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

Abstract

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.