Artificial Intelligence and Biotechnology in the Sustainable Design of Polymeric Biomaterials and Hybrid Drug Delivery Systems

The integration of circular bioeconomy principles, biotechnology, and artificial intelligence (AI) offers new opportunities for the rational design of sustainable polymeric biomaterials and hybrid drug delivery systems. Rather than reviewing these fields independently, this review examines how they can be connected within a feedstock-to-therapy framework, linking the selection and characterization of bio-derived resources with polymer production, material formulation, performance prediction, and biomedical application. Particular attention is given to the challenge of translating variable biological feedstocks, including plant biomass and microbial resources, into materials with reproducible physicochemical and biological properties. The review discusses natural polymers, microbial and enzymatically produced biopolymers, and biomass-derived functional compounds as building blocks for hydrogels, nanocomposites, scaffolds, and controlled drug delivery platforms. It further considers how biotechnology, including metabolic engineering and synthetic biology, can expand the range and improve the consistency of available material components. Within this framework, machine learning, materials informatics, and hybrid mechanistic-data-driven models are evaluated in terms of their potential to support feedstock characterization, formulation optimization, and prediction of material properties and drug-release behavior. The review identifies key barriers to integrating these approaches, including heterogeneous data, limited dataset sizes, feedstock variability, and insufficient experimental validation across different stages of material development.