Bibliographic record
Abstract
Purpose Antimicrobial resistance is a public health threat even in countries exercising aggressive antimicrobial stewardship. A market failure is also causing lackluster innovation in antimicrobial medicines development. At the heart of the issue are antimicrobial stewardship guidelines that, rightfully, reserve innovative antimicrobials for emergency situations that arise due to multidrug-resistant organisms. This suppresses revenues and research and development (R&D) investment incentives of manufacturers. The public policy makers and researchers have taken aim at the problem. The researchers have published strategies to encourage the production of innovative antimicrobials, while policy makers have taken legislative steps to address the issue. Most notably, the USA enacted the Generating Antibiotic Incentives Now (GAIN) act in 2012 and the EU created a commission to formally study possible policy solutions. The paper aims to discuss these issues. Design/methodology/approach In this paper, the authors describe incentives that drive pharmaceutical R&D and review the impact of a number of R&D stimulus policies in other pharmaceutical markets. The authors also discuss which policy levers are useful to boost R&D of new antimicrobials. Findings The authors find that a policy focused on extending intellectual property rights, as implemented in the GAIN act, are unlikely to be impactful. Instead, the authors see a need for the revision of the procurement policy to move away from paying per prescription and toward licenses and advanced market commitment models. Further, the authors note that the importance of steadfast public investment in basic biomedical research as it has been repeatedly shown to boost innovation. Originality/value The authors hope that the work can support the refinement of the GAIN act and the EU efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.015 | 0.010 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".