An ischemic stroke-on-a-chip model integrating with machine learning for screening of drug candidate

Abstract

Ischemic stroke is a leading cause of death and long-term disability worldwide, characterized by the sudden loss of cerebral blood flow, resulting in energy failure, oxidative stress, inflammation, and blood-brain barrier (BBB) disruption. Despite its clinical significance, current preclinical models inadequately recapitulate the multifaceted pathophysiology of cerebral ischemia and facilitate systematic therapeutic exploration. Here, we engineered a human iPSC-derived BBB-on-a-chip (iBBB-on-a-chip) platform that can simulate ischemic pathology through controlled oxygen-glucose deprivation (OGD). The model exhibited characteristic features of ischemia-induced BBB impairment, including disruption of endothelial tight junctions, pericytes fragmentation, and increased permeability. By integrating transcriptomic profiling with weighted gene co-expression network analysis (WGCNA), we identified stroke-related pathways, and applied machine learning (random forest and LASSO) to screen hub genes for biomarker discovery. Using the Connectivity Map database and molecular docking calculations, we identified coumarin as a potential therapeutic agent and experimentally confirmed its protective role in the iBBB-on-a-chip. This strategy establishes a novel paradigm combining organ-on-a-chip technology with machine learning-driven data analysis, creating an innovative platform for the study of cerebrovascular disease and drug screening