体内
均方误差
药代动力学
药物发现
生物利用度
广告
机器学习
计算机科学
外推法
人工智能
药理学
生物系统
化学
数学
统计
生物
生物化学
生物技术
作者
Olga Obrezanova,Anton Martinsson,Thomas M. Whitehead,Samar Mahmoud,Andreas Bender,Filip Miljković,Piotr Grabowski,Ben Irwin,Ioana Oprisiu,G. J. Conduit,Matthew Segall,G. Smith,Beth Williamson,Susanne Winiwarter,Nigel Greene
标识
DOI:10.1021/acs.molpharmaceut.2c00027
摘要
Animal pharmacokinetic (PK) data as well as human and animal in vitro systems are utilized in drug discovery to define the rate and route of drug elimination. Accurate prediction and mechanistic understanding of drug clearance and disposition in animals provide a degree of confidence for extrapolation to humans. In addition, prediction of in vivo properties can be used to improve design during drug discovery, help select compounds with better properties, and reduce the number of in vivo experiments. In this study, we generated machine learning models able to predict rat in vivo PK parameters and concentration-time PK profiles based on the molecular chemical structure and either measured or predicted in vitro parameters. The models were trained on internal in vivo rat PK data for over 3000 diverse compounds from multiple projects and therapeutic areas, and the predicted endpoints include clearance and oral bioavailability. We compared the performance of various traditional machine learning algorithms and deep learning approaches, including graph convolutional neural networks. The best models for PK parameters achieved R2 = 0.63 [root mean squared error (RMSE) = 0.26] for clearance and R2 = 0.55 (RMSE = 0.46) for bioavailability. The models provide a fast and cost-efficient way to guide the design of molecules with optimal PK profiles, to enable the prediction of virtual compounds at the point of design, and to drive prioritization of compounds for in vivo assays.
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