The clinical schedule of administration for anticancer agents is developed currently through a statistically-driven trial-and-error process based on previous experience with similar effective agents, in combination with animal studies and Phase I trials, to establish maximum tolerated dose (MTD) and starting dose for Phase II trials. The result of the ensuing Phase II trial, when successful, is a feasible, but suboptimal, schedule for a drug administered to an "average" patient. But individuals are a population of differences from the "average". The researchers employ a systems medicine approach, interfacing medical practice with rigorous systems engineering tools in an effort to improve the potential outcomes (better antitumor effect; fewer toxic side effects) for patients receiving chemotherapy for treatment of cancer. The mathematical models of disease that they will construct will be derived from mechanism and physiology and informed from available clinical data. With an interest in using these models for the design of novel patient-tailored chemotherapy treatment schedules, the researchers will focus on models that are control-relevant, meaning of suitable complexity to be used explicitly in model-based systems engineering algorithms. Finally, they will explicitly incorporate clinical concerns in the treatment design problem formulation, such that clinical treatment objectives and constraints will provide practical limits on the chemotherapy schedules returned by the design algorithm. With an eye to translating these results efficiently to clinical practice, the researchers focus on two first-line chemotherapeutics: gemcitabine and docetaxel.
