Cheminformatics Modeling of Adverse Drug Responses by Clinically Relevant Mutants of Human Androgen Receptor
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- 作者：Naman Paul ; Lavinia A. Carabet ; Nada Lallous ; Takeshi Yamazaki ; Martin E. Gleave ; Paul S. Rennie ; Artem Cherkasov
- 刊名：Journal of Chemical Information and Modeling
- 出版年：2016
- 出版时间：December 27, 2016
- 年：2016
- 卷：56
- 期：12
- 页码：2507-2516
- 全文大小：536K
- ISSN：1549-960X
文摘
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). Many forms of castration-resistant prostate cancer (CRPC) still rely on the AR for survival. Currently used antiandrogens face clinical limitations as drug resistance develops in patients over time since they all target the mutation-prone androgen binding site (ABS), where gain-of-function mutations eventually convert antagonists into agonists. With a significant number of reported distinct mutations located across the ABS, it is imperative to develop a prognostic platform which would equip clinicians with prior knowledge and actionable strategies if cases of previously unreported AR mutations are encountered. The goal of this study is to develop a theoretical approach that can predict such previously unreported AR mutants in response to current treatment options for PCa. The expected drug response by these mutants has been modeled using cheminformatics methodology. The corresponding QSAR pipeline has been created, which extracts key protein–ligand interactions and quantifies them by 4D molecular descriptors. The developed models reported with an accuracy reaching 90% and enable prediction of activation of AR mutants by its native ligand as well as assess whether known antiandrogens will act on them as agonists or antagonists. As a result, a previously uncharacterized mutant, T878G, has been predicted to be activated by the latest antiandrogen enzalutamide, and the corresponding experimental evaluation confirmed this prediction. Overall, the developed cheminformatics pipeline provides useful insights toward understanding the changing genomic landscape of advanced PCa.