Evolutionary drug scheduling models with different toxicity metabolism in cancer chemotherapy

Through incorporating into Martin's drug scheduling model a toxicity metabolism term, our modified model takes into account the body's ability of recovering from the effect of the drug and successively overcomes two unreasonable problems in Martin's model. Since different drugs have different toxicity metabolism processes, we propose two renewed drug scheduling models with different toxicity metabolism according to kinetics of enzyme-catalyzed chemical reactions. For exploring multiple efficient drug scheduling policies, we use our adaptive elitist-population based genetic algorithm (AEGA) to solve the renewed models, and discuss the situation of multiple optimal solutions under different parameter settings. The simulation results obtained by the renewed models match well with the clinical treatment experience, and can provide much more drug scheduling polices for the doctor to choose depending on the particular conditions of the patients.

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