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Predicting Human Tumor Drug Concentrations from a Preclinical Pharmacokinetic Model of Temozolomide Brain Disposition

Authors' Affiliations: ¹ Department of Pharmaceutical Sciences, School of Pharmacy, Temple University; ² Medical Science Division, Fox Chase Cancer Center, Philadelphia, Pennsylvania; and ³ Department of Biomedical Engineering, University of Washington, Seattle, Washington

Requests for reprints: James M. Gallo, Department of Pharmaceutical Sciences, School of Pharmacy, Temple University, 3307 North Broad Street, Philadelphia, PA 19140. Phone: 215-707-9699; Fax: 215-707-9409; E-mail: jmgallo{at}temple.edu.

Purpose: Knowledge of drug concentrations in tumors is critical for understanding the determinants of drug accumulation in tumors. Because significant obstacles prevent making these measurements in humans, development of a predictive pharmacokinetic model would be of great value to the translation of preclinical data to the clinic. Our goal was to show how the latter could be achieved for temozolomide, an agent used in the treatment of brain tumors, using an orthotopic brain tumor model in rats.

Experimental Design: Rats bearing i.c. tumors received 20 mg/kg i.v. of temozolomide followed by the subsequent measurement of serial plasma, cerebrospinal fluid (CSF), normal brain, and brain tumor temozolomide concentrations. The resultant data provided the framework to develop a hybrid physiologically based pharmacokinetic model for temozolomide in brain. The preclinical pharmacokinetic model was scaled to predict temozolomide concentrations in human CSF, normal brain, and brain tumor, and through a series of Monte Carlo simulations, the accumulation of temozolomide in brain tumors under conditions of altered blood-brain barrier permeability, fractional blood volume, and clinical dosing schedules was evaluated.

Results: The developed physiologically based pharmacokinetic model afforded a mechanistic and accurate prediction of temozolomide brain disposition in rats, which through model scale-up procedures accurately predicted the CSF/plasma area under the drug concentration-time curve ratios of 0.2 reported in patients. Through a series of model simulations, it was shown that the brain tumor accumulation of temozolomide varied substantially based on changes in blood-brain barrier permeability and fractional tumor blood volume but minimally based on clinical dosing regimens.

Conclusions: A physiologically based pharmacokinetic modeling approach offers a means to translate preclinical to clinical characteristics of drug disposition in target tissues and, thus, a means to select appropriate drug dosing regimens for achieving optimal target tissue drug concentrations.

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