Optimized drug regimen and chemotherapy scheduling for cancer treatment using swarm intelligence

Najmeddine Dhieb (), Ismail Abdulrashid (), Hakim Ghazzai () and Yehia Massoud ()
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Najmeddine Dhieb: Stevens Institute of Technology
Ismail Abdulrashid: The University of Tulsa
Hakim Ghazzai: Stevens Institute of Technology
Yehia Massoud: King Abdullah University of Science and Technology (KAUST)

Annals of Operations Research, 2023, vol. 320, issue 2, No 9, 757-770

Abstract: Abstract This note presents a novel chemotherapy protocol for physicians to treat cancer tumors. Mathematical modeling, analysis, and simulations are used to describe the detailed dynamics of tumor, effector-immune cells, lymphocyte population, and chemotherapy drug, inside the patient body. An optimized scheduling alternating between treatment and relaxation sessions is determined to minimize the tumor size at the end of therapy period and overcome the toxicity level of patient’s organs. To this end, we propose and allot relaxation sessions between two consecutive treatment sessions so that the body can partially recover. For each treatment period, we determine an optimal control strategy to minimize the tumor size and drug consumption without negatively affecting the natural cells. Finally, a particle swarm optimization-based approach is developed in order to ascertain the duration of each therapy session. The obtained results show that the proposed solution presents significant advantages in drug dosage, tumor reduction, and chemotherapy scheduling sessions compared to mathematical-based state-of-art approaches.

Keywords: Chemotherapy; Optimization; Drug regimen; optimal control model; Scheduling (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10479-021-04234-6

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