Can Population Modelling Principles be Used to Identify Key PBPK Parameters for Paediatric Clearance Predictions? An Innovative Application of Optimal Design Theory

Abstract

PurposePhysiologically-based pharmacokinetic (PBPK) models are essential in drug development, but require parameters that are not always obtainable. We developed a methodology to investigate the feasibility and requirements for precise and accurate estimation of PBPK parameters using population modelling of clinical data and illustrate this for two key PBPK parameters for hepatic metabolic clearance, namely whole liver unbound intrinsic clearance (CLint,u,WL) and hepatic blood flow (Qh) in children.MethodsFirst, structural identifiability was enabled through re-parametrization and the definition of essential trial design components. Subsequently, requirements for the trial components to yield precise estimation of the PBPK parameters and their inter-individual variability were established using a novel application of population optimal design theory. Finally, the performance of the proposed trial design was assessed using stochastic simulation and estimation.ResultsPrecise estimation of CLint,u,WL and Qh and their inter-individual variability was found to require a trial with two drugs, of which one has an extraction ratio (ER) ≤ 0.27 and the other has an ER ≥ 0.93. The proposed clinical trial design was found to lead to precise and accurate parameter estimates and was robust to parameter uncertainty.ConclusionThe proposed framework can be applied to other PBPK parameters and facilitate the development of PBPK models.Electronic supplementary materialThe online version of this article (10.1007/s11095-018-2487-1) contains supplementary material, which is available to authorized users.

Recent advances in the ontogeny of drug disposition

“…169 This new knowledge has led to an increasing number of new applications impacting regulatory and clinical decision-making processes, such as initial dose selection for paediatric clinical trials, 170 extrapolation of a model developed for 1 compound to other compounds metabolized by the same elimination pathway, 171 potential for drug-drug interactions, 3,172 and paediatric regulatory approvals. 173 Particularly interesting are the use of population PK approaches to improve the quality of PBPK models 174 and the use of PBPK modelling and simulation to identify covariates that can be used to improve the quality of population PK models, 175 with further potential application for model-informed precision dosing. 176 In this study, we found that study participants could be assigned to 1 of 3 patterns of cumulative acylglucuronidation/ O-demethylation excretion in urine, with a higher percentage of the administered dose being recovered in newborns in whom O-demethylation was the predominant biotransformation pathway.…”

Physiologically Based Pharmacokinetic Models Are Effective Support for Pediatric Drug Development

“…Quantitative measures of sensitivity can be attained by varying individual model parameters by 1% and evaluating the impact on simulated outputs ( 7 , 25 ). Model parameters with certainty can be determined in advance so that the computational cost can be greatly reduced ( 26 , 27 ). Where some uncertainty about the similarity of disease and/or response to intervention remains, this is referred to as partial extrapolation.…”

“…The DDI study was utilized to evaluate the CYP3A4 induction component in the liver and gut. This validated mechanistic model can now be applied in clinical pharmacology studies to prospectively assess the potential DDI of efavirenz as the victim and perpetrator ( 27 ).…”

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

“…However, such extrapolations can be uncertain and may benefit from inclusion of evidence from clinical observations, via parametric inference on some model parameters 3 . When clinical interindividual variability is high, or the data sparse, it is essential to use a population pharmacokinetics framework when conducting such parametric inference 4 . Several approaches and algorithms are available for that purpose, and their relative advantages for PBPK modeling are unclear.…”

“… 3 When clinical interindividual variability is high, or the data sparse, it is essential to use a population pharmacokinetics framework when conducting such parametric inference. 4 Several approaches and algorithms are available for that purpose, and their relative advantages for PBPK modeling are unclear. A recent article 5 compared the application of two Markov Chain Monte Carlo (MCMC) numerical samplers to a diazepam PBPK model: the Metropolis‐Hastings sampler (MH), 6 and the Hamiltonian MCMC sampler (HMCMC).…”