Optimal administration strategy in chemotherapy regimens using multi-drug cell-cycle specific tumor growth models

Qods, Parak; Arkat, Jamal; Batmani, Yazdan

doi:10.1016/j.bspc.2023.105221

Cited by 2 publications

(1 citation statement)

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“…Batmani et al [4] used the cycle-specific tumor growth compartmentalization model proposed by Westman et al [10] to determine cycle-specific chemotherapy dosing under the influence of drug resistance and chose compatible regimens for the three special cases of young patients, old patients, and pregnant patients from the set of non-dominated solutions obtained. Qods et al [11] similarly used the Westman et al [10] compartmentalized model to explore the efficacy of sequential and simultaneous dosing strategies in the treatment of rectal cancer, using a combination of 5-Fluorouracil (5-FU) and CPT-11, optimizing the dosage of the drugs by using Genetic Algorithm (GA). The simultaneous dosing strategy in the experiments achieved maximum tumor reduction and demonstrated stable performance in simulation experiments with different tumor sizes.…”

Section: Literature Review 21 Chemotherapy Dose Optimization Based On...mentioning

confidence: 99%

Chemotherapy Regimen Optimization Using a Two-Archive Multi-Objective Squirrel Search Algorithm

Huo,

Liang,

Huo

2024

Applied Sciences

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Chemotherapy is one of the most effective treatments for cancer, but the efficacy of standard chemotherapy regimens is often limited by toxicities and the individual heterogeneity of cancers. Precise dosing is an important tool to improve efficacy and reduce significant differences in toxicity. However, most of the existing studies on chemotherapy optimization fail to fully consider the toxic side effects, drug resistance, and drug combinations, and thus the chemotherapy regimens obtained may face difficulty in achieving the expected efficacy and also affect the subsequent treatment. Therefore, this paper establishes a tumor growth model for the combination chemotherapy of cell cycle-specific and non-cycle-specific drugs and includes the factors of acquired drug resistance and toxic side effects, proposing an improved multi-objective Squirrel Search Algorithm, the TA-MOSSA, to solve the problem of accurate chemotherapy drug optimization. In this paper, experiments were conducted to analyze the efficacy of chemotherapy dosing regimens obtained by the TA-MOSSA based on the tumor growth model, and the results show that the TA-MOSSA can provide effective chemotherapy regimens for patients who take different treatment approaches.

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