Pilot Study of Essential Drug Quality in Two Major Cities in India
Bibliographic record
Abstract
BACKGROUND: India is an increasingly influential player in the global pharmaceutical market. Key parts of the drug regulatory system are controlled by the states, each of which applies its own standards for enforcement, not always consistent with others. A pilot study was conducted in two major cities in India, Delhi and Chennai, to explore the question/hypothesis/extent of substandard and counterfeit drugs available in the market and to discuss how the Indian state and federal governments could improve drug regulation and more importantly regulatory enforcement to combat these drugs. METHODOLOGY/PRINCIPAL FINDINGS: Random samples of antimalarial, antibiotic, and antimycobacterial drugs were collected from pharmacies in urban and peri-urban areas of Delhi and Chennai, India. Semi-quantitative thin-layer chromatography and disintegration testing were used to measure the concentration of active ingredients against internationally acceptable standards. 12% of all samples tested from Delhi failed either one or both tests, and were substandard. 5% of all samples tested from Chennai failed either one or both tests, and were substandard. Spatial heterogeneity between pharmacies was observed, with some having more or less substandard drugs (30% and 0% respectively), as was product heterogeneity, with some drugs being more or less frequently substandard (12% and 7% respectively). CONCLUSIONS/SIGNIFICANCE: In a study using basic field-deployable techniques of lesser sensitivity rather than the most advanced laboratory-based techniques, the prevalence of substandard drugs in Delhi and Chennai is confirmed to be roughly in accordance with the Indian government's current estimates. However, important spatial and product heterogeneity exists, which suggests that India's substandard drug problem is not ubiquitous, but driven by a subset of manufacturers and pharmacies which thrive in an inadequately regulated environment. It is likely that the drug regulatory system in India needs to be improved for domestic consumption, and because India is an increasingly important exporter of drugs for both developed and developing countries. Some poor countries with high burdens of disease have weak drug regulatory systems and import many HIV/AIDS, tuberculosis and malaria drugs from India.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".