Literature DB >> 24048277

Evaluation of various static in vitro-in vivo extrapolation models for risk assessment of the CYP3A inhibition potential of an investigational drug.

Md L T Vieira1, B Kirby2, I Ragueneau-Majlessi2, A Galetin3, J Y L Chien4, H J Einolf5, O A Fahmi6, V Fischer7, A Fretland4, K Grime8, S D Hall4, R Higgs4, D Plowchalk6, R Riley9, E Seibert10, K Skordos11, J Snoeys12, K Venkatakrishnan13, T Waterhouse4, R S Obach6, E G Berglund14, L Zhang1, P Zhao1, K S Reynolds1, S-M Huang1.   

Abstract

Nine static models (seven basic and two mechanistic) and their respective cutoff values used for predicting cytochrome P450 3A (CYP3A) inhibition, as recommended by the US Food and Drug Administration and the European Medicines Agency, were evaluated using data from 119 clinical studies with orally administered midazolam as a substrate. Positive predictive error (PPE) and negative predictive error (NPE) rates were used to assess model performance, based on a cutoff of 1.25-fold change in midazolam area under the curve (AUC) by inhibitor. For reversible inhibition, basic models using total or unbound systemic inhibitor concentration [I] had high NPE rates (46-47%), whereas those using intestinal luminal ([I]gut) values had no NPE but a higher PPE. All basic models for time-dependent inhibition had no NPE and reasonable PPE rates (15-18%). Mechanistic static models that incorporate all interaction mechanisms and organ specific [I] values (enterocyte and hepatic inlet) provided a higher predictive precision, a slightly increased NPE, and a reasonable PPE. Various cutoffs for predicting the likelihood of CYP3A inhibition were evaluated for mechanistic models, and a cutoff of 1.25-fold change in midazolam AUC appears appropriate.

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Year:  2013        PMID: 24048277     DOI: 10.1038/clpt.2013.187

Source DB:  PubMed          Journal:  Clin Pharmacol Ther        ISSN: 0009-9236            Impact factor:   6.875


  18 in total

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Journal:  J Pharmacol Exp Ther       Date:  2014-09-24       Impact factor: 4.030

2.  Predictive Performance of Physiologically-Based Pharmacokinetic Models in Predicting Drug-Drug Interactions Involving Enzyme Modulation.

Authors:  Chia-Hsiang Hsueh; Vicky Hsu; Yuzhuo Pan; Ping Zhao
Journal:  Clin Pharmacokinet       Date:  2018-10       Impact factor: 6.447

3.  Evaluation of drug-drug interactions for oncology therapies: in vitro-in vivo extrapolation model-based risk assessment.

Authors:  Nigel J Waters
Journal:  Br J Clin Pharmacol       Date:  2015-06       Impact factor: 4.335

4.  In vitro to in vivo extrapolation of the complex drug-drug interaction of bupropion and its metabolites with CYP2D6; simultaneous reversible inhibition and CYP2D6 downregulation.

Authors:  Jennifer E Sager; Sasmita Tripathy; Lauren S L Price; Abhinav Nath; Justine Chang; Alyssa Stephenson-Famy; Nina Isoherranen
Journal:  Biochem Pharmacol       Date:  2016-11-09       Impact factor: 5.858

5.  Evaluation of vatiquinone drug-drug interaction potential in vitro and in a phase 1 clinical study with tolbutamide, a CYP2C9 substrate, and omeprazole, a CYP2C19 substrate, in healthy subjects.

Authors:  Katsuyuki Murase; Lucy Lee; Jiyuan Ma; Rosemary Barrett; Martin Thoolen
Journal:  Eur J Clin Pharmacol       Date:  2022-09-27       Impact factor: 3.064

6.  Mechanism-Based Inhibition of CYP3A4 by Podophyllotoxin: Aging of an Intermediate Is Important for in Vitro/in Vivo Correlations.

Authors:  Carlo Barnaba; Jaydeep Yadav; Swati Nagar; Ken Korzekwa; Jeffrey P Jones
Journal:  Mol Pharm       Date:  2016-07-01       Impact factor: 4.939

7.  Physiologically based pharmacokinetic modeling of impaired carboxylesterase-1 activity: effects on oseltamivir disposition.

Authors:  Zhe-Yi Hu; Andrea N Edginton; S Casey Laizure; Robert B Parker
Journal:  Clin Pharmacokinet       Date:  2014-09       Impact factor: 6.447

8.  How Science Is Driving Regulatory Guidances.

Authors:  Xinning Yang; Jianghong Fan; Lei Zhang
Journal:  Methods Mol Biol       Date:  2021

9.  Improved Predictions of Drug-Drug Interactions Mediated by Time-Dependent Inhibition of CYP3A.

Authors:  Jaydeep Yadav; Ken Korzekwa; Swati Nagar
Journal:  Mol Pharm       Date:  2018-04-10       Impact factor: 4.939

Review 10.  Time-dependent enzyme inactivation: Numerical analyses of in vitro data and prediction of drug-drug interactions.

Authors:  Jaydeep Yadav; Erickson Paragas; Ken Korzekwa; Swati Nagar
Journal:  Pharmacol Ther       Date:  2019-12-11       Impact factor: 12.310

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