Determining sex differences in drug combinations targeting aortic valve myofibroblast activation using an artificial intelligence derived platform

Abstract

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 effectively target VIC myofibroblast activation. Here, we harnessed IDentif.AI, an artificial intelligence (AI)-derived drug optimization platform, to optimize sex-specific synergistic drug combinations that may prevent and reverse VIC myofibroblast activation on hydrogel biomaterials. The results reveal that anti-fibrotic drug efficacy and combinatorial interactions are dependent on cell sex. This study provides a framework for developing clinically relevant AVS treatment strategies through the integration of high-throughput hydrogel cell culture platforms and AI-driven drug optimization. The workflow towards designing targeted AVS drug combinations may help accelerate AVS drug development for male and female patients and address health disparities in AVS treatment outcomes.