Identifying New Persistent and Bioaccumulative Organics Among Chemicals in Commerce II: Pharmaceuticals
Bibliographic record
Abstract
The goal of this study was to identify commercial pharmaceuticals that might be persistent and bioaccumulative (P&B) and that were not being considered in current wastewater and aquatic environmental measurement programs. We developed a database of 3193 pharmaceuticals from two U.S. Food and Drug Administration (FDA) databases and some lists of top ranked or selling drugs. Of the 3193 pharmaceuticals, 275 pharmaceuticals have been found in the environment and 399 pharmaceuticals were, based upon production volumes, designated as high production volume (HPV) pharmaceuticals. All pharmaceuticals that had reported chemical structures were evaluated for potential bioaccumulation (B) or persistence (P) using quantitative structure property relationships (QSPR) or scientific judgment. Of the 275 drugs detected in the environment, 92 were rated as potentially bioaccumulative, 121 were rated as potentially persistent, and 99 were HPV pharmaceuticals. After removing the 275 pharmaceuticals previously detected in the environment, 58 HPV compounds were identified that were both P&B and 48 were identified as P only. Of the non-HPV compounds, 364 pharmaceuticals were identified that were P&B. This study has yielded some interesting and probable P&B pharmaceuticals that should be considered for further study.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".