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All Studies   Meta Analysis   Recent: 
0 0.5 1 1.5 2+ Mortality 27% Improvement Relative Risk Mortality (b) 34% Mortality (c) 30% c19early.com/mf Alamgir et al. Metformin for COVID-19 Prophylaxis Favors metformin Favors control
Alamgir, 22,124 patient metformin prophylaxis study: 27% lower mortality [p<0.0001] https://c19p.org/alamgir
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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
Alamgir et al., medRxiv, doi:10.1101/2021.03.22.21254110 (Preprint)
6 Apr 2021    Source   PDF   Share   Tweet
In Silico study followed by PSM analysis of the National COVID Cohort Collaborative data in the USA, showing 27% lower mortality with metformin use.
ABSTRACTBackgroundDrug 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.MethodsUsing 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.ResultsWe 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.ConclusionsTogether, 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.
risk of death, 27.0% lower, OR 0.73, p < 0.001, treatment 11,062, control 11,062, all patients, RR approximated with OR.
risk of death, 34.0% lower, OR 0.66, p = 0.007, treatment 5,369, control 5,369, diabetic patients with CCI≤3, RR approximated with OR.
risk of death, 30.0% lower, OR 0.70, p = 0.02, treatment 2,525, control 2,525, non-diabetic patients with CCI≤3, RR approximated with OR.
Effect extraction follows pre-specified rules prioritizing more serious outcomes. Submit updates
Alamgir et al., 4/6/2021, retrospective, database analysis, USA, North America, preprint, 11 authors.
Contact: joy@ariscience.com, ruhul_abid@brown.edu.
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