Leveraging Drug-Target Interaction Data for the Translation of Computational Models into Clinically Actionable Interventions
Department
Research
Document Type
Conference Proceeding
Publication Title
2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Conference Name
IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Conference Date
2021-12-09
Abstract
Computational modeling is an effective tool for studying complex disease. However, solutions to many models are purely mathematical and cannot immediately provide clinical insights. To overcome this barrier, we propose a series of quantitative scoring metrics that can be used in combination with drug-target interaction data to identify solutions that are readily clinically actionable. Furthermore, we introduce methods for the prediction and ranking of pharmaceutical interventions that closely align with these high-scoring solutions, with an emphasis on robustness across multiple solutions. We demonstrate these methods on a previously-described model of COVID-19 induced cytokine storm. These scoring methods ultimately identify multiple pharmaceutical candidates that have been shown to be effective in reducing mortality rates in COVID-19 patients.
First Page
2014
Last Page
2021
DOI
10.1109/BIBM52615.2021.9669536
Publication Date
1-14-2022
Publisher
IEEE
Recommended Citation
Richman, S. C., Lyman, C. A., Morris, M. C., & Broderick, G. (2022). Leveraging Drug-Target Interaction Data for the Translation of Computational Models into Clinically Actionable Interventions. 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2014-2021. https://doi.org/10.1109/BIBM52615.2021.9669536
Comments
See full list of authors at journal website.