Understanding and creating value from open source drug discovery for neglected tropical diseases
Bibliographic record
Abstract
INTRODUCTION: The health commons (known as open source) refers to knowledge-based assets that are shared or owned in common by stakeholders found across the health value chain. The unique challenges presented to drug discovery and development including biological, chemical and economic are particularly salient for neglected tropical diseases (NTDs), warranting new networks and models of open collaboration between scientists from the public and private sectors. AREAS COVERED: The goal of this paper is to progress beyond the review of open source strategies NTDs to a discussion of an assessment of these strategies. The authors discuss the notion of evolving openness across the pharmaceutical value chain and discuss and develop a framework for assessing the value and outcomes of open source drug discovery for NTDs, based on the available literature. EXPERT OPINION: Collectively, open source programs for NTDs, beyond the actual deposits themselves, should foster linkages between experts in key disease arenas, encourage collaborations through such linkages and most urgently enable human capacity development. Quantifying their value in absolute terms is not an easy task since exchange markets do not always exist for deposited and shared assets. From the perspective of capacity development, the potential and benefits certainly exist for program development involving NTD initiatives, institutions and students in disease-endemic countries. Here, stakeholders such as public grant agencies and private sector sponsors can play a role in encouraging initiatives that link open and broad knowledge dissemination to the development of local human and technological capacity in NTD-endemic countries.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".