Grant: Travel Awards - Institute Community Support

Many anti-cancer drugs exhibit significant variability with regard to their exposure and therapeutic action, and show a narrow range between toxic and beneficial doses. Thus, a universal strategy to design dose and time schedules for a whole population may result in an increased risk of therapeutic failure and/or toxicity. Individually tailoring the chemotherapy to patients, while considering the balance between the therapeutic efficacy and adverse effects, is the main concern for clinicians in their routine treatment. In most cases, drug concentration related markers, such as the area under the concentration curve or the maximum concentration value, are the sole quantitative criteria used for individual dose adaptation, while the drug effect related markers are there only for clinical reference. In recent years, many mathematical modeling and simulation approaches have been proposed to link the drug efficacy and toxicity to drug exposition or directly to dose. The population pharmacokinetic and phamacodynamic (Pop PK/PD) approach(1), which is now widely accepted in the pharmaceutical research, contributed by many efforts in this direction. Inspired by these developments and our own experience in drug modeling and simulation, we want to invest in this PK/PD dose adaptation issue, where we are likely to make important and original contributions. As a part of the care management system we have been developing, we will take advantage of available Pop PK models and use a Bayesian signal decision approach we formerly developed(2). Recently, we have successfully developed and implemented a computational program that can be used in the optimisation and design of doses and time schedules for oral anticancer drugs. (1) Guidance for Industry-Population Pharmacokinetics: FDA website (2) O. Barrière et al, A Bayesian approach for the estimation of patient compliance based on the last sampling information, Journal of Pharmacokinetic and Pharmacodynamics.(2011)