The Potential Role of Ecotoxicological Data in National Essential Medicine Lists: A Cross-Sectional Analysis
Bibliographic record
Abstract
BACKGROUND: Medicines affect the environment throughout their lifecycle, from production and distribution to use and disposal. They contribute to the pollution of air, water, and soil, impacting ecosystems and human health. Recognizing these risks, regulatory bodies and organizations have highlighted pharmaceutical pollution as a global concern, emphasizing the need for environmental risk assessments and sustainable practices. METHODS: This study reviewed the essential medicines lists (EMLs) from 158 countries and examined the available ecotoxicological data. Medicines with high bioaccumulation, persistence, and toxicity were identified and cross-referenced with their inclusion in EMLs. Additionally, we analyzed the presence of alternative medicines with similar therapeutic effects but potentially lower environmental risks. RESULTS: Five medicines-ciprofloxacin, ethinylestradiol, levonorgestrel, ibuprofen, and sertraline-were selected as illustrative examples due to their high environmental persistence and toxicity. All were listed in the 2023 WHO model list, with ciprofloxacin appearing in 94.3% of national EMLs. CONCLUSIONS: This study underscores the limited availability of ecotoxicological data, which hinders environmental risk assessment for medicines. EMLs could serve as a tool to enhance the awareness and data mobilization of pharmaceutical pollution. Incorporating environmental criteria into EMLs could support more sustainable medicine selection and regulatory practices.
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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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".