Optimal control drug scheduling of cancer chemotherapy
Automatica (Journal of IFAC)
Iterative Dynamic Programming
Multi-objective Optimisation of Cancer Chemotherapy Using Evolutionary Algorithms
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Optimising cancer chemotherapy using an estimation of distribution algorithm and genetic algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
An evolutionary approach to cancer chemotherapy scheduling
Genetic Programming and Evolvable Machines
Computational Intelligence in Bioinformatics
Computational Intelligence in Bioinformatics
Heuristic design of cancer chemotherapies
IEEE Transactions on Evolutionary Computation
A novel evolutionary drug scheduling model in cancer chemotherapy
IEEE Transactions on Information Technology in Biomedicine
A Memetic Algorithm for Multiple-Drug Cancer Chemotherapy Schedule Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The dynamics of an optimally controlled tumor model: A case study
Mathematical and Computer Modelling: An International Journal
A mathematical model of cycle-specific chemotherapy
Mathematical and Computer Modelling: An International Journal
| Hi-index | 0.00 |
Objectives: This study extends a previous mathematical model of cancer cytotoxic chemotherapy, which considered cycling tumor cells and interactions with the immune system, by incorporating a different type of drug: a cytostatic agent. The effect of a cytostatic drug is to arrest cells in a phase of their cycle. In consequence, once tumor cells are arrested and synchronized they can be targeted with a cytotoxic agent, thus maximizing cell kill fraction and minimizing normal cell killing. The goal is to incorporate the new drug into the chemotherapy protocol and devise optimal delivery schedules. Methods: The problem of designing efficient combined chemotherapies is formulated as an optimal control problem and tackled using a state-of-the-art evolutionary algorithm for real-valued encoding, namely the covariance matrix adaptation evolution strategy. Alternative solution representations and three formulations of the underlying objective function are proposed and compared. Results: The optimization problem was successfully solved by the proposed approach. The encoding that enforced non-overlapping (simultaneous) application of the two types of drugs produced competitive protocols with significant less amount of toxic drug, thus achieving better immune system health. When compared to treatment protocols that only consider a cytotoxic agent, the incorporation of a cytostatic drug dramatically improved the outcome and performance of the overall treatment, confirming in silico that the combination of a cytostatic with a cytotoxic agent improves the efficacy and efficiency of the chemotherapy. Conclusion: We conclude that the proposed approach can serve as a valuable decision support tool for the medical practitioner facing the complex problem of designing efficient combined chemotherapies.