ARTIFICIAL INTELLIGENCE AND DIGITAL TWIN TECHNOLOGY IN POLYMERIC NANOSPONGES: CONVERGENCE, APPLICATIONS, AND FUTURE DIRECTIONS

The polymeric nanosponges, which are hyper-cross-linked three-dimensional nanostructures, have progressed from a laboratory curiosity to a platform for delivering drugs, decontaminating environments and formulating cosmetics in the last 20 years. It’s their porous, cage-like structure that can be precisely engineered and fine-tuned virtually endlessly by changing the parent polymer, the cross-linking compound, and the reaction conditions, providing formulators with control over the particle size, the amount of drug loaded and the drug release rate. The same tunability has been the stumbling block in nanosponge synthesis, however, because researchers have to test dozens of different ratios of polymers to cross-linkers before they find a combination that works. This review explores how these two computational technologies, artificial intelligence (AI) and digital twins, are starting to shift that equation. We illustrate the evolution of nanosponge chemistry, highlight the use of machine learning models for predicting nanosponge yield, particle size, encapsulation efficiency, and release behavior from formulation parameters, and discuss how digital twin concepts, initially developed for pharmaceutical production lines, are now being applied to model nanosponge production lines in real time. We also discuss the practical challenges that make it difficult to use these tools in the laboratory, such as the small and fragmented datasets, the high costs of constructing a twin and validating it, and the regulatory framework that wasn’t designed for a self-updating model; and we provide a glimpse at where the field is likely to go in the next five to 10 years.