Combining the ‘bottom up’ and ‘top down’ approaches in pharmacokinetic modelling: fittingPBPKmodels to observed clinical data

Abstract

Pharmacokinetic models range from being entirely exploratory and empirical, to semi-mechanistic and ultimately complex physiologically based pharmacokinetic (PBPK) models. This choice is conditional on the modelling purpose as well as the amount and quality of the available data. The main advantage of PBPK models is that they can be used to extrapolate outside the studied population and experimental conditions. The trade-off for this advantage is a complex system of differential equations with a considerable number of model parameters. When these parameters cannot be informed from in vitro or in silico experiments they are usually optimized with respect to observed clinical data. Parameter estimation in complex models is a challenging task associated with many methodological issues which are discussed here with specific recommendations. Concepts such as structural and practical identifiability are described with regards to PBPK modelling and the value of experimental design and sensitivity analyses is sketched out. Parameter estimation approaches are discussed, while we also highlight the importance of not neglecting the covariance structure between model parameters and the uncertainty and population variability that is associated with them. Finally the possibility of using model order reduction techniques and minimal semi-mechanistic models that retain the physiological-mechanistic nature only in the parts of the model which are relevant to the desired modelling purpose is emphasized. Careful attention to all the above issues allows us to integrate successfully information from in vitro or in silico experiments together with information deriving from observed clinical data and develop mechanistically sound models with clinical relevance.

How Does In Vivo Biliary Elimination of Drugs Change with Age? Evidence from In Vitro and Clinical Data Using a Systems Pharmacology Approach

“…The latter approach has been taken up in regulatory guidance on drug-drug interaction assessment in special populations, including pediatric subjects, by both the Food and Drug Administration and European Medicines Agency. Such use of PBPK models to extrapolate outside the study populations and experimental conditions required careful attention to a number of issues and confidence that key systems parameters within the PBPK model were correct (Tsamandouras et al, 2015).…”

Development of a Novel Simplified PBPK Absorption Model to Explain the Higher Relative Bioavailability of the OROS® Formulation of Oxybutynin

“…The latter also highlights the importance of having good disposition data available for the purpose of judging mechanistic bioavailability predictions, particularly due to the possible confounding issues that can arise from the use of the oral data to calibrate some of the absorption parameters in the model, particularly when the predictions are not in agreement with the observed clinical data. The latter might lead to biased estimates of the absorption parameters due to the fact that the disposition parameters were biased in the first instance (42,91).…”

Key to Opening Kidney for In Vitro-In Vivo Extrapolation Entrance in Health and Disease: Part II: Mechanistic Models and In Vitro-In Vivo Extrapolation

“…If certain parameters cannot be unambiguously informed from available clinical data then such parameters may be non-identifiable and therefore prone to bias and uncertainty. This problem is well documented for complex mechanistic models and particularly relevant when parameters estimated are to be used for extrapolation (83). Although these issues have been discussed in detail in relation to hepatic disposition (83,84), they are particularly pertinent to mechanistic kidney models, due to the high number of parameters used in these models.…”

“…This problem is well documented for complex mechanistic models and particularly relevant when parameters estimated are to be used for extrapolation (83). Although these issues have been discussed in detail in relation to hepatic disposition (83,84), they are particularly pertinent to mechanistic kidney models, due to the high number of parameters used in these models. As knowledge of specific drugs and biological systems are improved, such uncertainties are likely to become reduced, providing more confidence in model predictions (5,85).…”