» Articles » PMID: 31236775

Requirements to Establishing Confidence in Physiologically Based Pharmacokinetic (PBPK) Models and Overcoming Some of the Challenges to Meeting Them

Overview
Specialty Pharmacology
Date 2019 Jun 26
PMID 31236775
Citations 28
Authors
Affiliations
Soon will be listed here.
Abstract

When scientifically well-founded, the mechanistic basis of physiologically based pharmacokinetic (PBPK) models can help reduce the uncertainty and increase confidence in extrapolations outside the studied scenarios or studied populations. However, it is not always possible to establish mechanistically credible PBPK models. Requirements to establishing confidence in PBPK models, and challenges to meeting these requirements, are presented in this article. Parameter non-identifiability is the most challenging among the barriers to establishing confidence in PBPK models. Using case examples of small molecule drugs, this article examines the use of hypothesis testing to overcome parameter non-identifiability issues, with the objective of enhancing confidence in the mechanistic basis of PBPK models and thereby improving the quality of predictions that are meant for internal decisions and regulatory submissions. When the mechanistic basis of a PBPK model cannot be established, we propose the use of simpler models or evidence-based approaches.

Citing Articles

Solubility-Limited Absorption Identified by a Simplified PBPK Model for the Prediction of Positive Food Effect for BCS II/IV Drugs.

Rodriguez-Fernandez K, Gomez-Mantilla J, Shukla S, Mangas-Sanjuan V, Peters S Clin Pharmacokinet. 2025; .

PMID: 39899202 DOI: 10.1007/s40262-025-01472-w.


Evaluation of Solubility-Limited Absorption as a Surrogate to Predicting Positive Food Effect of BCS II/IV Drugs.

Rodriguez-Fernandez K, Gomez-Mantilla J, Shukla S, Stopfer P, Sieger P, Mangas-Sanjuan V Clin Pharmacokinet. 2025; .

PMID: 39899201 DOI: 10.1007/s40262-025-01473-9.


Physiologically Based Pharmacokinetic Model of Cefotaxime in Patients with Impaired Renal Function.

Zbib F, Deschamps A, Velly L, Blin O, Guilhaumou R, Gattacceca F Clin Pharmacokinet. 2025; 64(2):257-273.

PMID: 39762592 DOI: 10.1007/s40262-024-01469-x.


From hazard to risk prioritization: a case study to predict drug-induced cholestasis using physiologically based kinetic modeling.

de Bruijn V, Rietjens I Arch Toxicol. 2024; 98(9):3077-3095.

PMID: 38755481 PMC: 11324677. DOI: 10.1007/s00204-024-03775-6.


Parameter grouping and co-estimation in physiologically based kinetic models using genetic algorithms.

Tsiros P, Minadakis V, Li D, Sarimveis H Toxicol Sci. 2024; 200(1):31-46.

PMID: 38637946 PMC: 11199918. DOI: 10.1093/toxsci/kfae051.


References
1.
Marshall S, Burghaus R, Cosson V, Cheung S, Chenel M, DellaPasqua O . Good Practices in Model-Informed Drug Discovery and Development: Practice, Application, and Documentation. CPT Pharmacometrics Syst Pharmacol. 2016; 5(3):93-122. PMC: 4809625. DOI: 10.1002/psp4.12049. View

2.
Mao J, Doshi U, Wright M, Hop C, Li A, Chen Y . Prediction of the Pharmacokinetics of Pravastatin as an OATP Substrate Using Plateable Human Hepatocytes With Human Plasma Data and PBPK Modeling. CPT Pharmacometrics Syst Pharmacol. 2018; 7(4):251-258. PMC: 5915609. DOI: 10.1002/psp4.12283. View

3.
Huang S, Abernethy D, Wang Y, Zhao P, Zineh I . The utility of modeling and simulation in drug development and regulatory review. J Pharm Sci. 2013; 102(9):2912-23. DOI: 10.1002/jps.23570. View

4.
Peters S . Identification of intestinal loss of a drug through physiologically based pharmacokinetic simulation of plasma concentration-time profiles. Clin Pharmacokinet. 2008; 47(4):245-59. DOI: 10.2165/00003088-200847040-00003. View

5.
Lappin G, Stevens L . Biomedical accelerator mass spectrometry: recent applications in metabolism and pharmacokinetics. Expert Opin Drug Metab Toxicol. 2008; 4(8):1021-33. DOI: 10.1517/17425255.4.8.1021. View