Surge of branded generics and antimicrobial resistance: analyzing the antibiotic market dynamics in Pakistan through the WHO essential medicines and AWaRe lens
Bibliographic record
Abstract
BACKGROUND: Access to safe and effective antibiotics is crucial in low- and middle-income countries (LMICs) coupled with reducing their overuse to reduce antimicrobial resistance (AMR). We sought to systematically analyze the extent of branded generic antibiotics in Pakistan, particularly Watch antibiotics, given concerns with AMR in Pakistan. RESEARCH DESIGN AND METHODS: Data on registered antibiotics was collected from the Drug Regulatory Authority of Pakistan (DRAP) and the Pharmaguides. Two hundred and fifty-seven antibiotics were analyzed using the AWaRe classification. RESULTS: Of these, 99 were registered in Pakistan including 91 single entities and 8 combinations, with 6,025 brands and 14,076 presentations. Distribution across AWaRe categories included Access - 37, Watch - 56, and Reserve - 6. Cephalosporins (2186 brands, 6447 presentations) and Quinolones (1333 brands, 2586 presentations) are the most prevalent, with ciprofloxacin (393 brands, 1158 presentations) leading in brand and presentation counts. Six antibiotics from the WHO Essential Medicines List lacked registered brands in Pakistan, while many available antibiotics were not included in the WHO framework. CONCLUSION: Extensive availability of branded generic antibiotics, particularly Watch antibiotics, in Pakistan poses a serious risk, exacerbated by the current misuse of antibiotics. Improving regulatory frameworks and strengthening stewardship are critical to reducing AMR in Pakistan along with addressing uncontrolled registration by DRAP.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".