ArticleThe AAPS journal2013
In vivo quantitative prediction of the effect of gene polymorphisms and drug interactions on drug exposure for CYP2C19 substrates.
Article in The AAPS journal, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 43 citations in OpenAlex.
- Quantitative prediction of intravenous drug interactions caused by cytochromes P450 inhibitors and inducers.British journal of clinical pharmacology · 2026Article
- A Genetic Algorithm-Based Approach for Quantitative Prediction of Drug-Drug Interactions Caused by Cytochrome P450 3A Inhibition or Induction in Horses.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Effects of total saikosaponins on CYP3A4 and CYP1A2 in HepaRG cells.Experimental and therapeutic medicine · 2024Article
- Integrated Use of In Vitro and In Vivo Information for Comprehensive Prediction of Drug Interactions Due to Inhibition of Multiple CYP Isoenzymes.Clinical pharmacokinetics · 2023Article
- Evaluation of Renal Impairment Influence on Metabolic Drug Clearance using a Modelling Approach.Clinical pharmacokinetics · 2023Article
- The impact ofFrontiers in pharmacology · 2023Article
- Quantitative Prediction of Drug Interactions Caused by Cytochrome P450 2B6 Inhibition or Induction.Clinical pharmacokinetics · 2022Article
- Model-Based Comparative Analysis of Rifampicin and Rifabutin Drug-Drug Interaction Profile.Antimicrobial agents and chemotherapy · 2021Article
- Interaction between Omeprazole and Gliclazide in Relation to CYP2C19 Phenotype.Journal of personalized medicine · 2021Article
- Modeling Approach to Predict the Impact of Inflammation on the Pharmacokinetics of CYP2C19 and CYP3A4 Substrates.Pharmaceutical research · 2021Article
- Quantitative Prediction of Interactions Mediated by Transporters and Cytochromes: Application to Organic Anion Transporting Polypeptides, Breast Cancer Resistance Protein and Cytochrome 2C8.Clinical pharmacokinetics · 2020Article
- Genetics and Extracellular Vesicles of Pediatrics Sleep Disordered Breathing and Epilepsy.International journal of molecular sciences · 2019Review
- A Generic Model for Quantitative Prediction of Interactions Mediated by Efflux Transporters and Cytochromes: Application to P-Glycoprotein and Cytochrome 3A4.Clinical pharmacokinetics · 2019Article
- Integrating anatomo-physiological changes and pharmacogenomics in anti-infective therapy management: is it a major concern?British journal of clinical pharmacology · 2019Article
- Identification of Cytochrome P450-Mediated Drug-Drug Interactions at Risk in Cases of Gene Polymorphisms by Using a Quantitative Prediction Model.Clinical pharmacokinetics · 2018Article
- Semi-Mechanistic Model for Predicting the Dosing Rate in Children and Neonates for Drugs Mainly Eliminated by Cytochrome Metabolism.Clinical pharmacokinetics · 2018Article
- A Model for Predicting the Interindividual Variability of Drug-Drug Interactions.The AAPS journal · 2017Article
- Quantitative Prediction of Drug Interactions Caused by CYP1A2 Inhibitors and Inducers.Clinical pharmacokinetics · 2016Article
- A Prediction Model of Drug Exposure in Cirrhotic Patients According to Child-Pugh Classification.Clinical pharmacokinetics · 2015Article
- Variability of voriconazole plasma concentrations after allogeneic hematopoietic stem cell transplantation: impact of cytochrome p450 polymorphisms and comedications on initial and subsequent trough levels.Antimicrobial agents and chemotherapy · 2015Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present a unified quantitative approach to predict the in vivo alteration in drug exposure caused by either cytochrome P450 (CYP) gene polymorphisms or CYP-mediated drug-drug interactions (DDI). An application to drugs metabolized by CYP2C19 is presented. The metrics used is the ratio of altered drug area under the curve (AUC) to the AUC in extensive metabolizers with no mutation or no interaction. Data from 42 pharmacokinetic studies performed in CYP2C19 genetic subgroups and 18 DDI studies were used to estimate model parameters and predicted AUC ratios by using Bayesian approach. Pharmacogenetic information was used to estimate a parameter of the model which was then used to predict DDI. The method adequately predicted the AUC ratios published in the literature, with mean errors of -0.15 and -0.62 and mean absolute errors of 0.62 and 1.05 for genotype and DDI data, respectively. The approach provides quantitative prediction of the effect of five genotype variants and 10 inhibitors on the exposure to 25 CYP2C19 substrates, including a number of unobserved cases. A quantitative approach for predicting the effect of gene polymorphisms and drug interactions on drug exposure has been successfully applied for CYP2C19 substrates. This study shows that pharmacogenetic information can be used to predict DDI. This may have important implications for the development of personalized medicine and drug development.
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