Caldwell Gary W
Janssen Research & Development LLC, Discovery Sciences , Spring House, PA , USA
Expert Opin Drug Discov. 2015;10(8):901-23. doi: 10.1517/17460441.2015.1043885. Epub 2015 May 8.
The target-based drug discovery process, including target selection, screening, hit-to-lead (H2L) and lead optimization stage gates, is the most common approach used in drug development. The full integration of in vitro and/or in vivo data with in silico tools across the entire process would be beneficial to R&D productivity by developing effective selection criteria and drug-design optimization strategies.
This review focuses on understanding the impact and extent in the past 5 years of in silico tools on the various stage gates of the target-based drug discovery approach.
There are a large number of in silico tools available for establishing selection criteria and drug-design optimization strategies in the target-based approach. However, the inconsistent use of in vitro and/or in vivo data integrated with predictive in silico multiparameter models throughout the process is contributing to R&D productivity issues. In particular, the lack of reliable in silico tools at the H2L stage gate is contributing to the suboptimal selection of viable lead compounds. It is suggested that further development of in silico multiparameter models and organizing biologists, medicinal and computational chemists into one team with a single accountable objective to expand the utilization of in silico tools in all phases of drug discovery would improve R&D productivity.
基于靶点的药物发现过程，包括靶点选择、筛选、从苗头化合物到先导化合物（H2L）以及先导化合物优化阶段关卡，是药物开发中最常用的方法。在整个过程中，将体外和/或体内数据与计算机工具进行全面整合，通过制定有效的选择标准和药物设计优化策略，将有利于提高研发效率。
本综述着重于了解计算机工具在过去五年中对基于靶点的药物发现方法的各个阶段关卡所产生的影响及程度。
有大量计算机工具可用于在基于靶点的方法中建立选择标准和药物设计优化策略。然而，在整个过程中，体外和/或体内数据与预测性计算机多参数模型的使用不一致，这导致了研发效率问题。特别是，在H2L阶段关卡缺乏可靠的计算机工具，导致可行先导化合物的选择不够理想。建议进一步开发计算机多参数模型，并将生物学家、药物化学家和计算化学家组织成一个团队，以单一的可问责目标在药物发现的所有阶段扩大计算机工具的应用，这将提高研发效率。
