Leveraging Drug-Target Interaction Data for the Translation of Computational Models into Clinically Actionable Interventions

Authors

Spencer C. Richman, Rochester Regional HealthFollow
Cole A. Lyman, Rochester Regional HealthFollow
Matthew C. Morris, Rochester Regional HealthFollow
Gordon Broderick, Rochester Regional HealthFollow

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

Comments

See full list of authors at journal website.

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