Overcoming Market Failures in Pandemic Drug Discovery Through Open Science: A Canadian Solution
Bibliographic record
Abstract
Among the lessons learned from the COVID-19 pandemic is the need to develop antiviral drugs poised to treat the next pandemic. Unfortunately, traditional drug development economic models, centered principally on patents, are ineffective to induce private sector investment due to unpredictable timing and cause of the next pandemic. As a result, illustrated by the COVID-19 pandemic, it is the public and philanthropic sectors sectors that overwhelmingly fund the development of innovative vaccines and therapies. To meet the need for proactive antiviral medicines in advance of the next pandemic, new models of drug development are needed. Open science partnerships (OSPs) show promise in this regard. Rather than rely principally on patents and private investment, OSPs combine a variety of academic, philanthropic, governmental, and private sector incentives to share knowledge and develop and test antiviral drugs. Private sector investments are, within an OSP, not only leveraged against investments by other actors, but predicated on gaining regulatory data exclusivity, a known and secure form of commercial advantage. Building on domestic expertise in OSPs, Canadian leaders created the Viral Interruption Medicines Initiative, a not-for-profit OSP, to develop pandemic ready-antivirals and address other areas of market failure.
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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 | 0.021 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".