Microbiological contamination in counterfeit and unapproved drugs
Bibliographic record
Abstract
BACKGROUND: Counterfeit and unapproved medicines are inherently dangerous and can cause patient injury due to ineffectiveness, chemical or biological contamination, or wrong dosage. Growth of the counterfeit medical market in developed countries is mainly attributable to life-style drugs, which are used in the treatment of non-life-threatening and non-painful conditions, such as slimming pills, cosmetic-related pharmaceuticals, and drugs for sexual enhancement. One of the main tasks of health authorities is to identify the exact active pharmaceutical ingredients (APIs) in confiscated drugs, because wrong API compounds, wrong concentrations, and/or the presence of chemical contaminants are the main risks associated with counterfeit medicines. Serious danger may also arise from microbiological contamination. We therefore performed a market surveillance study focused on the microbial burden in counterfeit and unapproved medicines. METHODS: Counterfeit and unapproved medicines confiscated in Canada and Austria and controls from the legal market were examined for microbial contaminations according to the US and European pharmacopoeia guidelines. The microbiological load of illegal and legitimate samples was statistically compared with the Wilcoxon rank-sum test. RESULTS: Microbial cultivable contaminations in counterfeit and unapproved phosphodiesterase type 5 inhibitors were significantly higher than in products from the legal medicines market (p < 0.0001). Contamination levels exceeding the USP and EP limits were seen in 23% of the tested illegal samples in Canada. Additionally, microbiological contaminations above the pharmacopoeial limits were detected in an anabolic steroid and an herbal medicinal product in Austria (6% of illegal products tested). CONCLUSIONS: Our results show that counterfeit and unapproved pharmaceuticals are not manufactured under the same hygienic conditions as legitimate products. The microbiological contamination of illegal medicinal products often exceeds USP and EP limits, representing a potential threat to consumer health.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".