Regulatory Strategies for Orphan drug Development in USA–Europe
Bibliographic record
Abstract
Objectives of the present work are as follows: • To study the current principles of rare diseases & orphan drugs. • To study the assessment, challenges and regulatory frame work of orphan drugs • To study the integrated approach for the development and approval of orphan drugs. • To carry out the study of globalization in orphan drug development strategies in US & EU markets. Methods: Internet using web page content: The literature was collected using numerous search engines e.g. Science Direct, Google Scholar and many more. Online books also served as a good source of information. Documents and information’s collected using numerous regulatory websites such as: a) USFDA: https://www.fda.gov b) EMA: https://www.ema.europa.eu/en c) CANADA: https://www.canada.ca/en/health-canada.html d) TGA: https://www.tga.gov.au/ e) INDIA: http://www.cdsco.com/ Results: US- FDA Approved Orphan Drug ex: Tafenoquine - Treatment of malaria - Krintafel is indicated for the radical cure (prevention of relapse) of Plasmodium vivax malaria. EU – EMA Approved Orphan Drug ex: Eculizumab, Soliris - Treatment myasthenia gravis. Conclusion: The orphan drug guidelines made via distinct countries have established as promoters in development of orphan drugs. The orphan drug regulation in the US and the EU has been a success in offering remedies to the patients with rare diseases.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".