 65.060 6 0.1 0.778 -1.000 0.922 8.347 6 0.5 0.778 -0.301 1.205 16.044 6 1 0.778 0.000 1.328 21.259 6 2 0.778 0.301 1.450 28.168 6 3 0.778 0.477 1.521 33.208 6 4 0.778 0.602 1.572 37.323 6 6 0.778 0.778 1.643 44.001 6 8 0.778 0.903 1.694 49.453 6 10 0.778 1.000 1.734 54.142 6 15 0.778 1.176 1.805 63.830 6 20 0.778 1.301 1.856 71.739 6 0.1 0.778 -1.000 0.922 8.347 6 0.5 0.778 -0.301 1.205 16.044 8 1 0.903 0.000 1.358 22.785 8 2 0.903 0.301 1.480 30.190 8 3 0.903 0.477 1.551 35.592 8 4 0.903 0.602 1.602 40.002 8 6 0.903 0.778 1.674 47.160 8 8 0.903 0.903 1.724 53.003 8 10 0.903 1.000 1.764 58.029 8 15 0.903 1.176 1.835 68.413 8 20 0.903 1.301 1.886 76.889 10 0.1 1.000 -1.000 0.975 9.441 10 0.5 1.000 -0.301 1.259 18.146 10 1 1.000 0.000 1.381 24.044 10 2 1.000 0.301 1.503 31.858 10 3 1.000 0.477 1.575 37.559 10 4 1.000 0.602 1.625 42.212 10 6 1.000 0.778 1.697 49.766 10 8 1.000 0.903 1.748 55.931 10 10 1.000 1.000 1.787 61.235 10 15 1.000 1.176 1.858 72.193 10 20 1.000 1.301 1.909 81.137 

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