Pharmacokinetic Model-Predicted Anticancer Drug Concentrations in Human Tumors

Abstract

In an era when molecular and targeted anticancer therapeutics is a major focus and when understanding drug dynamics in tumor is critical, it seems advantageous to be able to relate drug concentrations in tumors to corresponding biological end points. To that end, a novel method, based on physiologically based hybrid pharmacokinetic models, is presented to predict human tumor drug concentrations. Such models consist of a forcing function, describing the plasma drug concentration-time profile, which is linked to a model describing drug disposition in tumors. The hybrid models are originally derived from preclinical data and then scaled to humans. Integral to the scale-up procedure is the ability to derive human forcing functions directly from clinical pharmacokinetic data. Three examples of this approach are presented based on preclinical investigations with carboplatin, topotecan, and temozolomide. Translation of these preclinical hybrid models to humans used a Monte Carlo simulation technique that accounted for intrasubject and intersubject variability. Different pharmacokinetic end points, such as the AUC tumor, were extracted from the simulated human tumor drug concentrations to show how the predicted drug concentrations might be used to select drug-dosing regimens. It is believed that this modeling strategy can be used as an aid in the drug development process by providing key insights into drug disposition in tumors and by offering a foundation to optimize drug regimen design.