Drug repositioning candidates identified using in-silico quasi-quantum molecular simulation demonstrate reduced COVID-19 mortality in 1.5M patient records


Abstract

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.

Conflict of interest statement

CONFLICTS OF INTEREST

Joy Alamgir is founder of ARIScience. Melissa Haendel is a co-founder of Pryzm Health.

Figures

Figure 1:
Figure 1:. Gender, Smoker, Diabetes, Hypertension by age group (as fraction of age group) for treated (with Metformin) and matched control cohorts across all CCI used for statistical analysis.
The darker shade of each colored pair is Control (untreated), while the lighter shade is Treated (with Metformin). For example, the dark purple (control) and light purple (treated) bars on each age group represent the fraction of patients in those age groups that are hypertensive. Top left box: death rate by age group for COVID+ patients in N3C.
Figure 2:
Figure 2:. Image of triamcinolone (red) interacting with SARS-CoV-2 NSP1 protein (blue) at neutral pH.
Darker shades signify regions of positive partial charges and lighter shades signify regions of negative partial charges.
See this image and copyright information in PMC

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