![]() ![]() Our research has been focused on the development of innovative CNS PBPK models for mechanistic prediction of heterogeneous penetration of anticancer drugs into the human brain and brain tumors. The in vitro-in vivo extrapolation-physiologically based pharmacokinetic (IVIVE-PBPK) model offers a unique platform that allows simultaneous incorporation of drug- and system-specific parameters into a pharmacokinetic model and enables a priori prediction of individual in vivo kinetic processes based on mechanistic scaling of in vitro data (e.g., in vitro enzyme and transporter kinetics). Hence, the development of innovative approaches is imperative. Given the fact that the rate and extent of drug BBB penetration is determined by not only drug properties but also biological system characteristics, prediction of human BBB permeability from preclinical in vitro or animal models is complicated by biological system differences. Mechanistic understanding and early prediction of drug BBB penetration is of paramount importance to rational drug development and treatment for brain cancer. Physiologically based pharmacokinetic modeling of the human central nervous system (CNS) pharmacokinetics: Insufficient penetration of potentially effective chemotherapeutic agents across the human blood-brain barrier (BBB) is a huge hurdle to successful treatment of brain cancer. We employ an integrated translational research approach that leverages preclinical pharmacology studies, pharmacokinetic modeling, and clinical trials to mechanistically understand and quantitatively predict pharmacokinetics and pharmacodynamics of anticancer drugs in patients. The central theme of my research is to promote rational cancer therapy and drug development by better understanding the clinical pharmacology of anticancer agents, that is, how the body handles a drug (i.e., pharmacokinetics) and how a drug acts on the body (i.e., pharmacodynamics). Physiologically based pharmacokinetic (PBPK) modeling and simulation.Clinical pharmacology of anticancer drugs. ![]()
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