Environmental Assessments to Evaluate Pharmaceutical Risks to Ecologic Systems
Bibliographic record
Abstract
Studies have found pharmaceutical products in surface and ground water resources, causing concern over the ecological impacts of these compounds in the environment. In response, regulatory agencies (FDA, EMA, and Health Canada) require an assessment of the potential environmental impact of pharmaceuticals as a component of the registration process for new drug applications. These assessments range from a simple analysis demonstrating that the compound will not pose a threat to the environment to very complex assessments requiring numerous studies. In some cases, the applicant may request a categorical exclusion from environmental testing based upon a low predicted concentration at the point of entry into the aquatic environment or due to intrinsic properties of the compound (e.g., biologics are generally excluded because they are expected to degrade rapidly before or after excretion from the treated patient population). However, other compounds, such as those with estrogenic, androgenic, and thyroid activity are coming under increasing scrutiny with more complex studies being required. This presentation will provide an overview of environmental assessment requirements for pharmaceuticals. A case study will also be presented.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".