Systems Modeling and Translational Biology, Computational Sciences, GlaxoSmithKline, 1250 South Collegeville Road, Collegeville, Pennsylvania 19426, United States.
J Med Chem. 2021 Mar 25;64(6):3185-3196. doi: 10.1021/acs.jmedchem.0c02033. Epub 2021 Mar 15.
The optimal pharmacokinetic (PK) required for a drug candidate to elicit efficacy is highly dependent on the targeted pharmacology, a relationship that is often not well characterized during early phases of drug discovery. Generic assumptions around PK and potency risk misguiding screening and compound design toward nonoptimal absorption, distribution, metabolism, and excretion (ADME) or molecular properties and ultimately may increase attrition as well as hit-to-lead and lead optimization timelines. The present work introduces model-based target pharmacology assessment (mTPA), a computational approach combining physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling, sensitivity analysis, and machine learning (ML) to elucidate the optimal combination of PK, potency, and ADME specific for the targeted pharmacology. Examples using frequently encountered PK/PD relationships are presented to illustrate its application, and the utility and benefits of deploying such an approach to guide early discovery efforts are discussed.
对于候选药物来说，发挥疗效所需的最佳药代动力学（PK）在很大程度上取决于其目标药理学，而在药物发现的早期阶段，这种关系往往没有得到很好的描述。在 PK 和效力方面的一般性假设可能会导致筛选和化合物设计误入歧途，不利于吸收、分布、代谢和排泄（ADME）或分子特性，最终可能会增加药物淘汰率，以及增加从苗头化合物到先导化合物优化和先导化合物优化的时间。本工作介绍了基于模型的靶标药理学评估（mTPA），这是一种结合了基于生理的药代动力学/药效动力学（PBPK/PD）建模、敏感性分析和机器学习（ML）的计算方法，用于阐明针对特定靶标药理学的 PK、效力和 ADME 的最佳组合。本文使用了常见的 PK/PD 关系示例来说明其应用，并讨论了采用这种方法来指导早期发现工作的实用性和好处。
