ArticleCPT: pharmacometrics & systems pharmacology2023
Predicting changes in the pharmacokinetics of CYP3A-metabolized drugs in hepatic impairment and insights into factors driving these changes.
Article in CPT: pharmacometrics & systems pharmacology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 15 citations in OpenAlex.
- Evaluation of Pharmacokinetics and Safety of Imlunestrant in Participants with Hepatic Impairment.Journal of clinical pharmacology · 2026Article
- Physiologically based pharmacokinetic modeling for optimal dosage prediction of statins and warfarin in patients with liver cirrhosis.European journal of clinical pharmacology · 2026Article
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- Using Physiologically Based Pharmacokinetic Modeling and Simulations to Predict Antihypertensive Drug Doses in Cirrhotic Patients.Journal of clinical pharmacology · 2026Article
- Physiologically Based Pharmacokinetic Modeling in Patients With Hepatic Impairment: Are Changes in Bosutinib Exposure Profiles Driven by Altered Absorption or Distribution?CPT: pharmacometrics & systems pharmacology · 2026Article
- Pharmacokinetics of CYP2C19- and CYP3A4-Metabolized Drugs in Cirrhosis Using a Whole-Body PBPK Approach.Pharmaceutics · 2025Article
- Applications of PBPK Modeling to Estimate Drug Metabolism and Related ADME Processes in Specific Populations.Pharmaceutics · 2025Review
- Physiologically based pharmacokinetic modeling of small molecules: How much progress have we made?Drug metabolism and disposition: the biological fate of chemicals · 2025Review
- Article
- Phase 1 pharmacokinetic and safety study of soticlestat in participants with mild or moderate hepatic impairment or normal hepatic function.Pharmacology research & perspectives · 2024Article
- Simultaneously Predicting the Pharmacokinetics of CES1-Metabolized Drugs and Their Metabolites Using Physiologically Based Pharmacokinetic Model in Cirrhosis Subjects.Pharmaceutics · 2024Article
- Toward improved predictions of pharmacokinetics of transported drugs in hepatic impairment: Insights from the extended clearance model.CPT: pharmacometrics & systems pharmacology · 2024Article
- Innovations, Opportunities, and Challenges for Predicting Alteration in Drug-Metabolizing Enzyme and Transporter Activity in Specific Populations.Drug metabolism and disposition: the biological fate of chemicals · 2023Article
- Predicting changes in the pharmacokinetics of CYP3A-metabolized drugs in hepatic impairment and insights into factors driving these changes.CPT: pharmacometrics & systems pharmacology · 2023Article
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.