Recommendations on the environmental risk assessment of pharmaceuticals: Effect characterization
Bibliographic record
Abstract
The effects testing of pharmaceuticals consists of a tiered investigation of ecotoxicological endpoints. However, effects testing has to be performed only when the predicted environmental concentrations (PECs) of pharmaceuticals are above certain action limits. To study the appropriateness of these action limits, a literature search was performed for pharmaceuticals with predicted no-effect concentrations (PNECs) close to or below the action limits. Some human pharmaceuticals showed effects at concentrations ≤100 ng/L, mostly in nonstandard fish or invertebrate tests. In addition, antibiotics and parasiticides sometimes had effects at concentrations <10 mg/kg soil. To help in identifying pharmaceuticals that should undergo effects testing although their PECs are below the action limits, "however clauses" are postulated for pharmaceuticals that are potentially persistent, bioaccumulative, carcinogenic, mutagenic, or reproductively toxic. Effects testing should also be performed for pharmaceuticals that 1) affect target structures that are conserved across species, 2) have a high potency or a small therapeutic margin, 3) are from a new therapeutic class, and 4) are structurally similar to compounds with known effects. Furthermore, suggestions for improving the effects testing of pharmaceuticals are made. These include inter alia chronic effects testing as a general approach, the use of invertebrate tests including sexual reproduction, the application of endpoints reflecting the mode of action of the drug or known side effects, and the simulation of more realistic exposure conditions in terrestrial laboratory tests.
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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 | 0.032 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.009 | 0.003 |
| Research integrity | 0.013 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.013 |
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".