Literature DB >> 28495566

The Constraints, Construction, and Verification of a Strain-Specific Physiologically Based Pharmacokinetic Rat Model.

Helen Musther1, Matthew D Harwood2, Jiansong Yang3, David B Turner2, Amin Rostami-Hodjegan4, Masoud Jamei2.   

Abstract

The use of in vitro-in vivo extrapolation (IVIVE) techniques, mechanistically incorporated within physiologically based pharmacokinetic (PBPK) models, can harness in vitro drug data and enhance understanding of in vivo pharmacokinetics. This study's objective was to develop a user-friendly rat (250 g, male Sprague-Dawley) IVIVE-linked PBPK model. A 13-compartment PBPK model including mechanistic absorption models was developed, with required system data (anatomical, physiological, and relevant IVIVE scaling factors) collated from literature and analyzed. Overall, 178 system parameter values for the model are provided. This study also highlights gaps in available system data required for strain-specific rat PBPK model development. The model's functionality and performance were assessed using previous literature-sourced in vitro properties for diazepam, metoprolol, and midazolam. The results of simulations were compared against observed pharmacokinetic rat data. Predicted and observed concentration profiles in 10 tissues for diazepam after a single intravenous (i.v.) dose making use of either observed i.v. clearance (CLiv) or in vitro hepatocyte intrinsic clearance (CLint) for simulations generally led to good predictions in various tissue compartments. Overall, all i.v. plasma concentration profiles were successfully predicted. However, there were challenges in predicting oral plasma concentration profiles for metoprolol and midazolam, and the potential reasons and according solutions are discussed.
Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  absorption; clearance; distribution; physiological model; preclinical pharmacokinetics; simulations

Mesh:

Substances:

Year:  2017        PMID: 28495566     DOI: 10.1016/j.xphs.2017.05.003

Source DB:  PubMed          Journal:  J Pharm Sci        ISSN: 0022-3549            Impact factor:   3.534


  6 in total

1.  Prediction of Pharmacokinetics of IDP-73152 in Humans Using Physiologically-Based Pharmacokinetics.

Authors:  Myongjae Lee; Yoo-Seong Jeong; Min-Soo Kim; Kyung-Mi An; Suk-Jae Chung
Journal:  Pharmaceutics       Date:  2022-05-28       Impact factor: 6.525

2.  Sex Differences in Intestinal P-Glycoprotein Expression in Wistar versus Sprague Dawley Rats.

Authors:  Christine M Madla; Yujia Qin; Francesca K H Gavins; Jing Liu; Liu Dou; Mine Orlu; Sudaxshina Murdan; Yang Mai; Abdul W Basit
Journal:  Pharmaceutics       Date:  2022-05-10       Impact factor: 6.525

3.  Population PBPK modeling using parametric and nonparametric methods of the Simcyp Simulator, and Bayesian samplers.

Authors:  Janak R Wedagedera; Anthonia Afuape; Siri Kalyan Chirumamilla; Hiroshi Momiji; Robert Leary; Mike Dunlavey; Richard Matthews; Khaled Abduljalil; Masoud Jamei; Frederic Y Bois
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2022-04-22

4.  Development of a Web-Based Toolbox to Support Quantitative In-Vitro-to-In-Vivo Extrapolations (QIVIVE) within Nonanimal Testing Strategies.

Authors:  Ans Punt; Nicole Pinckaers; Ad Peijnenburg; Jochem Louisse
Journal:  Chem Res Toxicol       Date:  2020-12-31       Impact factor: 3.739

5.  Predictive Performance of Next Generation Physiologically Based Kinetic (PBK) Model Predictions in Rats Based on In Vitro and In Silico Input Data.

Authors:  Ans Punt; Jochem Louisse; Nicole Pinckaers; Eric Fabian; Bennard van Ravenzwaay
Journal:  Toxicol Sci       Date:  2022-02-28       Impact factor: 4.849

6.  In Vitro and In Vivo Assessment of Metabolic Drug Interaction Potential of Dutasteride with Ketoconazole.

Authors:  Seong-Wook Seo; Jin Woo Park; Dong-Gyun Han; Ji-Min Kim; Sanghyun Kim; Taeuk Park; Kyung-Hwa Kang; Min Hye Yang; In-Soo Yoon
Journal:  Pharmaceutics       Date:  2019-12-11       Impact factor: 6.321

  6 in total

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