Artificial intelligence and machine learning-enabled smart nanodrug delivery systems for tissue regeneration and precision therapeutic release

Abstract

Rational nanoparticle design, stimuli-responsive release control, and release kinetics matching with changing tissue microenvironment are improving preclinical and clinical regenerative therapy results and clinical translation utilizing artificial intelligence and machine learning. This review is intended to explore the use of interdisciplinary strategies that integrate computational design and experimental validation to advance tissue-focused drug delivery systems. Using AI-enabled procedures to increase encapsulation efficiencies and tunability of release and target engagement has led to the acceleration of the inverse design and closed-loop optimization processes in numerous preclinical models. Unfortunately, sparse metadata and tiny heterogeneous datasets restrained repeatability. Incompetent benchmark repositories, inconsistent reporting, and data sharing pipelines. The regulatory paths for algorithm-informed device-drug products are new and require models, quantification of uncertainty, sterilization validation, and clinical-grade stability data. The pragmatic roadmap emphasizes short-term metadata standards, intermediate community-based benchmarks, and long-term adaptive clinical trials linking computational outcomes with biomarker-driven goals. A consolidated series of technical, translational, and governance recommendations is presented with an actionable checklist made for bridging computational design with clinical translation, such as a synthesis of governance and experiments checklist, which has never been seen before, for faster real-world implementation.