Optimum multi-drug regime for compartment model of tumour: cell-cycle-specific dynamics in the presence of resistance

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

“…Panjwani et al [22] extended the two linear tumor growth models proposed by Panetta et al [21,23], which consider drug resistance and cell cycle-specific drugs. They proposed a dynamic model of tumor growth capable of simultaneously simulating acquired drug resistance, cell cycle specificity, and multi-agent combination chemotherapy.…”

“…Karar et al [8] used the Invasive Weed Optimization (IWO) algorithm to tune the parameters of an intuitionistic fuzzy logic controller with the final tumor size as the single optimization objective. Qods et al [22] used the Genetic Algorithm (GA) to solve the optimal control problem of cell cycle-specific chemotherapy dosing, again using the total number of tumor cells at the end of the regimen as the optimization objective, to find the optimal drug accumulation and drug dosage. Dhieb et al [16] chose the Particle Swarm Optimization (PSO) algorithm to minimize the duration of the chemotherapy course and the tumor size as the optimization objectives, and the two objectives were aggregated in a weighted manner.…”

“…They initially applied the NSGA-II algorithm to a Two-Degree-of-Freedom Fractional-Order Proportional-Integral-Derivative controller (2DOF FOPID) in a cascaded drug scheduling model [12], demonstrating its superior performance over the Two-Degree-of-Freedom Proportional-Integral-Derivative controller (2DOF PID) in terms of toxicity level peak, drug concentration mean, and the quantity of normal tissue cells. Later, they employed NSGA-II for open-loop drug scheduling optimization [22], considering two optimization objectives, which were the total number of tumor cells and cumulative damage to normal tissue cells. The performance of NSGA-II in open-loop drug scheduling was superior to that of the Modified Fractional-Order Internal Model Controller (MFOIMC) and the Integral−Proportional−Derivative (I-PD) controller in closed-loop drug scheduling under two scenarios of drug resistance.…”

“…The combined cell cycle-specific and non-cycle-specific chemotherapy proposed in this paper is based on the linear tumor growth model of drug resistance and cell cycle specificity proposed by Panetta et al [21,23] and further modified by Bharti Panjwani et al [22]. All cells in the body undergo a series of proliferation stages involving division and replication during growth, which are divided into the interphase and the mitotic phase, with a resting phase indicating temporary cell inactivity.…”

Self-regulatory Fractional Fuzzy Control for Dynamic Systems: An Analytical Approach

“…The supremacy of NSGA-II lies in converting multiple objectives into a single measure by generating a set of Pareto fronts, sorted on the basis of non-domination. To solve multiple objectives problems in engineering fields, NSGA-II is implemented because of its efficiency, simplicity, and elitism [9,10,13,47,48]. NSGA-II performs all the computations in this work and finds the controller parameters for its optimum performance.…”