Date of Award

2022-05-01

Degree Name

Master of Science

Department

Computational Science

Advisor(s)

Suman Sirimulla

Abstract

For more than two years, the COVID-19 pandemic has upended the lives of billions of individualsworldwide leading to disruptions in healthcare, the economy and society at large. As the pandemic enters its third year, the human impact cannot be overstated and the need to develop effective pharmaceuticals remains. Though there currently exits FDA-approved medications for COVID-19, the emergence of novel variants, such as Omicron, highlights the importance of discovering new therapies which will continue to be effective regardless of the pandemicâ??s progression. Because discovering new medications is a costly and timeintensive endeavor, my approach entails drug repurposing to test medications which are already in use. In this publication, combinations of previously approved drugs are tested for synergy against SARS-CoV-2. The intention of using combinations of drugs is to improve patient outcomes and prevent treatment escape. My approach uses various machine learning models to predict synergy for repurposed drugs which have been previously shown to have activity against SARS-CoV-2. Drug synergy models are made publicly available to researchers hoping to study SARS-CoV-2. In addition to the in silico experiments, top-scoring combinations are experimentally validated in vitro.

Language

en

Provenance

Received from ProQuest

File Size

121 p.

File Format

application/pdf

Rights Holder

Jason Eden Sanchez

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