Literature DB >> 29027194

Predictive Performance of Physiologically Based Pharmacokinetic (PBPK) Modeling of Drugs Extensively Metabolized by Major Cytochrome P450s in Children.

Wangda Zhou1, Trevor N Johnson2, Khanh H Bui1, S Y Amy Cheung3, Jianguo Li1, Hongmei Xu1, Nidal Al-Huniti1, Diansong Zhou1.   

Abstract

The accuracy of physiologically based pharmacokinetic (PBPK) model prediction in children, especially those younger than 2 years old, has not been systematically evaluated. The aim of this study was to characterize the pediatric predictive performance of the PBPK approach for 10 drugs extensively metabolized by CYP1A2 (theophylline), CYP2C8 (desloratidine, montelukast), CYP2C9 (diclofenac), CYP2C19 (esomeprazole, lansoprazole), CYP2D6 (tramadol), and CYP3A4 (itraconazole, ondansetron, sufentanil). Model performance in children was evaluated by comparing simulated plasma concentration-time profiles with observed clinical results for each drug and age group. PBPK models reasonably predicted the pharmacokinetics of desloratadine, diclofenac, itraconazole, lansoprazole, montelukast, ondansetron, sufentanil, theophylline, and tramadol across all age groups. Collectively, 58 out of 67 predictions were within 2-fold and 43 out of 67 predictions within 1.5-fold of observed values. Developed PBPK models can reasonably predict exposure in children age 1 month and older for an array of predominantly CYP metabolized drugs.
© 2017 American Society for Clinical Pharmacology and Therapeutics.

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Year:  2017        PMID: 29027194     DOI: 10.1002/cpt.905

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


  12 in total

Review 1.  Pediatric Dose Selection and Utility of PBPK in Determining Dose.

Authors:  Ian E Templeton; Nicholas S Jones; Luna Musib
Journal:  AAPS J       Date:  2018-02-13       Impact factor: 4.009

2.  Developmental Pharmacogenetics of SLCO2B1 on Montelukast Pharmacokinetics in Chinese Children.

Authors:  Qian Li; Kai Wang; Jun Zhou; Wei Zhao; Hai-Yan Shi; Yue-E Wu; Yue Zhou; Min Kan; Yi Zheng; Guo-Xiang Hao; Xin-Mei Yang; Yi-Lei Yang; Le-Qun Su; Xiao-Ling Wang; Evelyne Jacqz-Aigrain
Journal:  Drug Des Devel Ther       Date:  2019-12-27       Impact factor: 4.162

3.  Evaluation of Quantitative Structure Property Relationship Algorithms for Predicting Plasma Protein Binding in Humans.

Authors:  Yejin Esther Yun; Rogelio Tornero-Velez; S Thomas Purucker; Daniel T Chang; Andrea N Edginton
Journal:  Comput Toxicol       Date:  2021-02-01

4.  A generic framework for the physiologically-based pharmacokinetic platform qualification of PK-Sim and its application to predicting cytochrome P450 3A4-mediated drug-drug interactions.

Authors:  Sebastian Frechen; Juri Solodenko; Thomas Wendl; André Dallmann; Ibrahim Ince; Thorsten Lehr; Jörg Lippert; Rolf Burghaus
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2021-05-24

5.  Chloroquine Dosing Recommendations for Pediatric COVID-19 Supported by Modeling and Simulation.

Authors:  Laurens F M Verscheijden; Tjitske M van der Zanden; Lianne P M van Bussel; Marika de Hoop-Sommen; Frans G M Russel; Trevor N Johnson; Saskia N de Wildt
Journal:  Clin Pharmacol Ther       Date:  2020-05-21       Impact factor: 6.875

6.  Assessing CYP2C19 Ontogeny in Neonates and Infants Using Physiologically Based Pharmacokinetic Models: Impact of Enzyme Maturation Versus Inhibition.

Authors:  Peng Duan; Fang Wu; Jason N Moore; Jeffrey Fisher; Victor Crentsil; Daniel Gonzalez; Lei Zhang; Gilbert J Burckart; Jian Wang
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2018-12-05

7.  A Retrospective Evaluation of Allometry, Population Pharmacokinetics, and Physiologically-Based Pharmacokinetics for Pediatric Dosing Using Clearance as a Surrogate.

Authors:  Qier Wu; Sheila Annie Peters
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2019-02-26

Review 8.  A Physiology-Based Pharmacokinetic Framework to Support Drug Development and Dose Precision During Therapeutic Hypothermia in Neonates.

Authors:  Anne Smits; Pieter Annaert; Steven Van Cruchten; Karel Allegaert
Journal:  Front Pharmacol       Date:  2020-05-13       Impact factor: 5.810

9.  A Whole-Body Physiologically Based Pharmacokinetic Model Characterizing Interplay of OCTs and MATEs in Intestine, Liver and Kidney to Predict Drug-Drug Interactions of Metformin with Perpetrators.

Authors:  Yiting Yang; Zexin Zhang; Ping Li; Weimin Kong; Xiaodong Liu; Li Liu
Journal:  Pharmaceutics       Date:  2021-05-11       Impact factor: 6.321

Review 10.  Utilization of Physiologically Based Pharmacokinetic Modeling in Clinical Pharmacology and Therapeutics: an Overview.

Authors:  Courtney Perry; Grace Davis; Todd M Conner; Tao Zhang
Journal:  Curr Pharmacol Rep       Date:  2020-05-12
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