Evidence map›Paper›PMID 38752675›Full record

ArticleIntegrated environmental assessment and management2024

An automated computational data pipeline to rapidly acquire, score, and rank toxicological data for ecological hazard assessment.

Christopher M Schaupp, Gregory Byrne, Manli Chan, Derik E Haggard, Monique Hazemi, Mark D Jankowski, Carlie A LaLone, Andrea LaTier, Kali Z Mattingly, Jennifer H Olker and 3 more

Abstract read
In one paragraph

Article in Integrated environmental assessment and management, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Christopher M SchauppOak Ridge Institute for Science and Education, Oak Ridge, Tennessee, USA.ORCID http://orcid.org/0000-0002-7792-3861
Gregory ByrneGeneral Dynamics Information Technology Inc., Supporting USEPA IV (MAINES) Contract, Research Triangle Park, North Carolina, USA.
Manli ChanUSEPA, Center for Computational Toxicology and Exposure, Scientific Computing and Data Curation Division, Data Management and Mining Branch, Research Triangle Park, North Carolina, USA.
Derik E HaggardUSEPA, Center for Computational Toxicology and Exposure, Scientific Computing and Data Curation Division, Data Management and Mining Branch, Research Triangle Park, North Carolina, USA.
Monique HazemiOak Ridge Institute for Science and Education, Oak Ridge, Tennessee, USA.ORCID http://orcid.org/0000-0003-3544-0180
Mark D JankowskiUSEPA, Region 10 Office, Laboratory Services & Applied Science Division, Risk Evaluation Branch, Seattle, Washington, USA.
Carlie A LaLoneUS Environmental Protection Agency, Great Lakes Toxicology and Ecology Division, Center for Computational Toxicology and Exposure, Duluth, Minnesota, USA.
Andrea LaTierUSEPA, Region 10 Office, Laboratory Services & Applied Science Division, Risk Evaluation Branch, Seattle, Washington, USA.
Kali Z MattinglySpec-Pro Professional Services, Duluth, Minnesota, USA.
Jennifer H OlkerUS Environmental Protection Agency, Great Lakes Toxicology and Ecology Division, Center for Computational Toxicology and Exposure, Duluth, Minnesota, USA.ORCID http://orcid.org/0000-0002-7877-4773
James RennerUSEPA, Scientific Computing and Data Curation Division, Center for Computational Toxicology and Exposure, Data Management and Mining Branch, Duluth, Minnesota, USA.
Bhaskar SharmaUSEPA, Center for Computational Toxicology and Exposure, Scientific Computing and Data Curation Division, Data Management and Mining Branch, Research Triangle Park, North Carolina, USA.
Daniel L VilleneuveUS Environmental Protection Agency, Great Lakes Toxicology and Ecology Division, Center for Computational Toxicology and Exposure, Duluth, Minnesota, USA.

Funding

Intramural EPA EPA999999USEPA Office of Research and Development, Chemical Safety for Sustainability National Research Program
6 · The paper itself

Abstract

Biological Evaluations support Endangered Species Act (ESA) consultation with the US Fish and Wildlife Service and National Marine Fisheries Service by federal action agencies, such as the USEPA, regarding impacts of federal activities on threatened or endangered species. However, they are often time-consuming and challenging to conduct. The identification of pollutant benchmarks or guidance to protect taxa for states and tribes when USEPA has not yet developed criteria recommendations is also of importance to ensure a streamlined approach to Clean Water Act program implementation. Due to substantial workloads, tight regulatory timelines, and the often-protracted length of ESA consultations, there is a need to streamline the development of biological evaluation toxicity assessments for determining the impact of chemical pollutants on ESA-listed species. Moreover, there is limited availability of species-specific toxicity data for many contaminants, further complicating the consultation process. New approach methodologies are being increasingly used in toxicology and chemical safety assessment to rapidly and cost-effectively provide data that can fill gaps in hazard and/or exposure characterization. Here, we present the development of an automated computational pipeline-RASRTox (Rapidly Acquire, Score, and Rank Toxicological data)-to rapidly extract and categorize ecological toxicity benchmark values from curated data sources (ECOTOX, ToxCast) and well-established quantitative structure-activity relationships (TEST, ECOSAR). As a proof of concept, points-of-departure (PODs) generated in RASRTox for 13 chemicals were compared against benchmark values derived using traditional methods-toxicity reference values (TRVs) and water quality criteria (WQC). The RASRTox PODs were generally within an order of magnitude of corresponding TRVs, though less concordant compared with WQC. The greatest utility of RASRTox, however, lies in its ability to quickly and systematically identify critical studies that may serve as a basis for screening value derivation by toxicologists as part of an ecological hazard assessment. As such, the strategy described in this case study can potentially be adapted for other risk assessment contexts and stakeholder needs. Integr Environ Assess Manag 2024;20:2203-2217. © 2024 Society of Environmental Toxicology & Chemistry (SETAC). This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Indexed as

Environmental MonitoringAnimalsEcotoxicologyEndangered SpeciesQuantitative Structure-Activity RelationshipRisk AssessmentUnited StatesWater Pollutants, ChemicalWater Pollutants, ChemicalComputational toxicologyEcotoxicologyNew approach methodologiesRisk assessment

Identifiers

PMID38752675
PMCPMC13502720

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.