Deriving therapies for children with primary CNS tumors using pharmacokinetic modeling and simulation of cerebral...

The treatment of children with primary central nervous system (CNS) tumors continues to be a challenge despite recent advances in technology and diagnostics. In this overview, we describe our approach for identifying and evaluating active anticancer drugs through a process that enables rational translation from the lab to the clinic. The preclinical approach we discuss uses tumor subgroup-specific models of pediatric CNS tumors, cerebral microdialysis sampling of tumor extracellular fluid (tECF), and pharmacokinetic modeling and simulation to overcome challenges that currently hinder researchers in this field. This approach involves performing extensive systemic (plasma) and target site (CNS tumor) pharmacokinetic studies. Pharmacokinetic modeling and simulation of the data derived from these studies are then used to inform future decisions regarding drug administration, including dosage and schedule. Here, we also present how our approach was used to examine two FDA approved drugs, simvastatin and pemetrexed, as candidates for new therapies for pediatric CNS tumors. We determined that due to unfavorable pharmacokinetic characteristics and insufficient concentrations in tumor tissue in a mouse model of ependymoma, simvastatin would not be efficacious in further preclinical trials. In contrast to simvastatin, pemetrexed was advanced to preclinical efficacy studies after our studies determined that plasma exposures were similar to those in humans treated at similar tolerable dosages and adequate unbound concentrations were found in tumor tissue of medulloblastoma-bearing mice. Generally speaking, the high clinical failure rates for CNS drug candidates can be partially explained by the fact that therapies are often moved into clinical trials without extensive and rational preclinical studies to optimize the transition. Our approach addresses this limitation by using pharmacokinetic and pharmacodynamic modeling of data generated from appropriate in vivo models to support the rational testing and usage of innovative therapies in children with CNS tumors.