Numerical Investigation of Drug Delivery Mechanisms in Pulsatile Flow with Machine Learning Approach

Abstract

This study is significant as it introduces an innovative approach to improving cancer drug delivery. It integrates machine learning algorithms, potentially revolutionizing treatment precision and efficiency. The study investigates nanoparticles modeled using a Levenberg–Marquardt neural network (LM-NN). These nanoparticles can act as dual-function agents to enhance drug delivery to tumors and reduce harmful hydrogen peroxide levels in the bloodstream. The research methodology includes modeling blood flow as pulsatile within a parallel plate channel. It also incorporates porous media to simulate foamy structures. The study addresses heat transfer dynamics and chemically reactive species. It uses nanoparticle concentration equations that account for diffusion, convection, and chemical reaction processes. The study concludes that combining machine learning with fluid dynamics modeling greatly improves the understanding of drug delivery efficiency. It also highlights its impact on physiological factors. Heat transfer, Reynolds number, and parameters like Darcy and Forchheimer are vital for optimizing the delivery of therapeutic agents. Magnetic fields and Schmidt numbers are crucial for regulating blood flow and enhancing treatment outcomes. These insights could lead to improved treatment protocols and better management of blood flow within the cardiovascular system, particularly in areas targeted for cancer treatment. By optimizing drug delivery to cancer tissues, this approach could potentially lower side effects through more controlled and efficient targeting of therapeutic agents. Additionally, the research emphasizes the critical role of computational models in refining drug delivery strategies, offering a pathway toward more personalized and effective cancer treatment protocols in the future.