Quantitative Proteomics in Translational Absorption, Distribution, Metabolism, and Excretion and Precision Medicine.

Abstract

A reliable translation of in vitro and preclinical data on drug absorption, distribution, metabolism, and excretion (ADME) to humans is important for safe and effective drug development. Precision medicine that is expected to provide the right clinical dose for the right patient at the right time requires a comprehensive understanding of population factors affecting drug disposition and response. Characterization of drug-metabolizing enzymes and transporters for the protein abundance and their interindividual as well as differential tissue and cross-species variabilities is important for translational ADME and precision medicine. This review first provides a brief overview of quantitative proteomics principles including liquid chromatography-tandem mass spectrometry tools, data acquisition approaches, proteomics sample preparation techniques, and quality controls for ensuring rigor and reproducibility in protein quantification data. Then, potential applications of quantitative proteomics in the translation of in vitro and preclinical data as well as prediction of interindividual variability are discussed in detail with tabulated examples. The applications of quantitative proteomics data in physiologically based pharmacokinetic modeling for ADME prediction are discussed with representative case examples. Finally, various considerations for reliable quantitative proteomics analysis for translational ADME and precision medicine and the future directions are discussed. SIGNIFICANCE STATEMENT: Quantitative proteomics analysis of drug-metabolizing enzymes and transporters in humans and preclinical species provides key physiological information that assists in the translation of in vitro and preclinical data to humans. This review provides the principles and applications of quantitative proteomics in characterizing in vitro, ex vivo, and preclinical models for translational research and interindividual variability prediction. Integration of these data into physiologically based pharmacokinetic modeling is proving to be critical for safe, effective, timely, and cost-effective drug development.