Multi-drug cancer chemotherapy scheduling by a new memetic optimization algorithm

This paper proposes a new memetic algorithm (MA) to solve the multi-drug chemotherapy optimization problem. The new MA combines GA with a local search algorithm called iterative dynamic programming (IDP). A multi-drug chemotherapy model is introduced to simulate the possible response of the tumor cells under drugs administration. Optimization of the multiple chemotherapeutic agents' administration schedules is based on this tumor model. We formulate the optimization problem as an optimal control problem (OCP) with a set of dynamic equations. The objective is to design efficient schedules which minimize the tumor size under a set of constraints. Our new MA has been shown to be very efficient on solving our multi-drug model

[1]  Zbigniew Michalewicz,et al.  Genetic algorithms and optimal control problems , 1990, 29th IEEE Conference on Decision and Control.

[2]  J. J. Westman,et al.  Cancer Treatment Using Multiple Chemotheraputic Agents Subject to Drug Resistance , 2022 .

[3]  Urszula Ledzewicz,et al.  Optimal Bang-Bang Controls for a Two-Compartment Model in Cancer Chemotherapy , 2002 .

[4]  P. Steerenberg,et al.  Targeting pathophysiological rhythms: prednisone chronotherapy shows sustained efficacy in rheumatoid arthritis. , 2010, Annals of the rheumatic diseases.

[5]  José Luiz Boldrini,et al.  Therapy burden, drug resistance, and optimal treatment regimen for cancer chemotherapy , 2000 .

[6]  R. B. Martin,et al.  Optimal control drug scheduling of cancer chemotherapy , 1992, Autom..

[7]  R. Luus,et al.  Multiplicity of solutions in the optimization of a bifunctional catalyst blend in a tubular reactor , 1992 .

[8]  John H. Holland,et al.  Genetic Algorithms and the Optimal Allocation of Trials , 1973, SIAM J. Comput..

[9]  J. J. Westman,et al.  Compartmental Model for Cancer Evolution: Chemotherapy and Drug Resistance , 2001 .

[10]  Rein Luus,et al.  Global optimization of the bifunctional catalyst problem , 1994 .

[11]  J. Banga,et al.  Dynamic Optimization of Batch Reactors Using Adaptive Stochastic Algorithms , 1997 .

[12]  R. Bassanezi,et al.  Drug kinetics and drug resistance in optimal chemotherapy. , 1995, Mathematical biosciences.

[13]  M. Abundo,et al.  Numerical simulation of a stochastic model for cancerous cells submitted to chemotherapy , 1989, Journal of mathematical biology.

[14]  Kwong-Sak Leung,et al.  Adaptive Elitist-Population Based Genetic Algorithm for Multimodal Function Optimization , 2003, GECCO.

[15]  Rein Luus,et al.  Iterative dynamic programming programs , 2000 .

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

[17]  V. Slysh,et al.  to 4.4 , 1998 .

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

[19]  Kwong-Sak Leung,et al.  Evolutionary Drug Scheduling Model for Cancer Chemotherapy , 2004, GECCO.

[20]  Shea N Gardner,et al.  Cell Cycle Phase-Specific CHemotherapy: Computation Methods for Guiding Treatment , 2002, Cell cycle.

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

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

[23]  Rein Luus,et al.  Optimal control of batch reactors by iterative dynamic programming , 1994 .

[24]  J. L. Boldrini,et al.  Therapy burden, drug resistance, and optimal treatment regimen for cancer chemotherapy. , 2000, IMA journal of mathematics applied in medicine and biology.