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Drug repositioning candidates identified using in-silico quasi-quantum molecular simulation demonstrate reduced COVID-19 mortality in 1.5M patient records
BACKGROUND: Drug repositioning is a key component of COVID-19 pandemic response, through identification of existing drugs that can effectively disrupt COVID-19 disease processes, contributing valuable insights into disease pathways. Traditional non in silico drug repositioning approaches take substa...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8043466/ https://www.ncbi.nlm.nih.gov/pubmed/33851170 http://dx.doi.org/10.1101/2021.03.22.21254110 |
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author | Alamgir, Joy Yajima, Masanao Ergas, Rosa Chen, Xinci Hill, Nicholas Munir, Naved Saeed, Mohsan Gersing, Ken Haendel, Melissa Chute, Christopher G Abid, M. Ruhul |
author_facet | Alamgir, Joy Yajima, Masanao Ergas, Rosa Chen, Xinci Hill, Nicholas Munir, Naved Saeed, Mohsan Gersing, Ken Haendel, Melissa Chute, Christopher G Abid, M. Ruhul |
author_sort | Alamgir, Joy |
collection | PubMed |
description | BACKGROUND: Drug repositioning is a key component of COVID-19 pandemic response, through identification of existing drugs that can effectively disrupt COVID-19 disease processes, contributing valuable insights into disease pathways. Traditional non in silico drug repositioning approaches take substantial time and cost to discover effect and, crucially, to validate repositioned effects. METHODS: Using a novel in-silico quasi-quantum molecular simulation platform that analyzes energies and electron densities of both target proteins and candidate interruption compounds on High Performance Computing (HPC), we identified a list of FDA-approved compounds with potential to interrupt specific SARS-CoV-2 proteins. Subsequently we used 1.5M patient records from the National COVID Cohort Collaborative to create matched cohorts to refine our in-silico hits to those candidates that show statistically significant clinical effect. RESULTS: We identified four drugs, Metformin, Triamcinolone, Amoxicillin and Hydrochlorothiazide, that were associated with reduced mortality by 27%, 26%, 26%, and 23%, respectively, in COVID-19 patients. CONCLUSIONS: Together, these findings provide support to our hypothesis that in-silico simulation of active compounds against SARS-CoV-2 proteins followed by statistical analysis of electronic health data results in effective therapeutics identification. |
format | Online Article Text |
id | pubmed-8043466 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-80434662021-04-14 Drug repositioning candidates identified using in-silico quasi-quantum molecular simulation demonstrate reduced COVID-19 mortality in 1.5M patient records Alamgir, Joy Yajima, Masanao Ergas, Rosa Chen, Xinci Hill, Nicholas Munir, Naved Saeed, Mohsan Gersing, Ken Haendel, Melissa Chute, Christopher G Abid, M. Ruhul medRxiv Article BACKGROUND: Drug repositioning is a key component of COVID-19 pandemic response, through identification of existing drugs that can effectively disrupt COVID-19 disease processes, contributing valuable insights into disease pathways. Traditional non in silico drug repositioning approaches take substantial time and cost to discover effect and, crucially, to validate repositioned effects. METHODS: Using a novel in-silico quasi-quantum molecular simulation platform that analyzes energies and electron densities of both target proteins and candidate interruption compounds on High Performance Computing (HPC), we identified a list of FDA-approved compounds with potential to interrupt specific SARS-CoV-2 proteins. Subsequently we used 1.5M patient records from the National COVID Cohort Collaborative to create matched cohorts to refine our in-silico hits to those candidates that show statistically significant clinical effect. RESULTS: We identified four drugs, Metformin, Triamcinolone, Amoxicillin and Hydrochlorothiazide, that were associated with reduced mortality by 27%, 26%, 26%, and 23%, respectively, in COVID-19 patients. CONCLUSIONS: Together, these findings provide support to our hypothesis that in-silico simulation of active compounds against SARS-CoV-2 proteins followed by statistical analysis of electronic health data results in effective therapeutics identification. Cold Spring Harbor Laboratory 2021-04-06 /pmc/articles/PMC8043466/ /pubmed/33851170 http://dx.doi.org/10.1101/2021.03.22.21254110 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator. |
spellingShingle | Article Alamgir, Joy Yajima, Masanao Ergas, Rosa Chen, Xinci Hill, Nicholas Munir, Naved Saeed, Mohsan Gersing, Ken Haendel, Melissa Chute, Christopher G Abid, M. Ruhul Drug repositioning candidates identified using in-silico quasi-quantum molecular simulation demonstrate reduced COVID-19 mortality in 1.5M patient records |
title | Drug repositioning candidates identified using in-silico quasi-quantum molecular simulation demonstrate reduced COVID-19 mortality in 1.5M patient records |
title_full | Drug repositioning candidates identified using in-silico quasi-quantum molecular simulation demonstrate reduced COVID-19 mortality in 1.5M patient records |
title_fullStr | Drug repositioning candidates identified using in-silico quasi-quantum molecular simulation demonstrate reduced COVID-19 mortality in 1.5M patient records |
title_full_unstemmed | Drug repositioning candidates identified using in-silico quasi-quantum molecular simulation demonstrate reduced COVID-19 mortality in 1.5M patient records |
title_short | Drug repositioning candidates identified using in-silico quasi-quantum molecular simulation demonstrate reduced COVID-19 mortality in 1.5M patient records |
title_sort | drug repositioning candidates identified using in-silico quasi-quantum molecular simulation demonstrate reduced covid-19 mortality in 1.5m patient records |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8043466/ https://www.ncbi.nlm.nih.gov/pubmed/33851170 http://dx.doi.org/10.1101/2021.03.22.21254110 |
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