PROJECT SUMMARY Despite advances in our understanding of rare genetic diseases and their causes, only 8% of these diseases have targeted drugs. Much of this arises from the disconnect between the inhibitory nature of drug molecules and a predominance of loss-of-function mechanisms in such diseases. There has been a growing appreciation of the role of gain-of-function variants in this context, especially towards drug repurposing. More specifically, we and others have shown that even subtle changes in function such as alterations of post-translational modification or molecular interaction sites can frequently lead to such disorders. It is unclear to what extent these observations generalize and are actionable from a therapeutic perspective. Common Fund data sets such as those from the Gabriella Miller Kids First, Undiagnosed Disease Network, the Illuminating the Druggable Genome and LINCS programs provide a unique opportunity to assess this computationally. Our central hypothesis is that gain-of-function variants account for a much larger proportion of rare genetic diseases than currently known and in silico functional profiling can be used to computationally identify such diseases. The proposed work will test this hypothesis through two aims. In Aim 1, we will apply our previously- developed predictors of variant impact towards the identification of known and predicted disease-associated variants in large Common Fund genomic data sets. In Aim 2, we will subset out those variants that impact druggable biochemical properties either directly or indirectly, to thus, infer novel drug-disease pairs. Over the award period, the principal investigator (PI) will leverage his and his team's expertise in variant interpretation, machine learning and bioinformatics knowledgebases towards the systematic integration of genomic and drug- related data from multiple Common Fund data sets to identify candidate drugs that can be repurposed for rare genetic diseases. This work will be carried out at the Icahn School of Medicine at Mount Sinai, home to world- renowned researchers in human disease genetics, robust computational infrastructure, and a thriving biomedical data science training environment. The proposed research will not only provide valuable pilot data for experimental validation of promising drug repurposing candidates but will serve as the foundation for future computational methodology development that will expand the scope of variants and mechanisms that can be queried. The work is expected to have broad impact, as it presents a new mechanism-centric, data-driven approach to identifying drug repurposing candidates for rare genetic diseases, that is generalizable to other situations.

PROJECT NARRATIVE Drug repurposing has emerged as an important therapeutic strategy in the context of rare genetic diseases. Due to the mostly inhibitory nature of existing drug molecules, they are most effective on genetic variants that are likely to increase a protein's function, but the identification of such variants remains an open challenge. The objective of the proposed research is to apply variant impact prediction methods and other computational approaches, on publicly available Common Fund data sets to: (1) identify disease-associated variants that increase the propensity for specific biochemical functions in proteins, and (2) identify candidate drug molecules that directly or indirectly inhibit these functions.