Literature DB >> 24556783

PBPK model describes the effects of comedication and genetic polymorphism on systemic exposure of drugs that undergo multiple clearance pathways.

M D L T Vieira1, M-J Kim1, S Apparaju1, V Sinha1, I Zineh1, S-M Huang1, P Zhao1.   

Abstract

An important goal in drug development is to understand the effects of intrinsic and/or extrinsic factors (IEFs) on drug pharmacokinetics. Although clinical studies investigating a given IEF can accomplish this goal, they may not be feasible for all IEFs or for situations when multiple IEFs exist concurrently. Physiologically based pharmacokinetic (PBPK) models may serve as a complementary tool for forecasting the effects of IEFs. We developed PBPK models for four drugs that are eliminated by both cytochrome P450 (CYP)3A4 and CYP2D6, and evaluated model prediction of the effects of comedications and/or genetic polymorphism on drug exposure. PBPK models predicted 100 and ≥70% of the observed results when the conventional "twofold rule" and the more conservative 25% deviation cut point were applied, respectively. These findings suggest that PBPK models can be used to infer effects of individual or combined IEFs and should be considered to optimize studies that evaluate these factors, specifically drug interactions and genetic polymorphism of drug-metabolizing enzymes.

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Year:  2014        PMID: 24556783     DOI: 10.1038/clpt.2014.43

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


  22 in total

1.  Predicting the effect of cytochrome P450 inhibitors on substrate drugs: analysis of physiologically based pharmacokinetic modeling submissions to the US Food and Drug Administration.

Authors:  Christian Wagner; Yuzhuo Pan; Vicky Hsu; Joseph A Grillo; Lei Zhang; Kellie S Reynolds; Vikram Sinha; Ping Zhao
Journal:  Clin Pharmacokinet       Date:  2015-01       Impact factor: 6.447

2.  Prediction of Drug Clearance from Enzyme and Transporter Kinetics.

Authors:  Priyanka R Kulkarni; Amir S Youssef; Aneesh A Argikar
Journal:  Methods Mol Biol       Date:  2021

3.  Physiologically-based pharmacokinetic modelling of a CYP2C19 substrate, BMS-823778, utilizing pharmacogenetic data.

Authors:  Jiachang Gong; Lisa Iacono; Ramaswamy A Iyer; William G Humphreys; Ming Zheng
Journal:  Br J Clin Pharmacol       Date:  2018-04-10       Impact factor: 4.335

4.  Discontinuities and disruptions in drug dosage guidelines for the paediatric population.

Authors:  Kate M Chitty; Bosco Chan; Camille L Pulanco; Sonya Luu; Oluwaseun Egunsola; Nicholas A Buckley
Journal:  Br J Clin Pharmacol       Date:  2018-02-21       Impact factor: 4.335

5.  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

Review 6.  Complex Drug-Drug-Gene-Disease Interactions Involving Cytochromes P450: Systematic Review of Published Case Reports and Clinical Perspectives.

Authors:  Flavia Storelli; Caroline Samer; Jean-Luc Reny; Jules Desmeules; Youssef Daali
Journal:  Clin Pharmacokinet       Date:  2018-10       Impact factor: 6.447

7.  Human hepatocytes and cytochrome P450-selective inhibitors predict variability in human drug exposure more accurately than human recombinant P450s.

Authors:  Bo Lindmark; Anna Lundahl; Kajsa P Kanebratt; Tommy B Andersson; Emre M Isin
Journal:  Br J Pharmacol       Date:  2018-04-19       Impact factor: 8.739

8.  A minimal physiologically based pharmacokinetic model that predicts anti-PEG IgG-mediated clearance of PEGylated drugs in human and mouse.

Authors:  M D McSweeney; T Wessler; L S L Price; E C Ciociola; L B Herity; J A Piscitelli; W C Zamboni; M G Forest; Y Cao; S K Lai
Journal:  J Control Release       Date:  2018-06-05       Impact factor: 9.776

9.  Predicting the Effect of CYP3A Inducers on the Pharmacokinetics of Substrate Drugs Using Physiologically Based Pharmacokinetic (PBPK) Modeling: An Analysis of PBPK Submissions to the US FDA.

Authors:  Christian Wagner; Yuzhuo Pan; Vicky Hsu; Vikram Sinha; Ping Zhao
Journal:  Clin Pharmacokinet       Date:  2016-04       Impact factor: 6.447

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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