Advancing drug delivery: Neural network perspectives on nanoparticle-mediated treatments for cancerous tissues

Abstract

The article introduces a machine learning-based approach to enhance drug delivery to cancerous tissues via the human cardiovascular system. It addresses the need for improved drug transport in the presence of cardiovascular obstacles, such as foamy structures, which are implicated in cardiovascular diseases. By examining the impact of nanoparticles on drug transport and biomarkers like hydrogen peroxide, the study refines drug delivery strategies. The motivation is to understand how nanoparticles not only facilitate drug delivery to cancer cells but also mitigate hydrogen peroxide concentration in the blood. This study explores the interaction between nanoparticle behavior, hydrogen peroxide concentration, and drug delivery using machine learning techniques. The integration of modern-day approaches, mainly the Levenberg–Marquardt neural network (LM-NN), offers a healthy assessment of drug delivery systems. Blood flow is exhibited numerically as pulsatile flow in a parallel plate channel, incorporating the properties of foamy structures modeled as porous media. Nanostructures are treated as drug carriers by a concentration equation that considers diffusion, convection, and reaction dynamics in the blood flow. The investigation reveals that nanostructures serve a dual function by augmenting drug delivery to cancer cells and reducing hydrogen peroxide levels in the blood. Machine learning techniques, particularly the LM-NN, identify vital factors affecting drug delivery efficiency, offering insights into optimizing physiological parameters, drug properties, and patient-specific variables. This research presents a novel approach by integrating machine learning, specifically LM-NN, to optimize nanoparticle-mediated drug delivery. It exclusively combines modeling blood flow as pulsatile within a parallel plate channel with the contemplation of foamy structures as porous media. This dual-focus approach advances up-to-date methodologies by providing an inclusive understanding of the interplay between drug carriers and biomarkers, leading to potential enhancements in cancer treatment strategies.

Keywords

• drug delivery
• cardiovascular system
• nanofluidics
• machine learning
• cancer tissues