International Regulatory Collaboration on the Analysis of Nitrosamines in Metformin-Containing Medicines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recalls of some batches of metformin have occurred due to the detection of N-nitrosodimethylamine (NDMA) in amounts above the acceptable intake (AI) of 96 ng per day. Prior to the recalls, an international regulatory laboratory network had been monitoring drugs for nitrosamine impurities with each laboratory independently developing and validating multiple analytical procedures to detect and measure nitrosamines in metformin drugs used in their jurisdictions. Here, we provide an overview of the analysis of metformin active pharmaceutical ingredients (APIs) and drug products with 1090 samples (875 finished dosage forms (FDFs) and 215 API samples) tested beginning in November of 2019 through July of 2020. Samples were obtained internationally by a variety of approaches, including purchased, received from firms via information requests or selected by regional regulatory authorities (either at wholesalers or during GMP inspections). Only one nitrosamine (NDMA) was detected and was only present in some batches of metformin products. For API samples, 213 out of 215 lots tested had no measurable level of NDMA. For FDF samples tested, the number of batches with NDMA above the AI amount for patient safety was 17.8% (156/875). Based on these data, although the presence of NDMA was of concern, 82.2% of the samples of metformin drug products tested met quality and safety standards for patients. Regulatory agencies continue to collaborate extensively and work with marketing authorization holders to understand root causes of nitrosamine formation and agree on corrective actions to mitigate the presence of NDMA in future metformin batches.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it