Heuristic design of cancer chemotherapies

A methodology using heuristic search methods is proposed for optimizing cancer chemotherapies with drugs acting on a specific phase of the cell cycle. Specifically, two evolutionary algorithms, and a simulated annealing method are considered. The methodology relies on an underlying mathematical model for tumor growth that includes cycle phase specificity, and multiple applications of a single cytotoxic agent. The goal is to determine effective protocols for administering the agent, so that the tumor is eradicated, while the immune system remains above a given threshold. Results confirm that modern heuristic methods are a good choice for optimizing complex systems. The three algorithms considered produced effective solutions, and provided drug schedules suitable for practice, although some methods excelled others in performance. A discussion of comparative results is presented.

[1]  Wainer Zoli,et al.  In vitro activity of taxol and taxotere in comparison with doxorubicin and cisplatin on primary cell cultures of human breast cancers , 1995, Breast Cancer Research and Treatment.

[2]  S Gallivan,et al.  A mathematical model of the development of drug resistance to cancer chemotherapy. , 1987, European journal of cancer & clinical oncology.

[3]  A. S. Buldayev A numerical method of control optimization in delay systems for immune response modelling , 1990 .

[4]  R. C. Adams,et al.  Factors Influencing the Choice of the Anesthetic Agent and some Suggestions on Anesthetic Technic , 1941 .

[5]  R. DeCarlo,et al.  Systematic method for determining intravenous drug treatment strategies aiding the humoral immune response , 1998, IEEE Transactions on Biomedical Engineering.

[6]  L. Shampine,et al.  Solving DDEs in MATLAB , 2001 .

[7]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[8]  William H. Press,et al.  Numerical recipes in C , 2002 .

[9]  Bayliss C. McInnis,et al.  Optimal control of bilinear systems: Time-varying effects of cancer drugs , 1979, Autom..

[10]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[11]  Sandro Ridella,et al.  Minimizing multimodal functions of continuous variables with the “simulated annealing” algorithmCorrigenda for this article is available here , 1987, TOMS.

[12]  Ami Radunskaya,et al.  A mathematical tumor model with immune resistance and drug therapy: an optimal control approach , 2001 .

[13]  Morton I. Kamien,et al.  Dynamic Optimization , 2020, Natural Resource Economics.

[14]  L T Chuang,et al.  Effect of new investigational drug taxol on oncolytic activity and stimulation of human lymphocytes , 1993, Gynecologic oncology.

[15]  Dr. Zbigniew Michalewicz,et al.  How to Solve It: Modern Heuristics , 2004 .

[16]  Hans-Georg Beyer,et al.  A Comparison of Evolution Strategies with Other Direct Search Methods in the Presence of Noise , 2003, Comput. Optim. Appl..

[17]  Nikolaus Hansen,et al.  Completely Derandomized Self-Adaptation in Evolution Strategies , 2001, Evolutionary Computation.

[18]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[19]  Rein Luus,et al.  Iterative dynamic programming , 2019, Iterative Dynamic Programming.

[20]  W. Stigelman,et al.  Goodman and Gilman's the Pharmacological Basis of Therapeutics , 1986 .

[21]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[22]  Z. Agur,et al.  A theoretical analysis of interval drug dosing for cell-cycle-phase-specific drugs. , 1992, Mathematical biosciences.

[23]  John A. Adam,et al.  A mathematical model of cycle-specific chemotherapy , 1995 .

[24]  Nikolaus Hansen,et al.  Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[25]  Christopher R. Houck,et al.  A Genetic Algorithm for Function Optimization: A Matlab Implementation , 2001 .

[26]  Ami Radunskaya,et al.  A delay differential equation model for tumor growth , 2003, Journal of mathematical biology.