Predicting changes in the pharmacokinetics of CYP3A‐metabolized drugs in hepatic impairment and insights into factors driving these changes

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. Study Highlights WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC?The ability of physiologically based pharmacokinetic (PBPK) models, populated with hepatic and intestinal drug-metabolizing enzyme and transporter

Incorporation and Performance Verification of Hepatic Portal Blood Flow Shunting in Minimal and Full PBPK Models of Liver Cirrhosis

“…This was corroborated in a recent study looking at the performance of the PBPK models in HVs and cirrhotic subjects with i.v. and oral CYP 3A4 substrates with a range of hepatic CL values 28 …”

“…and oral CYP 3A4 substrates with a range of hepatic CL values. 28 As well as increased bioavailability due to shunting, ibrutinib, midazolam, and buspirone are all significant substrates for intestinal CYP3A4, with increased AUC and C max reported due to co-administered grapefruit juice. [29][30][31] A decrease in intestinal CYP3A4 activity in cirrhosis has been demonstrated with a 39% and 52% reduction in CP-B and CP-C, respectively.…”

Simultaneously Predicting the Pharmacokinetics of CES1-Metabolized Drugs and Their Metabolites Using Physiologically Based Pharmacokinetic Model in Cirrhosis Subjects

“…The PBPK model was considered to be successful if the simulated AUC or C max fell within 0.5- to 2-fold of the observed data or the observed data were within the 5th and 95th percentiles of the simulation derived from 1000 virtual subjects [ 101 ].…”

Toward improved predictions of pharmacokinetics of transported drugs in hepatic impairment: Insights from the extended clearance model

“…Physiological changes, other than transporter abundance and tissue volume scalar, were kept the same as those in the population library in Simcyp simulator version 21. Population‐specific physiological parameters are summarized elsewhere 11 . Root mean square error and mean error were used to compare performances in terms of precision and bias, respectively.…”

“…However, before using such PBPK models with confidence, their prediction performance in HI needs to be assessed. Whereas PBPK M&S studies assessing the effect of HI on drugs that are metabolized by cytochromes P450 (CYP) are available, 10,11 there are limited data on the predictive performance of such models for drugs that are substrates of hepatic drug transporters, such as the sinusoidal organic anion transporting polypeptides (OATPs) and the ABC efflux transporters, such as P‐glycoprotein (P‐gp), Breast Cancer Resistance Protein (BCRP), or multidrug resistance proteins 2, 3, or 4 (MRP2/3/4) 12 . These transporters, as well as hepatic metabolism, can affect the systemic exposure and response of many drugs, including cholesterol‐lowering (e.g., HMG‐CoA reductase inhibitors), antihypertensive (e.g., angiotensin II receptor blockers), antiviral (e.g., NS3/4A protease inhibitors), and antidiabetic (e.g., meglitinides) drugs 13–15 …”