Integration of Artificial Intelligence and Green Nanotechnology for the Optimization of Nanoparticle-Based Drug Delivery Systems

Abstract

Green nanotechnology offers an eco-friendly alternative to conventional nanoparticle synthesis by utilizing plant extracts, microorganisms, enzymes, and other biological resources. However, optimizing synthesis and formulation parameters remains challenging due to complex interactions that influence nanoparticle characteristics and performance. This study explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) with green nanotechnology to optimize nanoparticle-based drug delivery systems. An AI-driven framework can utilize experimental data from green nanoparticle synthesis to establish relationships between key formulation and process variables, including biological extract concentration, precursor concentration, pH, temperature, and reaction time, and critical quality attributes such as particle size, zeta potential, drug entrapment efficiency, and drug release. Machine learning algorithms, including Artificial Neural Networks, Random Forest, and Support Vector Machines, can be employed to predict optimal formulation conditions and enhance formulation development. The integrated approach is expected to minimize trial-and-error experimentation, accelerate formulation optimization, improve nanoparticle quality and drug delivery performance, and enhance reproducibility while reducing resource consumption and environmental impact. AI-integrated green nanotechnology represents a promising multidisciplinary strategy for developing sustainable, efficient, and safer nanoparticle-based drug delivery systems.