ADME Optimization and Toxicity Assessment in Early- and Late-Phase Drug Discovery
Gary W. Caldwell, Zhengyin Yan, Weimin Tang, Malini Dasgupta, Becki Hasting
Abstract
Gary W. Caldwell, Zhengyin Yan, Weimin Tang, Malini Dasgupta, Becki Hasting
Abstract
Integrating physicochemical, drug metabolism, pharmacokinetics, ADME, and toxicity assays into drug discovery in order to reduce the attrition rates in clinical development is reviewed. The review is organized around three main decision points used in discovery including hit generation, lead optimization and final candidate selection stages. The preclinical strategies used at each decision point are discussed from a drug discovery perspective. Typically, preclinical data produced at these stages use lower throughput assays, smaller amounts of compounds and operate within a timeframe that is consistent with the iterative cycle of most drug discovery research projects. Understanding the false positive rates of these drug discovery preclinical assays is a must in reducing attrition rates in development.
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Integrating physicochemical, drug metabolism, pharmacokinetics, ADME, and toxicity assays into drug discovery in order to reduce the attrition rates in clinical development is reviewed. The review is organized around three main decision points used in discovery including hit generation, lead optimization and final candidate selection stages. The preclinical strategies used at each decision point are discussed from a drug discovery perspective. Typically, preclinical data produced at these stages use lower throughput assays, smaller amounts of compounds and operate within a timeframe that is consistent with the iterative cycle of most drug discovery research projects. Understanding the false positive rates of these drug discovery preclinical assays is a must in reducing attrition rates in development.
Key concepts: ADME, Drug discovery, Drug development, Drug, Pharmacology, Pharmacokinetics, Medicine, Computational biology