Literature DB >> 28860113

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

Weize Huang1, Mariko Nakano1, Jennifer Sager1, Isabelle Ragueneau-Majlessi1, Nina Isoherranen2.   

Abstract

Physiologically based pharmacokinetic (PBPK) modeling of drug disposition and drug-drug interactions (DDIs) has become a key component of drug development. PBPK modeling has also been considered as an approach to predict drug disposition in special populations. However, whether models developed and validated in healthy populations can be extrapolated to special populations is not well established. The goal of this study was to determine whether a drug-specific PBPK model validated using healthy populations could be used to predict drug disposition in specific populations and in organ impairment patients. A full PBPK model of atomoxetine was developed using a training set of pharmacokinetic (PK) data from CYP2D6 genotyped individuals. The model was validated using drug-specific acceptance criteria and a test set of 14 healthy subject PK studies. Population PBPK models were then challenged by simulating the effects of ethnicity, DDIs, pediatrics, and renal and hepatic impairment on atomoxetine PK. Atomoxetine disposition was successfully predicted in 100% of healthy subject studies, 88% of studies in Asians, 79% of DDI studies, and 100% of pediatric studies. However, the atomoxetine area under the plasma concentration versus time curve (AUC) was overpredicted by 3- to 4-fold in end stage renal disease and hepatic impairment. The results show that validated PBPK models can be extrapolated to different ethnicities, DDIs, and pediatrics but not to renal and hepatic impairment patients, likely due to incomplete understanding of the physiologic changes in these conditions. These results show that systematic modeling efforts can be used to further refine population models to improve the predictive value in this area.
Copyright © 2017 by The American Society for Pharmacology and Experimental Therapeutics.

Entities:  

Mesh:

Substances:

Year:  2017        PMID: 28860113      PMCID: PMC5637815          DOI: 10.1124/dmd.117.076455

Source DB:  PubMed          Journal:  Drug Metab Dispos        ISSN: 0090-9556            Impact factor:   3.922


  45 in total

Review 1.  Genetic polymorphism of cytochrome P450 enzymes in Asian populations: focus on CYP2D6.

Authors:  M Kitada
Journal:  Int J Clin Pharmacol Res       Date:  2003

2.  Randomized open-label drug-drug interaction trial of dextromethorphan/quinidine and paroxetine in healthy volunteers.

Authors:  Kerri A Schoedel; Laura E Pope; Edward M Sellers
Journal:  Clin Drug Investig       Date:  2012-03-01       Impact factor: 2.859

3.  Impact of parallel pathways of drug elimination and multiple cytochrome P450 involvement on drug-drug interactions: CYP2D6 paradigm.

Authors:  Kiyomi Ito; David Hallifax; R Scott Obach; J Brian Houston
Journal:  Drug Metab Dispos       Date:  2005-06       Impact factor: 3.922

4.  Disposition and metabolic fate of atomoxetine hydrochloride: the role of CYP2D6 in human disposition and metabolism.

Authors:  John-Michael Sauer; G Douglas Ponsler; Edward L Mattiuz; Amanda J Long; Jennifer W Witcher; Holly R Thomasson; Karl A Desante
Journal:  Drug Metab Dispos       Date:  2003-01       Impact factor: 3.922

Review 5.  Physiologically based pharmacokinetic modeling in drug discovery and development: a pharmaceutical industry perspective.

Authors:  H M Jones; Y Chen; C Gibson; T Heimbach; N Parrott; S A Peters; J Snoeys; V V Upreti; M Zheng; S D Hall
Journal:  Clin Pharmacol Ther       Date:  2015-01-09       Impact factor: 6.875

6.  Development and evaluation of a generic physiologically based pharmacokinetic model for children.

Authors:  Andrea N Edginton; Walter Schmitt; Stefan Willmann
Journal:  Clin Pharmacokinet       Date:  2006       Impact factor: 6.447

7.  Comparative metabolic capabilities and inhibitory profiles of CYP2D6.1, CYP2D6.10, and CYP2D6.17.

Authors:  Hongwu Shen; Minxia M He; Houfu Liu; Steven A Wrighton; Li Wang; Bin Guo; Chuan Li
Journal:  Drug Metab Dispos       Date:  2007-04-30       Impact factor: 3.922

8.  Lack of pharmacologic interaction between paroxetine and alprazolam at steady state in healthy volunteers.

Authors:  Gonzalo Calvo; Consuelo García-Gea; Antonio Luque; Adelaida Morte; Rafael Dal-Ré; Manel Barbanoj
Journal:  J Clin Psychopharmacol       Date:  2004-06       Impact factor: 3.153

9.  A physiologically based pharmacokinetic model to predict disposition of CYP2D6 and CYP1A2 metabolized drugs in pregnant women.

