Literature DB >> 33426407

EPA's DSSTox database: History of development of a curated chemistry resource supporting computational toxicology research.

Christopher M Grulke1, Antony J Williams1, Inthirany Thillanadarajah2, Ann M Richard1.   

Abstract

The US Environmental Protection Agency's (EPA) Distributed Structure-Searchable Toxicity (DSSTox) database, launched publicly in 2004, currently exceeds 875 K substances spanning hundreds of lists of interest to EPA and environmental researchers. From its inception, DSSTox has focused curation efforts on resolving chemical identifier errors and conflicts in the public domain towards the goal of assigning accurate chemical structures to data and lists of importance to the environmental research and regulatory community. Accurate structure-data associations, in turn, are necessary inputs to structure-based predictive models supporting hazard and risk assessments. In 2014, the legacy, manually curated DSSTox_V1 content was migrated to a MySQL data model, with modern cheminformatics tools supporting both manual and automated curation processes to increase efficiencies. This was followed by sequential auto-loads of filtered portions of three public datasets: EPA's Substance Registry Services (SRS), the National Library of Medicine's ChemID, and PubChem. This process was constrained by a key requirement of uniquely mapped identifiers (i.e., CAS RN, name and structure) for each substance, rejecting content where any two identifiers were conflicted either within or across datasets. This rejected content highlighted the degree of conflicting, inaccurate substance-structure ID mappings in the public domain, ranging from 12% (within EPA SRS) to 49% (across ChemID and PubChem). Substances successfully added to DSSTox from each auto-load were assigned to one of five qc_levels, conveying curator confidence in each dataset. This process enabled a significant expansion of DSSTox content to provide better coverage of the chemical landscape of interest to environmental scientists, while retaining focus on the accuracy of substance-structure-data associations. Currently, DSSTox serves as the core foundation of EPA's CompTox Chemicals Dashboard [https://comptox.epa.gov/dashboard], which provides public access to DSSTox content in support of a broad range of modeling and research activities within EPA and, increasingly, across the field of computational toxicology.

Entities:  

Keywords:  Chemistry database; Computational toxicology; DSSTox; Data quality; Environmental science; QSAR; Structure curation

Year:  2019        PMID: 33426407      PMCID: PMC7787967          DOI: 10.1016/j.comtox.2019.100096

Source DB:  PubMed          Journal:  Comput Toxicol        ISSN: 2468-1113


  22 in total

1.  The Tox21 10K Compound Library: Collaborative Chemistry Advancing Toxicology.

Authors:  Ann M Richard; Ruili Huang; Suramya Waidyanatha; Paul Shinn; Bradley J Collins; Inthirany Thillainadarajah; Christopher M Grulke; Antony J Williams; Ryan R Lougee; Richard S Judson; Keith A Houck; Mahmoud Shobair; Chihae Yang; James F Rathman; Adam Yasgar; Suzanne C Fitzpatrick; Anton Simeonov; Russell S Thomas; Kevin M Crofton; Richard S Paules; John R Bucher; Christopher P Austin; Robert J Kavlock; Raymond R Tice
Journal:  Chem Res Toxicol       Date:  2020-11-03       Impact factor: 3.739

2.  CATMoS: Collaborative Acute Toxicity Modeling Suite.

Authors:  Kamel Mansouri; Agnes L Karmaus; Jeremy Fitzpatrick; Grace Patlewicz; Prachi Pradeep; Domenico Alberga; Nathalie Alepee; Timothy E H Allen; Dave Allen; Vinicius M Alves; Carolina H Andrade; Tyler R Auernhammer; Davide Ballabio; Shannon Bell; Emilio Benfenati; Sudin Bhattacharya; Joyce V Bastos; Stephen Boyd; J B Brown; Stephen J Capuzzi; Yaroslav Chushak; Heather Ciallella; Alex M Clark; Viviana Consonni; Pankaj R Daga; Sean Ekins; Sherif Farag; Maxim Fedorov; Denis Fourches; Domenico Gadaleta; Feng Gao; Jeffery M Gearhart; Garett Goh; Jonathan M Goodman; Francesca Grisoni; Christopher M Grulke; Thomas Hartung; Matthew Hirn; Pavel Karpov; Alexandru Korotcov; Giovanna J Lavado; Michael Lawless; Xinhao Li; Thomas Luechtefeld; Filippo Lunghini; Giuseppe F Mangiatordi; Gilles Marcou; Dan Marsh; Todd Martin; Andrea Mauri; Eugene N Muratov; Glenn J Myatt; Dac-Trung Nguyen; Orazio Nicolotti; Reine Note; Paritosh Pande; Amanda K Parks; Tyler Peryea; Ahsan H Polash; Robert Rallo; Alessandra Roncaglioni; Craig Rowlands; Patricia Ruiz; Daniel P Russo; Ahmed Sayed; Risa Sayre; Timothy Sheils; Charles Siegel; Arthur C Silva; Anton Simeonov; Sergey Sosnin; Noel Southall; Judy Strickland; Yun Tang; Brian Teppen; Igor V Tetko; Dennis Thomas; Valery Tkachenko; Roberto Todeschini; Cosimo Toma; Ignacio Tripodi; Daniela Trisciuzzi; Alexander Tropsha; Alexandre Varnek; Kristijan Vukovic; Zhongyu Wang; Liguo Wang; Katrina M Waters; Andrew J Wedlake; Sanjeeva J Wijeyesakere; Dan Wilson; Zijun Xiao; Hongbin Yang; Gergely Zahoranszky-Kohalmi; Alexey V Zakharov; Fagen F Zhang; Zhen Zhang; Tongan Zhao; Hao Zhu; Kimberley M Zorn; Warren Casey; Nicole C Kleinstreuer
Journal:  Environ Health Perspect       Date:  2021-04-30       Impact factor: 9.031

