NAVIGATING NITROSAMINES: ORIGIN, DETECTING, ANALYSING AND REGULATING IMPURITIES IN PHARMACEUTICALS
Bibliographic record
Abstract
N-nitrosamines are carcinogenic impurities mostly found in groundwater, treated water, foods, beverages, and consumer products like processed meats, alcoholic beverages, cosmetics, and cigarette smoke. The recent discovery of N-nitrosamines in pharmaceutical products and subsequent recalls pose a significant health risk to patients. Nitrosamine impurities in drug products have appeared as a critical concern in pharmaceuticals prompting extensive scrutiny from regulatory agencies and stakeholders. To avoid carcinogenic and mutagenic effects in patients relying on these medications, authorities have established specific guidelines in risk assessment scenarios and proposed control acceptable limits for nitrosamine impurities in pharmaceuticals. This review provides an information on historical background of Nitrosamine impurities; its carcinogenic effect; the sources and formation of impurities; associated risks of nitrosamines in drug formulations; different analytical techniques for nitrosamine detection. It also gives an understanding of the general Quality Risk management (QRM) process, techniques for measuring nitrosamine impurities with control strategies as directed by the regulatory authorities and how to avoid them in pharmaceutical drug products. A brief review on recalls of drug classes including angiotensin II receptor antagonists, histamine-2 receptor antagonists, antimicrobial agents, and antidiabetic drugs by regulatory bodies due to its potential harm produced by nitrosamine have been discussed. Moreover, the regulatory landscape governing nitrosamine impurities are explored, encompassing recent guidelines from major regulatory bodies such as the United States Food and Drug Administration (USFDA), European Medicines Agency (EMA) and Health Canada (HC) in controlling/eliminating the nitrosamine impurities in pharmaceuticals.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".