# Variant impact prediction on Common Fund data sets towards drug repurposing for rare genetic diseases

> **NIH NIH R03** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2024 · $338,000

## Abstract

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.

## Key facts

- **NIH application ID:** 10988576
- **Project number:** 1R03OD038386-01
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Vikas Rao Pejaver
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $338,000
- **Award type:** 1
- **Project period:** 2024-09-05 → 2026-09-04

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10988576

## Citation

> US National Institutes of Health, RePORTER application 10988576, Variant impact prediction on Common Fund data sets towards drug repurposing for rare genetic diseases (1R03OD038386-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10988576. Licensed CC0.

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