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.

Exploring the Potential of Anthraquinone-Based Hybrids for Identifying a Novel Generation of Antagonists for the Smoothened Receptor in HH-Dependent Tumour.

Natural products (NPs) are highly profitable pharmacological tools due to their chemical diversity and ability to modulate biological systems. Accessing new chemical entities while retaining the biological relevance of natural chemotypes is a fundamental goal in the design of novel bioactive compounds. Notably, NPs have played a crucial role in understanding Hedgehog (HH) signalling and its pharmacological modulation in anticancer therapy.