Artificial intelligence-guided green and circular nanotechnology: designing sustainable, biodegradable, and bioresponsive nanocarriers for next-generation precision drug delivery.

Abstract

The convergence of artificial intelligence (AI), green chemistry, and nanocarrier-based drug delivery is reshaping pharmaceutical research toward a more sustainable and precision-oriented paradigm. Conventional nanoparticle design strategies have traditionally prioritized biological efficacy while largely overlooking environmental sustainability, circular manufacturing, and life-cycle accountability, thereby challenging translational pipelines increasingly faced with regulatory, economic, and ecological concerns. This review evaluates how AI methodologies, including machine learning (ML), reinforcement learning (RL), generative modeling, and Bayesian optimization (BO), can guide the rational development of biodegradable and bioresponsive nanocarriers within green chemistry and circular economy (CE) frameworks. We examine current AI applications in nanocarrier engineering, assess green synthesis strategies for biodegradable polymeric and lipid-based systems, analyze bioresponsive release mechanisms, and evaluate life cycle assessment (LCA) methodologies adapted for nanomedicine manufacturing and deployment. We further examine the integration of CE principles into nanocarrier manufacturing and discuss translational case studies, including mRNA lipid nanoparticles (LNPs), poly(lactic-co-glycolic acid- poly(ethylene glycol) (PLGA-PEG) targeted systems, and exosome-based platforms, to highlight both technological advances and unresolved translational challenges. Five major structural barriers are identified: (i) the absence of standardized multimodal nanoformulation datasets for AI training; (ii) limited integration of LCA into multi-objective optimization frameworks; (iii) unclear regulatory pathways for AI-designed nanomedicines; (iv) protein corona-mediated failures in in vivo prediction; and (v) underdeveloped circular manufacturing infrastructures. We also critically discuss key challenges related to reproducibility, scalability, regulatory harmonization, and environmental fate assessment. Finally, we propose a prioritized research roadmap to support the sustainable clinical translation, scalable manufacturing, and long-term regulatory integration of next-generation nanomedicines.