Artificial intelligence in central-peripheral interaction organ crosstalk: the future of drug discovery and clinical trials

Abstract

Drug discovery before the 20th century often focused on single genes, molecules, cells, or organs, failing to capture the complexity of biological systems. The emergence of protein-protein interaction network studies in 2001 marked a turning point and promoted a holistic approach that considers the human body as an interconnected system. This is particularly evident in the study of bidirectional interactions between the central nervous system (CNS) and peripheral organs, which are critical for understanding health and disease. Understanding these complex interactions requires integrating multi-scale, heterogeneous data from molecular to organ levels, encompassing both omics (e.g., genomics, proteomics, microbiomics) and non-omics data (e.g., imaging, clinical phenotypes). Artificial intelligence (AI), particularly multi-modal models, has demonstrated significant potential in analyzing CNS-peripheral organ interactions by processing vast, heterogeneous datasets. Specifically, AI facilitates the identification of biomarkers, prediction of therapeutic targets, and simulation of drug effects on multi-organ systems, thereby paving the way for novel therapeutic strategies. This review highlights AI's transformative role in CNS-peripheral interaction research, focusing on its applications in unraveling disease mechanisms, discovering drug targets, and optimizing clinical trials through patient stratification and adaptive trial design.