Nanomedicines Targeting Metabolic Pathways in the Tumor Microenvironment: Future Perspectives and the Role of AI

Background: Tumor cells engage in continuous self-replication by utilizing a large number of resources and capabilities, typically within an aberrant metabolic regulatory network to meet their own demands. This metabolic dysregulation leads to the formation of the tumor microenvironment (TME) in most solid tumors. Nanomedicines, due to their unique physicochemical properties, can achieve passive targeting in certain solid tumors through the enhanced permeability and retention (EPR) effect, or active targeting through deliberate design optimization, resulting in accumulation within the TME. The use of nanomedicines to target critical metabolic pathways in tumors holds significant promise. However, the design of nanomedicines requires the careful selection of relevant drugs and materials, taking into account multiple factors. The traditional trial-and-error process is relatively inefficient. Artificial intelligence (AI) can integrate big data to evaluate the accumulation and delivery efficiency of nanomedicines, thereby assisting in the design of nanodrugs. Methods: We have conducted a detailed review of key papers from databases, such as ScienceDirect, Scopus, Wiley, Web of Science, and PubMed, focusing on tumor metabolic reprogramming, the mechanisms of action of nanomedicines, the development of nanomedicines targeting tumor metabolism, and the application of AI in empowering nanomedicines. We have integrated the relevant content to present the current status of research on nanomedicines targeting tumor metabolism and potential future directions in this field. Results: Nanomedicines possess excellent TME targeting properties, which can be utilized to disrupt key metabolic pathways in tumor cells, including glycolysis, lipid metabolism, amino acid metabolism, and nucleotide metabolism. This disruption leads to the selective killing of tumor cells and disturbance of the TME. Extensive research has demonstrated that AI-driven methodologies have revolutionized nanomedicine development, while concurrently enabling the precise identification of critical molecular regulators involved in oncogenic metabolic reprogramming pathways, thereby catalyzing transformative innovations in targeted cancer therapeutics. Conclusions: The development of nanomedicines targeting tumor metabolic pathways holds great promise. Additionally, AI will accelerate the discovery of metabolism-related targets, empower the design and optimization of nanomedicines, and help minimize their toxicity, thereby providing a new paradigm for future nanomedicine development.