Drug repositioning candidates identified using in-silico quasi-quantum molecular simulation demonstrate reduced COVID-19 mortality in 1.5M patient records
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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.
Article activity feed
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SciScore for 10.1101/2021.03.22.21254110: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Limitations: The N3C dataset did not track whether a patient was involved in a COVID-19 vaccination trial which, while unlikely, may skew results as vaccinated individuals are less likely to die from COVID-19. Our statistical …
SciScore for 10.1101/2021.03.22.21254110: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Limitations: The N3C dataset did not track whether a patient was involved in a COVID-19 vaccination trial which, while unlikely, may skew results as vaccinated individuals are less likely to die from COVID-19. Our statistical procedure’s uncertainty intervals did not take into account the selection procedure for the propensity model nor for the implicit multiple comparison post estimation. Given multiple treatments of interest, and varying sample sizes for each treatment, accounting for these factors is nontrivial and we are not aware of any currently available method to accurately account for them. This could potentially lead to optimistic uncertainty estimates potentially inflating the type I error. Finally, a patient’s diabetic disposition was solely based on clinical diagnosis and did not take HbA1c levels into consideration to compare between controlled versus uncontrolled diabetes. Future work: Depending on funding we may look at the effect of these compounds on hypertensive and hospitalized patient subsets in addition to in vitro and in vivo antiviral assays. The novel simulation platform and the methodology to assess clinical effects we used have implications much beyond SARS-CoV-2.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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