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The use of cheminformatics methods for predicting adverse drug responses by human androgen receptor

University of British Columbia

The human Androgen Receptor (AR) is a ligand-activated transcription factor that plays a pivotal role in the development and progression of prostate cancer (PCa). AR is also critical for the survival of many forms of castration resistant prostate cancer (CRPC). The currently used AR inhibitors (anti-androgens) face clinical limitations as drug resistance has been reported in patients, both primary and acquired. In 20% of the CRPC patients resistance to AR antagonists arise due to the mutations in the androgen binding site (ABS) of the receptor. Some mutations can convert antagonist to agonist. Such gain-of-function mutations have been reported across the length of the ligand binding domain (LBD) of AR that contains the ABS, it is imperative to develop a prognostic personalized therapy platform which would equip clinicians with actionable strategies in regard to previously unreported AR aberrations when they are encountered in clinical samples. The goal of this study is to develop a theoretical approach that can characterize such previously unreported AR mutants and predict their response to the currently used anti-androgens. Thus, a novel ‘in-silico’ pipeline has been created that amalgamates the state-of-the-art cheminformatics methods with experimental assays that enable predicting AR mutants and characterizing their drug responses with high accuracy. The corresponding pipeline utilizes QSAR approach that extracts key protein-ligand interactions quantified by the in-house developed 4D-inductive molecular descriptors. The developed QSAR models reach about 90% accuracy that forecasts agonist or antagonist behaviors of AR mutants caused by clinically used and experimental anti-androgens. Furthermore, a previously unreported mutant, T878G has been predicted to be activated by both first and second generation anti-androgens and the corresponding experimental evaluation confirmed this prediction. Finally, the applicability and adaptability of the developed cheminformatics pipeline was tested against an experimental anti-androgen drug ODM-201 which was not a part of the QSAR training dataset, and the predictions were confirmed by experimental evaluations. Overall, the developed pipeline can provide useful insights towards understanding the changing genomic landscape of advanced PCa.

Text

2017-01-21

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/59935

Bioinformatics

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/