A multi-objective optimization framework through genetic algorithm for hyperthermia-mediated drug delivery

This study presents an approach to the multi-objective optimization of hyperthermia-mediated drug delivery using thermo-sensitive liposomes (TSLs) for the treatment of hepatocellular carcinoma. The research focuses on addressing the non-optimal coupling methods that combine thermal treatments and chemotherapy by employing a Multi-Objective Genetic Algorithm (MOGA) optimization process, in order to identify the right combination of design variables to achieve better treatment outcomes. The proposed model integrates Computational Fluid Dynamics (CFD) analysis using the Pennes’ Bioheat equation for tissue heating and a convection-diffusion model for drug delivery. The goal is to maximize the fraction of killed cancer cells through the pharmaceutical treatment while minimizing thermal damage to the tissue, aiming to not hinder the drug feeding from the vascular system. The optimization considers several design variables, including heating power, timing, and the number of antenna slots for the microwave heating. Simulations results suggest that a two-slots antenna configuration with a specific heating schedule yields optimal therapeutic outcomes by maximizing drug concentration in the tumor while limiting damage to healthy tissue. The results of the CFD analysis also show a significant improvement in the treatment outcomes compared to non-optimized results proposed previously in the literature, leading to an increase from the 10 % up to the 33 % for the fraction of killed cells function. The proposed optimization through Genetic Algorithm framework could significantly improve patient-specific treatment planning for hyperthermia-mediated drug delivery.