Pharmaceuticals and Personal Care Products in the Environment: Regulatory Drivers and Research Needs
Bibliographic record
Abstract
Abstract The potential effect of human and veterinary medicines and other personal care products on the environment has become an important topic over the past few years. Whilst an assessment of the potential environmental risks posed by new and existing pharmaceuticals has been required in the United States (U.S.) for a number of decades, in the European Union (EU) and Canada assessments have only been required in the last 5–10 years. In the U.S., guidance has been available since the early 1980s on the assessment of veterinary medicines, whereas only recently has detailed guidance become available on how to perform the risk assessment in other areas. For example, in Canada, new pharmaceuticals (and other substances including novel foods, food additives, human biologics and genetic therapies, medical devices, natural health products, veterinary drugs, cosmetics) have been required to be notified for an environmental assessment under the Canadian Environmental Protection Act (CEPA 1999) since 2001. The European Medicines Evaluation Authority (EMEA) has published guidelines for assessment of veterinary medicines in use in Europe. For veterinary medicines attempts are currently being made by the Veterinary International Co‐operation on Harmonisation (VICH) to harmonise these approaches. Generally, the current assessment approaches are tiered and initially involve a comparison of environmental concentrations with set trigger values. If the trigger values are exceeded then a formal assessment has to be performed requiring data on environmental fate and ecotoxicity. Concerns have been raised over the current approaches used in each of the assessment processes and there are a number of areas that warrant further research. This paper will provide an overview of the regulatory assessment of pharmaceuticals and personal care products (PPCPs) and will expand on many of the topics for research in the future, and the role that QSAR scientists can play in this research will be highlighted.
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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 | 0.018 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".