Hybrid approach for drug-target interaction predictions in ischemic stroke models

Abstract

Multiple cell death mechanisms are triggered during ischemic stroke and they are interconnected in a complex network with extensive crosstalk, complicating the development of targeted therapies. We therefore propose a novel framework for identifying disease-specific drug-target interaction (DTI), named strokeDTI, to extract key nodes within an interconnected graph network of activated pathways via leveraging transcriptomic sequencing data. Our findings reveal that the drugs a model can predict are highly representative of the characteristics of the database the model is trained on. However, models with comparable performance yield diametrically opposite predictions in real testing scenarios. Our analysis reveals a correlation between the reported literature on drug-target pairs and their binding scores. Leveraging this correlation, we introduced an additional module to assess the predictive validity of our model for each unique target, thereby improving the reliability of the framework's predictions. Our framework identified Cerdulatinib as a potential anti-stroke drug via targeting multiple cell death pathways, particularly necroptosis and apoptosis. Experimental validation in in vitro and in vivo models demonstrated that Cerdulatinib significantly attenuated stroke-induced brain injury via inhibiting multiple cell death pathways, improving neurological function, and reducing infarct volume. This highlights strokeDTI's potential for disease-specific drug-target identification and Cerdulatinib's potential as a potent anti-stroke drug.