International Nonproprietary Names (INN) for novel vaccine substances: A matter of safety
Bibliographic record
Abstract
International Nonproprietary Names (INN) are assigned by the World Health Organization (WHO) to pharmaceutical substances to ensure global recognition by a unique name. INN facilitate safe prescribing through naming consistency, efficient communication and exchange of information, transnational access and pharmacovigilance of medicinal products. Traditional vaccines such as inactivated or live-attenuated vaccines have not been assigned INN and provision of a general name falls within the scope of the WHO Expert Committee on Biological Standardization (ECBS). However, novel vaccines that contain well-defined active ingredients such as nucleic acids or recombinant proteins fulfil the criteria to be assigned INN. In the current environment where multiple SARS-CoV-2 vaccines are being developed to combat the COVID-19 pandemic and with virus variants emerging, assigning INN to well-defined vaccine substances will strengthen pharmacovigilance and ultimately enhance the safety of vaccine recipients. This article examines the background to INN for vaccines and explains the applicability and value of assigning INN to novel well-defined vaccines.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".