Literature DB >> 33270249

Physiologically-Based Pharmacokinetic Modeling in Renal and Hepatic Impairment Populations: A Pharmaceutical Industry Perspective.

Tycho Heimbach1, Yuan Chen2, Jun Chen3, Vaishali Dixit4, Neil Parrott5, Sheila Annie Peters6, Italo Poggesi7, Pradeep Sharma8, Jan Snoeys9, Mohamad Shebley10, Guoying Tai11, Susanna Tse12, Vijay V Upreti13, Ying-Hong Wang14, Alice Tsai15, Binfeng Xia16, Ming Zheng17, Andy Z X Zhu18, Stephen Hall19.   

Abstract

The predictive performance of physiologically-based pharmacokinetics (PBPK) models for pharmacokinetics (PK) in renal impairment (RI) and hepatic impairment (HI) populations was evaluated using clinical data from 29 compounds with 106 organ impairment study arms were collected from 19 member companies of the International Consortium for Innovation and Quality in Pharmaceutical Development. Fifty RI and 56 HI study arms with varying degrees of organ insufficiency along with control populations were evaluated. For RI, the area under the curve (AUC) ratios of RI to healthy control were predicted within twofold of the observed ratios for > 90% (N = 47/50 arms). For HI, > 70% (N = 43/56 arms) of the hepatically impaired to healthy control AUC ratios were predicted within twofold. Inaccuracies, typically overestimation of AUC ratios, occurred more in moderate and severe HI. PBPK predictions can help determine the need and timing of organ impairment study. It may be suitable for predicting the impact of RI on PK of drugs predominantly cleared by metabolism with varying contribution of renal clearance. PBPK modeling may be used to support mild impairment study waivers or clinical study design.
© 2020 Merck Sharp & Dohme Corp. Clinical Pharmacology & Therapeutics © 2020 American Society for Clinical Pharmacology and Therapeutics.

Entities:  

Year:  2020        PMID: 33270249     DOI: 10.1002/cpt.2125

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


  17 in total

1.  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

2.  Physiologically based pharmacokinetic modelling to predict the pharmacokinetics of metoprolol in different CYP2D6 genotypes.

Authors:  Choong-Min Lee; Pureum Kang; Chang-Keun Cho; Hye-Jung Park; Yun Jeong Lee; Jung-Woo Bae; Chang-Ik Choi; Hyung Sik Kim; Choon-Gon Jang; Seok-Yong Lee
Journal:  Arch Pharm Res       Date:  2022-06-28       Impact factor: 4.946

3.  Physiologically based pharmacokinetic (PBPK) modeling of flurbiprofen in different CYP2C9 genotypes.

Authors:  Sang-Sup Whang; Chang-Keun Cho; Eui Hyun Jung; Pureum Kang; Hye-Jung Park; Yun Jeong Lee; Chang-Ik Choi; Jung-Woo Bae; Hyung Sik Kim; Choon-Gon Jang; Seok-Yong Lee
Journal:  Arch Pharm Res       Date:  2022-08-26       Impact factor: 6.010

Review 4.  Applications, Challenges, and Outlook for PBPK Modeling and Simulation: A Regulatory, Industrial and Academic Perspective.

Authors:  Wen Lin; Yuan Chen; Jashvant D Unadkat; Xinyuan Zhang; Di Wu; Tycho Heimbach
Journal:  Pharm Res       Date:  2022-05-13       Impact factor: 4.580

5.  Physiologically based pharmacokinetic modelling to predict the clinical effect of CYP3A inhibitors/inducers on esaxerenone pharmacokinetics in healthy subjects and subjects with hepatic impairment.

Authors:  Akiko Watanabe; Tomoko Ishizuka; Makiko Yamada; Yoshiyuki Igawa; Takako Shimizu; Hitoshi Ishizuka
Journal:  Eur J Clin Pharmacol       Date:  2021-08-20       Impact factor: 2.953

Review 6.  Computational Models for Clinical Applications in Personalized Medicine-Guidelines and Recommendations for Data Integration and Model Validation.

Authors:  Catherine Bjerre Collin; Tom Gebhardt; Martin Golebiewski; Tugce Karaderi; Maximilian Hillemanns; Faiz Muhammad Khan; Ali Salehzadeh-Yazdi; Marc Kirschner; Sylvia Krobitsch; Lars Kuepfer
Journal:  J Pers Med       Date:  2022-01-26

7.  Assessment of the Utility of Physiologically-based Pharmacokinetic Model for prediction of Pharmacokinetics in Chinese and Japanese Populations.

Authors:  Yanke Yu; Jian Lin; Chieko Muto; Yinhua Li; Yuko Mori; Rajendar K Mittapalli; Susanna Tse; Jian Liu; Bei Kang Ge; Jing Liu
Journal:  Int J Med Sci       Date:  2021-09-24       Impact factor: 3.738

8.  PBPK Simulation-Based Evaluation of Ganciclovir Crystalluria Risk Factors: Effect of Renal Impairment, Old Age, and Low Fluid Intake.

Authors:  Daniel Scotcher; Aleksandra Galetin
Journal:  AAPS J       Date:  2021-12-14       Impact factor: 4.009

9.  Neural-ODE for pharmacokinetics modeling and its advantage to alternative machine learning models in predicting new dosing regimens.

Authors:  James Lu; Kaiwen Deng; Xinyuan Zhang; Gengbo Liu; Yuanfang Guan
Journal:  iScience       Date:  2021-06-30

10.  Non-uniformity of Changes in Drug-Metabolizing Enzymes and Transporters in Liver Cirrhosis: Implications for Drug Dosage Adjustment.

Authors:  Eman El-Khateeb; Brahim Achour; Zubida M Al-Majdoub; Jill Barber; Amin Rostami-Hodjegan
Journal:  Mol Pharm       Date:  2021-08-24       Impact factor: 4.939

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