Determining sex differences in aortic valve myofibroblast responses to drug combinations identified using a digital medicine platform

Aortic valve stenosis (AVS) is a sexually dimorphic disease where aortic valve leaflets develop fibrosis and calcification, leading to heart failure if untreated. Sex differences in AVS progression depend on valvular interstitial cells (VICs) activating to myofibroblasts that drive aberrant extracellular matrix remodeling. To date, no treatment strategies have leveraged cellular sex differences to determine drug combinations that target VIC myofibroblast activation. Here, we harnessed IDentif.AI, an artificial intelligence (AI)-derived platform, to optimize sex-biased synergistic drug combinations that may prevent and reverse VIC myofibroblast activation on hydrogel biomaterials. The results reveal that sex-specific drug response models can be used to predict sex biases in drug efficacy and combinatorial interactions. This study provides a framework for developing AVS treatments through the integration of high-throughput hydrogel cell culture platforms and AI-driven drug optimization. Designing targeted AVS drug combinations may help accelerate AVS drug development and address health disparities in AVS treatment outcomes.