Open-source approaches for the repurposing of existing or failed candidate drugs: learning from and applying the lessons across diseases
Bibliographic record
Abstract
Repurposing has the objective of targeting existing drugs and failed, abandoned, or yet-to-be-pursued clinical candidates to new disease areas. The open-source model permits for the sharing of data, resources, compounds, clinical molecules, small libraries, and screening platforms to cost-effectively advance old drugs and/or candidates into clinical re-development. Clearly, at the core of drug-repurposing activities is collaboration, in many cases progressing beyond the open sharing of resources, technology, and intellectual property, to the sharing of facilities and joint program development to foster drug-repurposing human-capacity development. A variety of initiatives under way for drug repurposing, including those targeting rare and neglected diseases, are discussed in this review and provide insight into the stakeholders engaged in drug-repurposing discovery, the models of collaboration used, the intellectual property-management policies crafted, and human capacity developed. In the case of neglected tropical diseases, it is suggested that the development of human capital be a central aspect of drug-repurposing programs. Open-source models can support human-capital development through collaborative data generation, open compound access, open and collaborative screening, preclinical and possibly clinical studies. Given the urgency of drug development for neglected tropical diseases, the review suggests elements from current repurposing programs be extended to the neglected tropical diseases arena.
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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 | 0.024 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".