The Optimization of Combination Chemotherapy Schedules in the Presence of Drug Resistance

Abstract

This paper aims to devise efficient combination chemotherapy schedules that determine the dosages of drugs administered to cancer patients with drug resistance that, as a Gordian knot to cancer chemotherapy, may weaken the efficacy of chemotherapy. To characterize cell growth, we use the existing cell cycle-specific model, in which the mechanism of acquired drug resistance is incorporated. Subsequently, the determination of the optimal chemotherapy schedule for the patients is formulated as a nonlinear optimization problem, with the objective of minimizing not only the quantity of tumor cells but also the posttreatment chemotherapy-induced toxicity. To overcome the difficulty in finding a satisfactory solution to the problem due to its nonlinear nature, we develop a memetic algorithm (MA) with an advanced local search strategy. The efficiency of the proposed MA is validated by comparison with other state-of-the-art methods. In addition, we compare the best found solution to the problem in the presence of drug resistance with that in the absence of drug resistance. The resultant findings reveal that drug resistance is a crucial factor in the determination of chemotherapy schedules. Note to Practitioners —The efficacy of chemotherapy is limited by drug toxicity and is weakened by drug resistance. This paper deals with the problem of optimization of chemotherapy schedules with mathematical modeling and heuristic algorithms and is the first to consider the drug specificity, drug combination, and drug resistance simultaneously. The proposed algorithm is flexible. It can be applied to other optimization problems with proper adaptations. The experimental results suggest that the best found solution to the problem in the absence of drug resistance may, however, not remain as the best found solution, or even become an infeasible one, in the presence of drug resistance. Existing clinical regimens may be improved with the assistance of our method that aims to optimize the chemotherapy schedule in the presence of drug resistance. The parameters of our model should be refined with experimental and clinical data to confer more benefits on real treatments in clinical practice in the future.