AI-enabled, QbD-aligned Predictive, and Sustainable Design of Natural Polymer-based Drug Delivery Systems

Natural polymers such as chitosan, alginate, cellulose, gelatin, and silk fibroin have become central to modern drug delivery research due to their biocompatibility, biodegradability, and environmental sustainability. However, variability in source, molecular weight, and crosslinking chemistry often results in inconsistent formulation performance and limited scalability. To overcome these challenges, artificial intelligence (AI) and machine learning frameworks have been increasingly integrated into formulation science under the quality by design paradigm. This review synthesizes current advances in AI-assisted modeling and optimization of natural polymer drug delivery systems, highlighting how predictive algorithms capture nonlinear relationships among polymer structure, process variables, and release kinetics. Neural-network and Bayesian-optimization models demonstrate accurate prediction of encapsulation efficiency and dissolution profiles, while hybrid mechanistic-AI and physics-informed neural networks enhance interpretability by embedding kinetic and diffusion equations. The review also discusses data-generation workflows, FAIR-compliant standards, and polymer-informatics databases that enable interoperable, reproducible modeling. Collectively, these developments establish a pathway toward data-driven, sustainable pharmaceutics, where predictive and eco-designed formulations replace empirical trial-and-error methods. Remaining challenges include dataset standardization, model transparency, and regulatory validation. Addressing these will accelerate the translation of intelligent polymer design into reproducible, scalable, and environmentally responsible drug delivery innovations.