ArticleClinical pharmacokinetics2016
Quantitative Prediction of Drug Interactions Caused by CYP1A2 Inhibitors and Inducers.
Article in Clinical pharmacokinetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 26 citations in OpenAlex.
- Contraindicated drug-drug interactions and associated adverse drug reactions in an observational cohort study of 4543 paediatric hospitalized patients.British journal of clinical pharmacology · 2026Observational
- 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
- Computational Profiling of Monoterpenoid Phytochemicals: Insights for Medicinal Chemistry and Drug Design Strategies.International journal of molecular sciences · 2025Article
- 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
- Quantitative Prediction of Drug Interactions Caused by Cytochrome P450 2B6 Inhibition or Induction.Clinical pharmacokinetics · 2022Article
- In vitro evaluation of the inhibition potential of echinacoside on human cytochrome P450 isozymes.BMC complementary medicine and therapies · 2022Article
- Intravenous Lidocaine and Ketamine Infusions for Headache Disorders: A Retrospective Cohort Study.Frontiers in neurology · 2022Article
- Model-Based Comparative Analysis of Rifampicin and Rifabutin Drug-Drug Interaction Profile.Antimicrobial agents and chemotherapy · 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
- Impact of switching to a heat-not-burn tobacco product on CYP1A2 activity.Toxicology reports · 2020Article
- A Generic Model for Quantitative Prediction of Interactions Mediated by Efflux Transporters and Cytochromes: Application to P-Glycoprotein and Cytochrome 3A4.Clinical pharmacokinetics · 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
- Safety of Pirfenidone in Patients with Idiopathic Pulmonary Fibrosis: Experience from 92 Sites in an Open-Label US Expanded Access Program.Pulmonary therapy · 2017Article
- A Model for Predicting the Interindividual Variability of Drug-Drug Interactions.The AAPS journal · 2017Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA simple method to predict drug-drug interactions mediated by cytochrome P450 enzymes (CYPs) on the basis of in vivo data has been previously applied for several CYP isoforms but not for CYP1A2. The objective of this study was to extend this method to drug interactions caused by CYP1A2 inhibitors and inducers.
methodsFirst, initial estimates of the model parameters were obtained using data from the literature. Then, an external validation of these initial estimates was performed by comparing model-based predicted area under the concentration-time curve (AUC) ratios with observations not used in the initial estimation. Third, refined estimates of the model parameters were obtained by Bayesian orthogonal regression using Winbugs software, and predicted AUC ratios were compared with all available observations. Finally, predicted AUC ratios for all possible substrates-inhibitors and substrates-inducers were computed.
resultsA total of 100 AUC ratios were retrieved from the literature. Model parameters were estimated for 19 CYP1A2 substrate drugs, 26 inhibitors and seven inducers, including tobacco smoking. In the external validation, the mean prediction error of the AUC ratios was -0.22, while the mean absolute error was 0.97 (37 %). After the Bayesian estimation step, the mean prediction error was 0.11, while the mean absolute error was 0.43 (22 %). The AUC ratios for 625 possible interactions were computed.
conclusionThis analysis provides insights into the interaction profiles of drugs poorly studied so far and can help to identify and manage significant interactions in clinical practice. Those results are now available to the community via a web tool ( http://www.ddi-predictor.org ).
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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 OpenQuestion graph.