3.  Integrating publicly available information to screen potential candidates for chemical prioritization under the Toxic Substances Control Act: A proof of concept case study using genotoxicity and carcinogenicity.

Authors:  Grace Patlewicz; Jeffry L Dean; Catherine F Gibbons; Richard S Judson; Nagalakshmi Keshava; Leora Vegosen; Todd M Martin; Prachi Pradeep; Anita Simha; Sarah H Warren; Maureen R Gwinn; David M DeMarini
Journal:  Comput Toxicol       Date:  2021-11-01

4.  Comparing the performance and coverage of selected in silico (liver) metabolism tools relative to reported studies in the literature to inform analogue selection in read-across: A case study.

Authors:  Matthew Boyce; Brian Meyer; Chris Grulke; Lucina Lizarraga; Grace Patlewicz
Journal:  Comput Toxicol       Date:  2022-02-01

5.  Evaluation of Existing QSAR Models and Structural Alerts and Development of New Ensemble Models for Genotoxicity Using a Newly Compiled Experimental Dataset.

Authors:  Prachi Pradeep; Richard Judson; David M DeMarini; Nagalakshmi Keshava; Todd M Martin; Jeffry Dean; Catherine F Gibbons; Anita Simha; Sarah H Warren; Maureen R Gwinn; Grace Patlewicz
Journal:  Comput Toxicol       Date:  2021-05-01

6.  Integrative exposomic, transcriptomic, epigenomic analyses of human placental samples links understudied chemicals to preeclampsia.

Authors:  Alex Chao; Jarod Grossman; Celeste Carberry; Yunjia Lai; Antony J Williams; Jeffrey M Minucci; S Thomas Purucker; John Szilagyi; Kun Lu; Kim Boggess; Rebecca C Fry; Jon R Sobus; Julia E Rager
Journal:  Environ Int       Date:  2022-06-30       Impact factor: 13.352

7.  Uncovering PFAS and Other Xenobiotics in the Dark Metabolome Using Ion Mobility Spectrometry, Mass Defect Analysis, and Machine Learning.

Authors:  MaKayla Foster; Markace Rainey; Chandler Watson; James N Dodds; Kaylie I Kirkwood; Facundo M Fernández; Erin S Baker
Journal:  Environ Sci Technol       Date:  2022-06-02       Impact factor: 11.357

8.  Assembly and Curation of Lists of Per- and Polyfluoroalkyl Substances (PFAS) to Support Environmental Science Research.

Authors:  Antony J Williams; Linda G T Gaines; Christopher M Grulke; Charles N Lowe; Gabriel F B Sinclair; Vicente Samano; Inthirany Thillainadarajah; Bryan Meyer; Grace Patlewicz; Ann M Richard
Journal:  Front Environ Sci       Date:  2022-04-05

9.  A harmonized chemical monitoring database for support of exposure assessments.

Authors:  Kristin K Isaacs; Jonathan T Wall; Ashley R Williams; Kevin A Hobbie; Jon R Sobus; Elin Ulrich; David Lyons; Kathie L Dionisio; Antony J Williams; Christopher Grulke; Caroline A Foster; Josiah McCoy; Charles Bevington
Journal:  Sci Data       Date:  2022-06-16       Impact factor: 8.501

10.  Enabling High-Throughput Searches for Multiple Chemical Data Using the U.S.-EPA CompTox Chemicals Dashboard.

Authors:  Charles N Lowe; Antony J Williams
Journal:  J Chem Inf Model       Date:  2021-01-22       Impact factor: 4.956

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