Literature DB >> 22713733

Best practice in the use of physiologically based pharmacokinetic modeling and simulation to address clinical pharmacology regulatory questions.

P Zhao1, M Rowland, S-M Huang.   

Abstract

Physiologically based pharmacokinetic (PBPK) models are increasingly used by drug developers to evaluate the effect of patient factors on drug exposure. Between June 2008 and December 2011, the Office of Clinical Pharmacology at the US Food and Drug Administration (FDA) received 25 submissions containing PBPK analyses. This report summarizes the essential content of a PBPK analysis needed in a regulatory submission for the purpose of addressing clinical pharmacology questions.

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Year:  2012        PMID： 22713733     DOI： 10.1038/clpt.2012.68

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


  67 in total

Review 1.  Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification.

Authors:  Jennifer E Sager; Jingjing Yu; Isabelle Ragueneau-Majlessi; Nina Isoherranen
Journal:  Drug Metab Dispos       Date:  2015-08-21       Impact factor: 3.922

2.  Comment on: "A Physiologically Based Pharmacokinetic Drug-Disease Model to Predict Carvedilol Exposure in Adult and Paediatric Heart Failure Patients by Incorporating Pathophysiological Changes in Hepatic and Renal Blood".

Authors:  Guo-Fu Li; Xiao Gu; Guo Yu; Shui-Yu Zhao; Qing-Shan Zheng
Journal:  Clin Pharmacokinet       Date:  2016-01       Impact factor: 6.447

3.  Evaluating a physiologically based pharmacokinetic model for predicting the pharmacokinetics of midazolam in Chinese after oral administration.

Authors:  Hong-yun Wang; Xia Chen; Ji Jiang; Jun Shi; Pei Hu
Journal:  Acta Pharmacol Sin       Date:  2015-11-23       Impact factor: 6.150

4.  A PBPK model describing a xenobiotic with a short PK event scale.

Authors:  Xiaofeng Wang; Brian E Davies
Journal:  J Pharmacokinet Pharmacodyn       Date:  2015-07-09       Impact factor: 2.745

5.  Herb-drug interactions: challenges and opportunities for improved predictions.

Authors:  Scott J Brantley; Aneesh A Argikar; Yvonne S Lin; Swati Nagar; Mary F Paine
Journal:  Drug Metab Dispos       Date:  2013-12-11       Impact factor: 3.922

6.  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

7.  Evaluating a physiologically based pharmacokinetic model for prediction of omeprazole clearance and assessing ethnic sensitivity in CYP2C19 metabolic pathway.

Authors:  Sheng Feng; Yumi Cleary; Neil Parrott; Pei Hu; Cornelia Weber; Yongqing Wang; Ophelia Q P Yin; Jun Shi
Journal:  Eur J Clin Pharmacol       Date:  2015-03-24       Impact factor: 2.953

Review 8.  Physiologically Based Pharmacokinetic (PBPK) Modeling of Pharmaceutical Nanoparticles.

Authors:  Min Li; Peng Zou; Katherine Tyner; Sau Lee
Journal:  AAPS J       Date:  2016-11-10       Impact factor: 4.009

Review 9.  Physiologically-based pharmacokinetic models: approaches for enabling personalized medicine.

Authors:  Clara Hartmanshenn; Megerle Scherholz; Ioannis P Androulakis
Journal:  J Pharmacokinet Pharmacodyn       Date:  2016-09-19       Impact factor: 2.745

10.  Physiologically Based Pharmacokinetic Model of the CYP2D6 Probe Atomoxetine: Extrapolation to Special Populations and Drug-Drug Interactions.

Authors:  Weize Huang; Mariko Nakano; Jennifer Sager; Isabelle Ragueneau-Majlessi; Nina Isoherranen
Journal:  Drug Metab Dispos       Date:  2017-08-31       Impact factor: 3.922
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