Environmental risk assessment of pharmaceutical drug substances—conceptual considerations
Bibliographic record
Abstract
Drugs, i.e. active ingredients of human medicinal products, may be introduced into the environment after use in patients by sewage effluent pathways and consequently are detected at low concentrations in sewage effluents and in surface waters. Legal requirements in a number of geographical regions (Europe, US, and intended in Canada) demand environmental risk assessments (ERA) for new drug substances. Existing regulatory concepts of ERA are based initially on a set of short-term ecotoxicological studies in three to four different species, environmental behavior and the application of assessment factors to correct for the ERA inherent uncertainty. Based on theoretical considerations and the experience with a very limited, but well investigated, number of examples while considering that drugs are highly biologically active compounds, the appropriateness of this risk assessment procedure for all drug substances might be questioned. Indeed, e.g. long-term effects may occur at much lower concentrations and follow different toxicodynamic mechanism than extrapolated from short-term studies., In such cases, the application of assessment factors for deriving chronic no-observed effect concentration (NOECs) appears to be problematic. Although long-term tests with a variety of organisms would provide a complete database for the evaluation of the environmental risks, this is unachievable for all drugs due to time, money and animal welfare constraints. In order to avoid unnecessary testing, a concept is presented, which makes use of pharmacological and toxicological, as well
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".