Role of ADME Characteristics in Drug Discovery and Their In Silico Evaluation: In Silico Screening of Chemicals for their Metabolic Stability

生物信息学 广告 计算机科学 鉴定(生物学) 药物发现 药物开发 生化工程 计算生物学 风险分析(工程) 药品 生物信息学 药理学 生物 医学 工程类 基因 植物 生物化学
作者
Vijay K. Gombar,Ivin S. Silver,Zhiyang Zhao
出处
期刊:Current Topics in Medicinal Chemistry [Bentham Science Publishers]
卷期号:3 (11): 1205-1225 被引量:83
标识
DOI:10.2174/1568026033452014
摘要

Drug discovery is a long, arduous process broadly grouped into disease target identification, target validation, high-throughput identification of “hits” and “leads”, lead optimization, and pre-clinical and clinical evaluation. Each area is a vast discipline in itself. However, all but the first two stages involve, to varying degrees, the characterization of absorption, distribution, metabolism, excretion, (ADME), and toxicity (T) of the molecules being pursued as potential drug candidates. Clinical failures of about 50% of the Investigational New Drug (IND) filings are attributed to their inadequate ADMET attributes. It is, therefore, no surprise that, in the current climate of social and regulatory pressure on healthcare costs, the pharmaceutical industry is searching for any means to minimize this attrition. Building mathematical models, called in silico screens, to reliably predict ADMET attributes solely from molecular structure is at the heart of this effort in reducing costs as well as development cycle times. This article reviews the emerging field of in silico evaluation of ADME characteristics. For different approaches that have been employed in this area, a critique of the scope and limitations of their descriptors, statistical methods, and reliability are presented. For instance, are geometry-based descriptors absolutely essential or is lower-level structure quantification equally good? What advantages, if any, do we have for methods like artificial neural networks over the least squares optimization methods with rigorous statistical diagnostics? Is any in silico screen worth application, let alone interpretation, if it is not adequately validated? Once deemed acceptable, what good is an in silico screen if it cannot be made available at the workbench of drug discovery teams distributed across the globe throughout multi-national pharmaceutical companies? These are not mere discussion points, rather this article embarks on the stepwise mechanics of developing a successful in silico screen. The process is exemplified by our efforts in developing one such screen for predicting metabolic stability of chemicals in a human S9 liver homogenate assay. A real-life use of this in silico screen in a variety of discovery projects at GlaxoSmithKline is presented, highlighting successes and limitations of such applications. Finally, we project some capabilities of in silico ADME tools for greater impact and contribution to successful, efficient drug discovery. Keywords: in silico evaluation, In silico screening, metabolic stability, Investigational new drug, admet attributes
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