Toward Point-of-Care Drug Quality Assurance in Developing Countries: Comparison of Liquid Chromatography and Infrared Spectroscopy Quantitation of a Small-Scale Random Sample of Amoxicillin
Bibliographic record
Abstract
Substandard antibiotics are thought to be a major threat to public health in developing countries and a cause of antimicrobial resistance. However, assessing quality outside of a laboratory setting, using simple equipment, is challenging. The aim of this study was to validate the use of a portable Fourier transform infrared (FT-IR) spectrometer for the identification of substandard antibiotics. Results are presented for amoxicillin packages from Haiti, Ghana, Sierra Leone, Democratic Republic of Congo, India, Papua New Guinea, and Ethiopia collected over the course of 6 months in 2017, including two field trips with the FT-IR to Ghana and Sierra Leone. Canadian samples were used as a control. Regarding drug quality, of 290 individual capsules of amoxicillin analyzed, 13 were found to be substandard with total active pharmaceutical ingredients (API) lying outside the acceptable range of 90-110%. Of these 13, four were below 80% API. The FT-IR reliably identified these outliers and was found to yield results in good agreement with the established pharmacopeia liquid chromatography protocol. We conclude that the portable FT-IR may be suitable to intercept substandard antibiotics in developing countries where more sophisticated techniques are not readily available.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".