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

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.

Physiologically‐based pharmacokinetic modeling to support bioequivalence and approval of generic products: A case for diclofenac sodium topical gel, 1%

“…The "2-fold" criterion on mC max and AUC was referenced in this case for performance assessment of the dermal PBPK models developed for diclofenac sodium topical drug products and of dermal PBPK models developed for drug products supporting platform performance assessment. 24,[40][41][42][43] Overall, a good agreement between the predicted and observed PK profiles was observed (i.e., the overall shape of the PK profile, model-predicted absorption phase, and prediction of the time at C max [T max ] value were deemed satisfactory). Additional considerations on the acceptance criteria included the quality of the data used for building and assessing the performance of the model, the credibility of the model assumptions, the regulatory impact of the decision associated with the model, and the level of confidence on the overall modeling approach.…”

Current Approaches and Techniques in Physiologically Based Pharmacokinetic (PBPK) Modelling of Nanomaterials

“…Confidence in the predictive performance of the model is high when the mechanisms of the underlying processes are identified, the associated parameters are determined and the model is validated against experimental data [ 135 ]. Model validation, defined as the process by which the reliability and relevance of a particular model is established [ 136 ], assesses the ability of the model to predict the toxicokinetic behavior of the chemical under consideration, preferably using “data that was not used in the development of the model and the estimation of its parameters” [ 101 ].…”

Investigating the Utility of Humanized PXR-CAR-CYP3A4/7 Mouse Model to Assess CYP3A-Mediated Induction

“…In addition, a perpetrating drug may exert differential effect in vitro which may both inhibit and induce DMEs, complicating translation to the clinic. While some success and much progress have been demonstrated with physiological-based pharmacokinetic (PBPK) modeling (Guo et al, 2013;Wagner et al, 2016), there continues to be challenges arising from uncertainty such as with measuring in vitro endpoints, finding relevant and appropriate scaling in vitro parameters, as well as incomplete characterization of in vivo disposition of compounds that are in early stages of clinical trials (Jones et al, 2015;Shebley et al, 2018;Peters and Dolgos, 2019). Consequently, it has been reported that PBPK can underpredict the magnitude of induction (Almond et al, 2016).…”