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Record W1787810201 · doi:10.1002/ieam.1637

Selective serotonin reuptake inhibitors and β-blocker transformation products may not pose a significant risk of toxicity to aquatic organisms in wastewater effluent-dominated receiving waters

2015· article· en· W1787810201 on OpenAlexafffund
Alistair K. Brown, Jonathan K. Challis, Charles S. Wong, Mark L. Hanson

Bibliographic record

VenueIntegrated Environmental Assessment and Management · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsUniversity of WinnipegUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaU.S. Environmental Protection Agency
KeywordsWastewaterEnvironmental chemistryEnvironmental scienceSewage treatmentCitalopramAtenololEffluentSertralineFluvoxamineFluoxetinePharmacologyChemistryBiologyEnvironmental engineeringAntidepressant

Abstract

fetched live from OpenAlex

Abstract A probabilistic ecological risk assessment was conducted for the transformation products (TPs) of 3 β-blockers (atenolol, metoprolol, and propranolol) and 5 selective serotonin reuptake inhibitors (SSRIs; citalopram, fluoxetine, fluvoxamine, paroxetine, and sertraline) to assess potential threats to aquatic organisms in effluent-dominated surface waters. To this end, the pharmacokinetic literature, the University of Minnesota's Biocatalysis/Biodegradation Database Pathway Prediction System aerobic microbial degradation software, and photolysis literature pertaining to β-blockers and SSRIs were used to determine their most likely TPs formed via human metabolism, aerobic biodegradation, and photolysis, respectively. Monitoring data from North American and European surface waters receiving human wastewater inputs were the basis of the exposure characterizations of the parent compounds and the TPs, where available. In most cases, where monitoring data for TPs did not exist, we assumed a conservative 1:1 parent-to-TP production ratio (i.e., 100% of parent converted). The US Environmental Protection Agency (USEPA)'s EPISuite and ECOSAR v1.11 software were used to estimate acute and chronic toxicities to aquatic organisms. Hazard quotients, which were calculated using the 95th percentile of the exposure distributions, ranged from 10−11 to 10−3 (i.e., all significantly less than 1). Based on these results, the TPs of interest would be expected to pose little to no environmental risk in surface waters receiving wastewater inputs. Overall, we recommend developing analytical methods that can isolate and quantify human metabolites and TPs at environmentally relevant concentrations to confirm these predictions. Further, we recommend identifying the major species of TPs from classes of pharmaceuticals that could elicit toxic effects via specific modes of action (e.g., norfluoxetine via the serotonin 5-hydroxytryptamine [5-HT]1A receptors) and conducting aquatic toxicity tests to confirm these findings. To our knowledge, this is the first quantitative probabilistic ecotoxicological assessment of all of the predicted and probable TPs of these pharmaceuticals, and our approach provides a framework for future such studies with other compound classes as data become available. Integr Environ Assess Manag 2015;X:000–000. © 2015 SETAC Key Points Assessment incorporates novel human metabolites, photolysis, and biodegradation transformation products of beta-blockers and selective serotonin re-uptake inhibitors. Worst-case exposure scenarios suggest no acute or chronic risk posed by these pharmaceutical transformation products to fish, invertebrates and algae. This study illustrates the ability to perform a robust first-tier probabilistic aquatic risk assessment of pharmaceutical transformation products in the absence of actual exposure and effects data. It is recommended that analytical procedures be developed to screen surface water for exposure and support the generation of baseline toxicity data.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.256
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2015
Admission routes2
Has abstractyes

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