No Time to Waste: Real-World Repurposing of Generic Drugs as a Multifaceted Strategy Against COVID-19
Bibliographic record
Abstract
Real-world drug repurposing-the immediate "off-label" prescribing of drugs to address urgent clinical needs-is an indispensable strategy gaining rapid traction in the current COVID-19 crisis. Although off-label prescribing (ie, for a nonapproved indication) is legal in most countries, it tends to shift the burden of liability and cost to physicians and patients, respectively. Nevertheless, in urgent public health crises, it is often the only realistic source of a meaningful potential solution. To be considered for real-world repurposing, drug candidates should ideally have a track record of safety, affordability, and wide accessibility. Although thousands of such drugs are already available, the absence of a central repository of off-label uses presents a barrier to the immediate identification and selection of the safest, potentially useful interventions. Using the current COVID-19 pandemic as an example, we provide a glimpse at the extensive literature that supports the rationale behind six generic drugs, in four classes, all of which are affordable, supported by decades of safety data, and pleiotropically target the underlying pathophysiology that makes COVID-19 so dangerous. Having previously fast-tracked this paper to publication in summary form, we now expand on why cimetidine/famotidine (histamine type-2 receptor antagonists), dipyridamole (antiplatelet agent), fenofibrate/bezafibrate (cholesterol/triglyceride-lowering agents), and sildenafil (phosphodiesterase-5 inhibitor) are worth considering for patients with COVID-19 based on their antiviral, anti-inflammatory, renoprotective, cardioprotective, and anticoagulation properties. These examples also reveal the unlimited opportunity to future-proof public health by proactively mining, synthesizing, and cataloging the off-label treatment opportunities of thousands of safe, well-established, and affordable generic drugs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".