Physiologically based pharmacokinetic modelling: a sound mechanistic basis is needed

Abstract

Pharmacokinetics is the science of drug absorption, distribution, and elimination, or more specifically the quantification of those processes, leading to the understanding, interpretation, and prediction of blood concentration-time profiles. Occasionally, concentration-time data from other physiological fluids or tissues are available, but it is the lack of data in relevant tissues and organs that limits one's ability to get at the underlying mechanisms determining the blood profile. For example, blood concentration data are used to evaluate drug absorption for orally administered drugs, even though absorption is a multistep erratic process under the control of many factors, and measurements within the gut are not usually available.Nevertheless, blood concentration can be a useful biomarker, and sometimes a surrogate [1], to guide therapy. The idea is that pharmacokinetics accounts for some of the variability in the dose-response relationship. However, in order to transform dose into concentration, a pharmacokinetic model is required, based on an analysis of concentration-time data and preferably incorporating relevant patient characteristics, allowing it to be used for individualized therapy [2]. The complexity of a pharmacokinetic model depends on the level and quality of information available and on its purpose. A hierarchy of pharmacokinetic modelsPharmacokinetic models can be classified in order of increasing complexity. At the lowest level is the empirical model most often described by a sum of exponential terms, as in the following equation:This model adequately describes typical concentrationtime profiles and can be used to derive primary pharma- cokinetic parameters, such as clearance and half-life. It can also be used to devise a dosage regimen for a subject who has the same set of parameters [C i , λ i ]. Although useful for data description and interpolation, empirical models are very poor at extrapolation. This is because the parameters do not have a physiological interpretation, and it is difficult to predict how they change when the underlying physiology changes. For example, with a biexponential model after an intravenous bolus dose it is not obvious how the coefficients C 1 and C 2 change with age. This problem can be partly addressed by adopting a compartmental approach with a so-called physiological parameterization. A classical two-compartment model is shown in Figure 1. The concentration-time profiles that result from this model and a biexponential model are the same. However, with this model it is easier to relate the parameters to physiological processes. For example, clearance can be related to renal function, perhaps through creatinine clearance measurements, and the effect of renal impairment on the shape of the concentration-time profile can be predicted. Nevertheless, the compartments in these classical compartment models do not represent real physical spaces, and their meaning has been misrepresented by many authors. Consequently, these models should be viewed as semi-mechanistic mod...

Utility of a physiologically–based pharmacokinetic (PBPK) modeling approach to quantitatively predict a complex drug–drug–disease interaction scenario for rivaroxaban during the drug review process: implications for clinical practice

“…This makes such an assessment of complex DDI a challenge using traditional pharmacokinetic modeling. The PBPK approach has experienced a resurgence recently due to the development of highly sophisticated, population-based PBPK modeling and simulation tools [27,28], which may be valuable to addressing complex DDI issues. This approach is grounded in basic human physiology and chemistry and recent achievements allow for scaling of early in vitro and animal data to create a platform for simulations in humans [28].…”

Oseltamivir Pharmacokinetics and Clinical Experience in Neonates and Infants during an Outbreak of H1N1 Influenza A Virus Infection in a Neonatal Intensive Care Unit

“…5B). By not only relying on the data collected, as with a pure top-down approach, the model incorporates mechanistic covariates and scaling that places it in the middle of the hierarchy of pharmacokinetics between purely empirical and fully physiological (1). In doing so, it borrows strength from both the observed data and established biological processes, and it is envisaged that this should give more confidence to the dosing guidelines produced.…”

Semi-physiologically based pharmacokinetic modeling of paclitaxel metabolism and in silico-based study of the dynamic sensitivities in pathway kinetics

“…In addition to the top-down pharmacokinetic modeling approach, where the study data may be a limiting factor for model complexity (Aarons, 2005), a complementary mechanistic approach may help to understand the relative importance of the targeted genes, and consequently what findings that may be expected from a clinical study. For this reason, the aim of the present work was to develop a semi-physiologically based pharmacokinetic model with detailed description of the uptake, metabolism and efflux of paclitaxel and its metabolites, by extending an existing in vitro derived intracellular pharmacokinetic model and integration of existing population pharmacokinetic models.…”