Performance Characteristics of Mass Spectrometry-Based Analytical Procedures for Quantitation of Nitrosamines in Pharmaceuticals: Insights from an Inter-laboratory Study
Bibliographic record
Abstract
With the discovery of carcinogenic nitrosamine impurities in pharmaceuticals in 2018 and subsequent regulatory requirements for risk assessment for nitrosamine formation during pharmaceutical manufacturing processes, storage or from contaminated supply chains, effective testing of nitrosamines has become essential to ensure the quality of drug substances and products. Mass spectrometry has been widely applied to detect and quantify trace amounts of nitrosamines in pharmaceuticals. As part of an effort by regulatory authorities to assess the measurement variation in the determination of nitrosamines, an inter-laboratory study was performed by the laboratories from six regulatory agencies with each of the participants using their own analytical procedures to determine the amounts of nitrosamines in a set of identical samples. The results demonstrated that accurate and precise quantitation of trace level nitrosamines can be achieved across multiple analytical procedures and provided insight into the performance characteristics of mass spectrometry-based analytical procedures in terms of accuracy, repeatability and reproducibility.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".