Utility of Quantitative Proteomics for Enhancing the Predictive Ability of Physiologically Based Pharmacokinetic Models Across Disease States

Abstract

Disease states such as liver cirrhosis and chronic kidney disease can lead to altered pharmacokinetics (PK) of drugs by influencing drug absorption, blood flow to organs, plasma protein binding, apparent volume of distribution, and drug-metabolizing enzyme and transporter (DMET) abundance. Narrow therapeutic index drugs are particularly vulnerable to undesired pharmacodynamics (PD) because of the changes in drug PK in disease states. However, systematic clinical evaluation of disease effect on drug PK and PD is not always possible because of the complexity or the cost of clinical studies. Physiologically based PK (PBPK) modeling is emerging as an alternate method to extrapolate drug PK from the healthy population to disease states. These models require information on the effect of disease condition on the activity or tissue abundance of DMET proteins. Although immunoquantification-based abundance data were available in the literature for a limited number of DMET proteins, the emergence of mass spectrometry-based quantitative proteomics as a sensitive, robust, and high-throughput tool has allowed a rapid increase in data availability on tissue DMET abundance in healthy versus disease states, especially in liver tissue. Here, we summarize these data including the available immunoquantification or mRNA levels of DMET proteins (healthy vs disease states) in extrahepatic tissue and discuss the potential applications of DMET abundance data in enhancing the capability of PBPK modeling in predicting drug disposition across disease states. Successful examples of PBPK modeling that integrate differences in DMET proteins between healthy and disease states are discussed.

Keywords: PBPK; PBPK modeling; disease states; liver disease; pharmacokinetics and drug metabolism; quantitative proteomics; special populations.