This project proposes further development of a computational discovery platform shown to quantifiably identify and prioritize both targets and compounds for therapeutic intervention, which will accelerate the development of newer, more effective drugs with fewer side-effects. This platform is based on a novel fusion of two state of the art computational approaches (phenolog mapping and functional networks) with proprietary data from Genetic Networks powerful gene-drug screening assays (H-Tech and Y-Tech) that identify drug targets, conserved drug target pathways, and off-target effects (e.g. toxicity) by genome- wide phenotypic profiling. The computational platform identifies conserved and functionally linked genes to define conserved biological modules, groups of gene that work tightly together and contribute to disease phenotypes and drug responses.
The first aim i s to improve the throughput and robustness of Genetic Networks computational platform to provide the pharmaceutical industry with a proven tool to select better compounds for their drug development pipelines and clinical trials.
The second aim i s to improve the prioritization of target genes that will be most effective in treating disease. Many genes have very similar copies, known as paralogs, which can complicate the interpretation of biological data. This project will integrate diverse biological information to prioritize the best gene target based on the disease, the relevant tissue, and how each gene interacts with other genes.
The third aim i s to identify common groups of genes involved in multiple disease and/or multiple drug responses. Understanding the genes involved in multiple biological processes allows pharmaceutical companies to repurpose already approved drug for new purposes and lowers the cost of developing multiple drugs for multiple disease. In addition, this approach will identify the potential drug interactions that can lead to severe side-effects when drugs are taken together without requiring animal testing or risking patient lives. This automated system will rapidly identify and prioritize therapeutic interventions across multiple diseases and will increase the success rate of drug discovery and provide guidance to repurpose existing drugs for new indications. Implementation of the platform described in this proposal will strengthen Genetic Networks' contributions to the goal of bringing new treatments to patients faster.

Public Health Relevance

This project will accelerate drug discovery and reduce its cost by improving the identification and prioritization of both target genes and potential therapeutic compounds, which will benefit both the pharmaceutical industry and patients by supporting the creation of more effective drugs with fewer side-effects. Genetic Networks uses computational algorithms, proprietary data, and large clinical datasets to identify the conserved genetic modules underlying diseases and drug responses, which are the key to understanding how drugs interact with biological systems to effectively treat disease.

National Institute of Health (NIH)
National Center for Advancing Translational Sciences (NCATS)
Small Business Innovation Research Grants (SBIR) - Phase II (R44)
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Special Emphasis Panel (ZRG1)
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Portilla, Lillianne M
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Genetic Networks, LLC
Miami Beach
United States
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