Gombar Vijay K, Silver Ivin S, Zhao Zhiyang
Department of Drug Metabolism and Pharmacokinetics, Metabolic and Viral Diseases' Center of Excellence for Drug Discovery, GlaxoSmithKline, USA.
Curr Top Med Chem. 2003;3(11):1205-25. doi: 10.2174/1568026033452014.
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.
药物研发是一个漫长而艰巨的过程，大致可分为疾病靶点识别、靶点验证、高通量筛选“命中”和“先导”化合物、先导化合物优化以及临床前和临床评估。每个领域本身都是一门庞大的学科。然而，除了前两个阶段外，其他阶段都不同程度地涉及对作为潜在药物候选物的分子的吸收、分布、代谢、排泄（ADME）和毒性（T）的表征。约50%的研究性新药（IND）申请临床失败归因于其ADMET特性不足。因此，在当前社会和监管对医疗成本施加压力的环境下，制药行业寻求各种方法来尽量减少这种损耗也就不足为奇了。构建仅根据分子结构可靠预测ADMET特性的数学模型，即所谓的计算机模拟筛选，是这项降低成本以及缩短开发周期时间努力的核心。本文综述了ADME特性计算机模拟评估这一新兴领域。针对该领域所采用的不同方法，对其描述符、统计方法的范围和局限性以及可靠性进行了批判。例如，基于几何的描述符是否绝对必要，或者较低层次的结构量化是否同样有效？与具有严格统计诊断的最小二乘优化方法相比，人工神经网络等方法有哪些优势（如果有的话）？如果一个计算机模拟筛选没有经过充分验证，它是否值得应用，更不用说解读了？一旦被认为可以接受，如果一个计算机模拟筛选不能在跨国制药公司全球分布的药物研发团队的工作台上使用，那它又有什么用呢？这些不仅仅是讨论要点，相反，本文着手阐述开发一个成功的计算机模拟筛选的逐步机制。我们开发一个用于预测人S9肝匀浆试验中化学物质代谢稳定性的此类筛选的努力为例说明了这个过程。展示了这个计算机模拟筛选在葛兰素史克各种研发项目中的实际应用，突出了此类应用的成功和局限性。最后，我们预测了计算机模拟ADME工具的一些能力，以对成功、高效的药物研发产生更大影响并做出更大贡献。
