Green nanoparticles in drug delivery: Translational challenges and AI solutions

Green nanotechnology offers a sustainable and highly promising alternative for the biosynthesis of nanoparticles (NPs) tailored for advanced drug delivery systems, leveraging plant and microbial extracts due to their high biocompatibility and low systemic toxicity. However, the transition of these biogenic platforms from laboratory proof-of-concept to clinical-grade therapeutics is fundamentally bottlenecked by raw material heterogeneity, poor batch-to-batch reproducibility, a lack of process standardization, and the unpredictable clinical efficacy of the passive enhanced permeability and retention (EPR) effect. To address these systemic barriers, this review provides a critical translational analysis reframed around advanced computational engineering and digital design paradigms. We introduce a comparative Translation Readiness Matrix that evaluates six major green nanoparticle platforms across six distinct clinical criteria, systematically isolating current safety and regulatory gaps. Crucially, this review explores the paradigm shift driven by the integration of Artificial Intelligence (AI) and Quality by Digital Design (QbDD) frameworks. We evaluate how machine learning architectures optimize high-dimensional formulation spaces, how graph neural networks predict protein corona dynamics, and how predictive modeling enhances patient stratification to mitigate EPR variability. Furthermore, we analyze advanced fabrication methodologies, specifically microfluidics, continuous flow synthesis, and real-time AI-integrated quality control that stabilize hydrodynamic synthesis environments and transition manufacturing from empirical batch optimization to continuous quality assurance. By linking biogenic synthesis with intelligent digital architectures, this review delivers a definitive engineering blueprint to transform green nanoparticles from fragmented laboratory concepts into reproducible, regulatory-compliant industrial realities.