Artificial intelligence-enabled cross-scale integration of traditional Chinese medicine and biomedicine for sepsis: from mechanisms to delivery.

Sepsis is an organ dysfunction caused by a dysregulated host immune response to infection. Its pronounced heterogeneity and cross-scale pathological disturbances have resulted in poorly defined core therapeutic targets and inefficient drug delivery. Artificial intelligence, with its capacity for high-dimensional data integration, can serve as a data-integrative and hypothesis-generating tool, offering new avenues for exploring potential solutions to the aforementioned bottlenecks. This review summarizes recent advances in AI-driven multi-omics-based mechanistic dissection, the use of nanodelivery systems to optimize the in vivo behavior of both biomedical and botanical drugs, and AI-assisted nanocarrier design. At the mechanistic level, AI integrates multi-omics data with graph neural networks to provide computational clues for precise molecular subtyping and the identification of potential candidate targets such as S100A8/A9 and TREM-1. At the delivery level, nanocarriers help overcome the off-target toxicity of biomedical agents and the poor bioavailability of botanical drugs, while lesion acidification, high ROS levels, high MMP expression, and the EPR effect provide a biological basis for stimuli-responsive delivery. At the integration level, AI translates target information and microenvironmental parameters into carrier design parameters, offering computational support for material screening and response threshold optimization. A conceptual framework for an integrated "target recognition-drug matching-carrier design-subtype adaptation" decision model is proposed, which may inform the matching of combined biomedical and botanical drug regimens with nanodelivery systems based on patient molecular subtypes. This cross-scale integration framework may offer a reference direction for research on sepsis and other heterogeneous inflammatory diseases.