Authors:  Alice Ban Ke; Srikanth C Nallani; Ping Zhao; Amin Rostami-Hodjegan; Nina Isoherranen; Jashvant D Unadkat
Journal:  Drug Metab Dispos       Date:  2013-01-25       Impact factor: 3.922

10.  Atomoxetine hydrochloride: clinical drug-drug interaction prediction and outcome.

Authors:  John-Michael Sauer; Amanda J Long; Barbara Ring; Jennifer S Gillespie; Nathan P Sanburn; Karl A DeSante; David Petullo; Mark R VandenBranden; Charles B Jensen; Steven A Wrighton; Brian P Smith; Holly A Read; Jennifer W Witcher
Journal:  J Pharmacol Exp Ther       Date:  2003-11-10       Impact factor: 4.030

View more
  16 in total

Review 1.  State-of-the-Art Review on Physiologically Based Pharmacokinetic Modeling in Pediatric Drug Development.

Authors:  Venkata Yellepeddi; Joseph Rower; Xiaoxi Liu; Shaun Kumar; Jahidur Rashid; Catherine M T Sherwin
Journal:  Clin Pharmacokinet       Date:  2019-01       Impact factor: 6.447

2.  Modeling Drug Disposition and Drug-Drug Interactions Through Hypothesis-Driven Physiologically Based Pharmacokinetics: a Reversal Translation Perspective.

Authors:  Guo-Fu Li; Qing-Shan Zheng
Journal:  Eur J Drug Metab Pharmacokinet       Date:  2018-06       Impact factor: 2.441

3.  Sampling Site Has a Critical Impact on Physiologically Based Pharmacokinetic Modeling.

Authors:  Weize Huang; Nina Isoherranen
Journal:  J Pharmacol Exp Ther       Date:  2019-10-11       Impact factor: 4.030

4.  PBPK Analysis to Study the Impact of Genetic Polymorphism of NAT2 on Drug-Drug Interaction Potential of Isoniazid.

Authors:  Ankit Balhara; Saranjit Singh
Journal:  Pharm Res       Date:  2021-09-13       Impact factor: 4.200

5.  Physiologically based pharmacokinetic (PBPK) modeling of piroxicam with regard to CYP2C9 genetic polymorphism.

Authors:  Chang-Keun Cho; Pureum Kang; Hye-Jung Park; Eunvin Ko; Chou Yen Mu; Yun Jeong Lee; Chang-Ik Choi; Hyung Sik Kim; Choon-Gon Jang; Jung-Woo Bae; Seok-Yong Lee
Journal:  Arch Pharm Res       Date:  2022-05-31       Impact factor: 4.946

6.  The Impact of the CYP2D6 "Enhancer" Single Nucleotide Polymorphism on CYP2D6 Activity.

Authors:  Jean C Dinh; Erin C Boone; Vincent S Staggs; Robin E Pearce; Wendy Y Wang; Roger Gaedigk; James Steven Leeder; Andrea Gaedigk
Journal:  Clin Pharmacol Ther       Date:  2021-11-30       Impact factor: 6.875

7.  Mechanistic PBPK Modeling of Urine pH Effect on Renal and Systemic Disposition of Methamphetamine and Amphetamine.

Authors:  Weize Huang; Lindsay C Czuba; Nina Isoherranen
Journal:  J Pharmacol Exp Ther       Date:  2020-03-20       Impact factor: 4.030

8.  Pitfalls of using numerical predictive checks for population physiologically-based pharmacokinetic model evaluation.

Authors:  Anil R Maharaj; Huali Wu; Christoph P Hornik; Michael Cohen-Wolkowiez
Journal:  J Pharmacokinet Pharmacodyn       Date:  2019-04-23       Impact factor: 2.410

9.  Ontogeny of Hepatic Sulfotransferases and Prediction of Age-Dependent Fractional Contribution of Sulfation in Acetaminophen Metabolism.

Authors:  Mayur K Ladumor; Deepak Kumar Bhatt; Andrea Gaedigk; Sheena Sharma; Aarzoo Thakur; Robin E Pearce; J Steven Leeder; Michael B Bolger; Saranjit Singh; Bhagwat Prasad
Journal:  Drug Metab Dispos       Date:  2019-05-17       Impact factor: 3.922

Review 10.  Pediatric Drug-Drug Interaction Evaluation: Drug, Patient Population, and Methodological Considerations.

Authors:  Daniel Gonzalez; Jaydeep Sinha
Journal:  J Clin Pharmacol       Date:  2021-06       Impact factor: 2.860

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.