SBIR Phase I: Rapid repurposing and translation of COVID-19 therapeutics using a AI-based biosimulation platform: application to ACE2 and Ca2+ multi-target therapies (COVID-19)
- Funded by National Science Foundation (NSF)
- Total publications:0 publications
Grant number: 2035834
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Key facts
Disease
COVID-19Start & end year
20212021Known Financial Commitments (USD)
$255,908Funder
National Science Foundation (NSF)Principal Investigator
Jyotika VarshneyResearch Location
United States of AmericaLead Research Institution
VERISIM LIFE INCResearch Priority Alignment
N/A
Research Category
Therapeutics research, development and implementation
Research Subcategory
Pre-clinical studies
Special Interest Tags
N/A
Study Type
Non-Clinical
Clinical Trial Details
N/A
Broad Policy Alignment
Pending
Age Group
Unspecified
Vulnerable Population
Unspecified
Occupations of Interest
Unspecified
Abstract
The broader impact of this SBIR Phase I project is to provide immediate positive outcomes in the COVID-19 therapeutics. The platform will use artificial intelligence (AI) to develop models describing and ultimately predicting how specific individuals respond to various therapeutic combinations. This process can be extended to other diseases and conditions.
This SBIR Phase I project will utilize an artificial intelligence/machine learning-based in silico platform for optimizing combinations and dosing strategies of approved drugs as a repurposed, multi-target approach against COVID-19 to minimize the viral load, infection and replication in specific tissues. A key objective of the proposed work is to target COVID-19 replication transmission and effectively lower the net viral load, subsequently increasing clinical efficacy and patient survival. This project will develop a multiscale mechanistic model of drug-target interaction, encompassing expression levels, and interaction kinetics of therapies with signaling pathways to prevent viral infection and inflammation that can result in widespread tissue damage and increased mortality. The technology will enable, with high predictive accuracy, the selection of individuals and combinations of proposed drugs to optimize the dosage regimen that leads to a maximum therapeutic efficacy. This will further enable physicians to rapidly redeploy these therapies off-label in COVID-19 patients in the immediate term.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
This SBIR Phase I project will utilize an artificial intelligence/machine learning-based in silico platform for optimizing combinations and dosing strategies of approved drugs as a repurposed, multi-target approach against COVID-19 to minimize the viral load, infection and replication in specific tissues. A key objective of the proposed work is to target COVID-19 replication transmission and effectively lower the net viral load, subsequently increasing clinical efficacy and patient survival. This project will develop a multiscale mechanistic model of drug-target interaction, encompassing expression levels, and interaction kinetics of therapies with signaling pathways to prevent viral infection and inflammation that can result in widespread tissue damage and increased mortality. The technology will enable, with high predictive accuracy, the selection of individuals and combinations of proposed drugs to optimize the dosage regimen that leads to a maximum therapeutic efficacy. This will further enable physicians to rapidly redeploy these therapies off-label in COVID-19 patients in the immediate term.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.