Predicting changes in the pharmacokinetics of CYP3A‐metabolized drugs in hepatic impairment and insights into factors driving these changes. (20th December 2022)

• Record Type: Journal Article
• Authors: Ladumor, Mayur K.; Storelli, Flavia; Liang, Xiaomin; Lai, Yurong; Enogieru, Osatohanmwen J.; Chothe, Paresh P.; Evers, Raymond; Unadkat, Jashvant D.
• Is Part Of: CPT: pharmacometrics & systems pharmacology. Volume 12:Number 2(2023)
• Page Start: 261
• Page End: 273
• Publication Date: 2022-12-20
• Subjects: Pharmacokinetics -- Periodicals; Pharmacology -- Periodicals; Pharmacokinetics; Periodicals; 615.05
• DOI: 10.1002/psp4.12901
• Languages: English
• ISSNs: 2163-8306

Abstract

Abstract: Physiologically based pharmacokinetic models, populated with drug‐metabolizing enzyme and transporter (DMET) abundance, can be used to predict the impact of hepatic impairment (HI) on the pharmacokinetics (PK) of drugs. To increase confidence in the predictive power of such models, they must be validated by comparing the predicted and observed PK of drugs in HI obtained by phenotyping (or probe drug) studies. Therefore, we first predicted the effect of all stages of HI (mild to severe) on the PK of drugs primarily metabolized by cytochrome P450 (CYP) 3A enzymes using the default HI module of Simcyp Version 21, populated with hepatic and intestinal CYP3A abundance data. Then, we validated the predictions using CYP3A probe drug phenotyping studies conducted in HI. Seven CYP3A substrates, metabolized primarily via CYP3A (fraction metabolized, 0.7–0.95), with low to high hepatic availability, were studied. For all stages of HI, the predicted PK parameters of drugs were within twofold of the observed data. This successful validation increases confidence in using the DMET abundance data in HI to predict the changes in the PK of drugs cleared by DMET for which phenotyping studies in HI are not available or cannot be conducted. In addition, using CYP3A drugs as an example, through simulations, we identified the salient PK factors that drive the major changes in exposure (area under the plasma concentration–time profile curve) to drugs in HI. This theoretical framework can be applied to any drug and DMET to quickly determine the likely magnitude of change in drug PK due to HI.