Screening Large Population Health Databases for Potential Coronavirus Disease 2019 Therapeutics: A Pharmacopeia-Wide Association Study of Commonly Prescribed Medications
Bibliographic record
Abstract
Abstract Background For both the current and future pandemics, there is a need for high-throughput drug screening methods to identify existing drugs with potential preventive and/or therapeutic activity. Epidemiologic studies could complement laboratory-focused efforts to identify possible therapeutic agents. Methods We performed a pharmacopeia-wide association study (PWAS) to identify commonly prescribed medications and medication classes that are associated with the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in older individuals (≥65 years) in long-term care homes (LTCHs) and the community, between 15 January 2020 and 31 December 2020, across the province of Ontario, Canada. Results A total of 26 121 cases and 2 369 020 controls from LTCHs and the community were included in this analysis. Many of the drugs and drug classes evaluated did not yield significant associations with SARS-CoV-2 detection. However, some drugs and drug classes appeared to be significantly associated with reduced SARS-CoV-2 detection, including cardioprotective drug classes such as statins (weighted odds ratio [OR], 0.91; standard P < .01, adjusted P < .01) and β-blockers (weighted OR, 0.87; standard P < .01, adjusted P = .01), along with individual agents ranging from levetiracetam (weighted OR, 0.70; standard P < .01, adjusted P < .01) to fluoxetine (weighted OR, 0.86; standard P = .013, adjusted P = .198) to digoxin (weighted OR, 0.89; standard P < .01, adjusted P = .02). Conclusions Using this epidemiologic approach, which can be applied to current and future pandemics, we have identified a variety of target drugs and drug classes that could offer therapeutic benefit in coronavirus disease 2019 (COVID-19) and may warrant further validation. Some of these agents (eg, fluoxetine) have already been identified for their therapeutic potential.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".