Delivering Benefits at Speed Through Real-World Repurposing of Off-Patent Drugs: The COVID-19 Pandemic as a Case in Point
Bibliographic record
Abstract
Real-world drug repurposing-the immediate "off-label" prescribing of drugs to address urgent clinical needs-is a widely overlooked opportunity. Off-label prescribing (ie, for a nonapproved indication) is legal in most countries and tends to shift the burden of liability and cost to physicians and patients, respectively. Nevertheless, health crises may mean that real-world repurposing is the only realistic source for solutions. Optimal real-world repurposing requires a track record of safety, affordability, and access for drug candidates. Although thousands of such drugs are already available, there is no central repository of off-label uses to facilitate immediate identification and selection of potentially useful interventions during public health crises. Using the current coronavirus disease (COVID-19) pandemic as an example, we provide a glimpse of 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 targeted toward the underlying pathophysiology that makes COVID-19 so deadly. This paper briefly summarizes why cimetidine or famotidine, dipyridamole, fenofibrate or bezafibrate, and sildenafil citrate are worth considering for patients with COVID-19. Clinical trials to assess efficacy are already underway for famotidine, dipyridamole, and sildenafil, and further trials of all these agents will be important in due course. These examples also reveal the unlimited opportunity to future-proof our health care systems by proactively mining, synthesizing, cataloging, and evaluating 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 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 | 0.025 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.017 | 0.024 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".