Can Population Modelling Principles be Used to Identify Key PBPK Parameters for Paediatric Clearance Predictions? An Innovative Application of Optimal Design Theory
Purpose: Physiologically-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. Methods: First, 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. Results: Precise 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. Conclusion: The proposed framework can be applied to other PBPK parameters and facilitate the development of PBPK models.
|Keywords||paediatrics, physiologically-based pharmacokinetic, population modelling, population optimal design|
|Persistent URL||dx.doi.org/10.1007/s11095-018-2487-1, hdl.handle.net/1765/114304|
Calvier, E.A.M, Nguyen, T.T, Johnson, T.N, Rostami-Hodjegan, A, Tibboel, D, Krekels, E.H.J, & Knibbe, C.A.J. (2018). Can Population Modelling Principles be Used to Identify Key PBPK Parameters for Paediatric Clearance Predictions? An Innovative Application of Optimal Design Theory. Pharmaceutical Research, 35(11). doi:10.1007/s11095-018-2487-1