Steps Toward a Virtual Rat: Predictive Absorption, Distribution, Metabolism, and Toxicity Models
Yufeng Jane Tseng, Bo‐Han Su, Ming-Tsung Hsu, Olivia A. Lin · ACS symposium series · 2016
Predictive absorption, distribution, metabolism and toxicity models are promising tools to reduce the cost of preclinical safety screening in drug development processes. Traditionally, quantitative structure–activity relationship (QSAR)-based prediction models have a long-standing history of application for lead optimization on the drug development pipeline. With the advances in high-throughput screening techniques and public release of screening data, QSAR-based studies are no longer limited to a few analogs and lead optimization. This chapter focuses on the applications of predictive QSAR models in preclinical drug development. The key features of current QSAR practices, including molecular descriptors, machine learning methods, available databases, and the applications of various QSAR models of absorption, distribution, metabolism and toxicity studies, are reviewed and discussed.