Environmental Risks of Pharmaceutical Mixtures in Aquatic Ecosystems: Reflections on a Decade of Research
Bibliographic record
Abstract
Pharmaceuticals and personal care products (PPCPs) occur as variable mixtures in surface waters receiving discharges of human and animal wastes. A key question identified a decade ago is how to assess the effects of long-term exposures of these PPCP mixtures on nontarget organisms. We review the recent progress made on assessing the aquatic ecotoxicity of PPCP mixtures-with a focus on active pharmaceutical ingredients-and the challenges and research needs that remain. New knowledge has arisen from the use of whole-mixture testing combined with component-based approaches, and these studies show that mixtures often result in responses that meet the concentration addition model. However, such studies have mainly been done on individual species over shorter time periods, and longer-term, multispecies assessments remain limited. The recent use of targeted and nontargeted gene analyses has improved our understanding of the diverse pathways that are impacted, and there are promising new "read-across" methods that use mammalian data to predict toxicity in wildlife. Risk assessments remain challenging given the paucity of ecotoxicological and exposure data on PPCP mixtures. As such, the assessment of PPCP mixtures in aquatic environments should remain a priority given the potential for additive-as well as nontarget-effects in nontarget organisms. In addition, we need to improve our understanding of which species, life stages, and relevant endpoints are most sensitive to which types of PPCP mixtures and to expand our knowledge of environmental PPCP levels in regions of the globe that have been poorly studied to date. We recommend an increased use of new approach methodologies, in particular "omics," to advance our understanding of the molecular mechanics of mixture effects. Finally, we call for systematic research on the role of PPCP mixtures in the development of antimicrobial resistance. Environ Toxicol Chem 2024;43:549-558. © 2023 SETAC.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".