Establishing healthy pharmaceutical regulations on statutory exclusivity: Lessons from the experience in the European Union, Canada, South Korea, Australia, and the United States
Bibliographic record
Abstract
Abstracts Objectives Recent international trade agreements require member countries a prolonged statutory exclusivity for biologics, and domestic legislation guarantees various forms of exclusivity for specific drugs, indications, or studies. This study notes prolonged exclusivity provisions for biologics in the United States and international trade agreements. We aim to review various exclusivity systems, including chemical entities, in selected high-income countries and to suggest implications for establishing the system specifically relevant for biologics in low- and middle-income countries. Methods We conducted a review of a comprehensive range of literature to develop the framework. Then, a comparative legal analysis was conducted to analyze the deviations among the systems in the European Union, Canada, South Korea, Australia, and the United States. Results There is constructive ambiguity in international trade agreements, specifically for provisions regarding biologics. Furthermore, the selected countries operate different statutory exclusivity systems in terms of eligibility for statutory exclusivity, specific measures for exclusivity, and other elements of exclusivity. In addition, market exclusivity, which is distinguished from data exclusivity, is not available in Korea and Australia. There are also various forms of statutory exclusivity for specific drugs, indications, or studies requested by the marketing authority. Conclusions Given constructive ambiguities in international agreements and variations in the manner of implementations of the systems in selected countries, statutory exclusivity for biologics could be established with cautions to mediate the harms. In this study, we suggest several solutions and alternatives for low- and middle-income countries.
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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 | 0.090 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.013 | 0.026 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".