AI-driven Drug Discovery Targeting Cardiac Fibrosis in Aging Heart

Cardiac fibrosis, a hallmark of aging, leads to tissue stiffness, impaired cardiac function, and heart failure, significantly reducing quality of life in elderly populations. Despite its clinical burden, no FDA-approved therapies exist for treating cardiac fibrosis, underscoring an urgent need for innovative solutions. This UG3/UH3 study aims to discover and validate novel antifibrotic compounds by leveraging cutting-edge technologies, including human induced pluripotent stem cells (iPSCs), artificial intelligence (AI), and advanced preclinical models. The UG3 phase will focus on high-throughput screening (HTS) of over 225,000 compounds using iPSC-derived cardiac fibroblast (iPSC-CF) reporter lines to identify lead candidates. The ADMET-AI platform will filter these candidates for favorable drug-like properties and low toxicity. In the UH3 phase, selected compounds will undergo robust in vitro and in vivo validation. This includes testing in “cell villages” for population-scale evaluations and 3D cardiac organoids to assess efficacy and safety in tissue-like environments. The top two hits, including a repurposed drug and a novel compound, will be tested in aging mice to examine their therapeutic potential in restoring cardiac function and reducing fibrosis. This multidisciplinary approach integrates fibrosis research, AI/ML tools, and iPSC technology to address a critical unmet need in aging cardiovascular health. By targeting senescent fibroblasts and employing innovative drug screening platforms, this UG3/UH3 study has the potential to advance the discovery of effective antifibrotic therapies, paving the way for clinical translation and improving outcomes in aging populations. Project Number: 1UG3AG097135-01 | Fiscal Year: 2025 | NIH Institute/Center: National Institute on Aging (NIA) | Principal Investigator: Joseph Wu (+2 co-PIs) | Institution: STANFORD UNIVERSITY, STANFORD, CA | Award Amount: $900,195 | Activity Code: UG3 | Study Section: Special Emphasis Panel[ZRG1 CN-V (55)] View on NIH RePORTER: https://reporter.nih.gov/project-details/11257931