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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".