The Use Of Physiologically Based Pharmacokinetic (PBPK) Model In Drug Development
Perdaems Lambert N.(1), Bouzom F.(1), Freidig A.(2), Solbes Marchetti M.N.(1), Jochemsen R.(3), and Walther B.(1)
(1) Technologie SERVIER (Orléans-France)
In PBPK models, the kinetics of a drug is related to physiological parameters (such as blood flow and tissue size) and to drug specific parameters (such as enzyme kinetics and tissue partitioning coefficients). With this type of models, pharmacokinetic profiles in man can be predicted using data of in vitro experiments with human material (microsomes e.g.) and/or in vivo experiments in other species, which is especially useful in an early stage of development.
Later in the development, PBPK can help to predict drug-drug interactions from other in vitro parameters (Vmax, Km, inhibition constant…).
The present study was performed to evaluate the PBPK approach to predict drug-drug interactions for a compound in phase III, ivabradine. That compound is mainly metabolised by the cytochrome P450 3A4 which is involved in drug metabolism of many drugs. So, the potential for drug-drug interactions to occur is substantial and the result can be of great clinical significance.
First, a PBPK model was built to describe pharmacokinetics of ivabradine when administered alone. Five tissue compartments (liver, adipose, heart, richly and poorly perfused tissue) and blood were included in this PBPK model. Good predictions for blood concentrations after intravenous and oral administrations were obtained with this model.
In the same way, taking benefit from a collaboration project with TNO, PBPK models were built to predict blood and tissue profiles of potential inhibitors (verapamil and ketoconazole) when they are not co-administered.
Then, combination of the two PBPK models was realised to predict both, ivabradine and potential inhibitor concentrations when they are co-administered.
The influence of different parameters such as the unbound tissue fraction, the partition coefficient, the inhibition constant was evaluated by using sensitive analysis. The results of the simulations were compared to observed concentrations from in vivo studies in healthy volunteers.