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.

Fig. 1

Requirements that will allow a high level of confidence in PBPK predictions for the three broad categories of applications. The placement of these three categories of applications along the value chain is also depicted. ¹The greater the variability and smaller the size of the cohort, the larger the range of the estimated parameter. If this range is close to the entire range of plausible values, the exercise of parameter estimation is rendered less valuable. PK pharmacokinetics, NCE new chemical entity, DDI drug–drug interaction, PBPK physiologically based pharmacokinetics

Fig. 2

Barriers to establishing confidence in the key mechanisms impacting an application. CYP cytochrome P450

Fig. 3

Impact of changing of CL_int and multiplicative factor for K_p factors on the intravenous PK profile. As CL_int is increased, the profile shifts down, with the shape remaining intact. The effect of increasing the K_p factor is to change the shape of the profile. CL_int intrinsic clearance, K_p tissue partition coefficient, PK pharmacokinetic, IV intravenous, PBPK physiologically based pharmacokinetics

Fig. 4

Workflow to decide between establishing confidence in the application of PBPK model or situations in which simpler models for an intended purpose may be considered. Start with identifying the key PK mechanisms that are relevant for the intended purpose of the application. Next, build the model, ensuring that parameters needed for these mechanisms, especially the sensitive parameters, are estimated from clinical PK data. Verify the model and refine the parameters if necessary. If minimum requirements to establish confidence in the model are not met, simpler models should be preferred. Establishing confidence in sensitive PBPK model parameters for the mechanisms that are identified to be relevant to the intended purpose of a PBPK model application and verifying the model are necessary prior to model application. Hypothesis generation/testing can help resolve parameter non-identifiability through deconvolution of underlying mechanisms, and allows for robust parameterization. PBPK physiologically based pharmacokinetics, PK pharmacokinetics

Fig. 5

Building a platform of evidence to enhance confidence in underlying PK mechanisms for a poorly soluble weak base with absorption predicted to be limited by precipitation. PPI proton pump inhibitor, PK pharmacokinetics, tox toxicology, SAD single ascending dose, MAD multiple ascending dose, C_max maximum concentration, AUC area under the curve, t_max time to reach C_max, BCS Biopharmaceutics Classification System