Literature DB >> 30741315

Generalized Read-Across (GenRA): A workflow implemented into the EPA CompTox Chemicals Dashboard.

George Helman1,2, Imran Shah2, Antony J Williams2, Jeff Edwards2, Jeremy Dunne2, Grace Patlewicz2.   

Abstract

Generalized Read-Across (GenRA) is a data driven approach which makes read-across predictions on the basis of a similarity weighted activity of source analogues (nearest neighbors). GenRA has been described in more detail in the literature (Shah et al., 2016; Helman et al., 2018). Here we present its implementation within the EPA's CompTox Chemicals Dashboard to provide public access to a GenRA module structured as a read-across workflow. GenRA assists researchers in identifying source analogues, evaluating their validity and making predictions of in vivo toxicity effects for a target substance. Predictions are presented as binary outcomes reflecting presence or absence of toxicity together with quantitative measures of uncertainty. The approach allows users to identify analogues in different ways, quickly assess the availability of relevant in vivo data for those analogues and visualize these in a data matrix to evaluate the consistency and concordance of the available experimental data for those analogues before making a GenRA prediction. Predictions can be exported into a tab-separated value (TSV) or Excel file for additional review and analysis (e.g., doses of analogues associated with production of toxic effects).  GenRA offers a new capability of making reproducible read-across predictions in an easy-to use-interface.

Entities:  

Keywords:  read-across; data gap filling; chemical fingerprints; ToxCast; ToxRefDB

Mesh:

Substances:

Year:  2019        PMID: 30741315      PMCID: PMC6679759          DOI: 10.14573/altex.1811292

Source DB:  PubMed          Journal:  ALTEX        ISSN: 1868-596X            Impact factor:   6.043


  7 in total

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Journal:  J Chem Inf Model       Date:  2010-05-24       Impact factor: 4.956

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Journal:  J Chem Inf Model       Date:  2015-02-19       Impact factor: 4.956

3.  Navigating through the minefield of read-across tools: A review of in silico tools for grouping.

Authors:  Patlewicz Grace; Helman George; Pradeep Prachi; Shah Imran
Journal:  Comput Toxicol       Date:  2017-08

4.  Systematically evaluating read-across prediction and performance using a local validity approach characterized by chemical structure and bioactivity information.

Authors:  Imran Shah; Jie Liu; Richard S Judson; Russell S Thomas; Grace Patlewicz
Journal:  Regul Toxicol Pharmacol       Date:  2016-05-09       Impact factor: 3.271

5.  ToxCast Chemical Landscape: Paving the Road to 21st Century Toxicology.

Authors:  Ann M Richard; Richard S Judson; Keith A Houck; Christopher M Grulke; Patra Volarath; Inthirany Thillainadarajah; Chihae Yang; James Rathman; Matthew T Martin; John F Wambaugh; Thomas B Knudsen; Jayaram Kancherla; Kamel Mansouri; Grace Patlewicz; Antony J Williams; Stephen B Little; Kevin M Crofton; Russell S Thomas
Journal:  Chem Res Toxicol       Date:  2016-07-20       Impact factor: 3.739

6.  Integrative chemical-biological read-across approach for chemical hazard classification.

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7.  The CompTox Chemistry Dashboard: a community data resource for environmental chemistry.

Authors:  Antony J Williams; Christopher M Grulke; Jeff Edwards; Andrew D McEachran; Kamel Mansouri; Nancy C Baker; Grace Patlewicz; Imran Shah; John F Wambaugh; Richard S Judson; Ann M Richard
Journal:  J Cheminform       Date:  2017-11-28       Impact factor: 5.514

  7 in total
  12 in total

1.  Transitioning the Generalised Read-Across approach (GenRA) to quantitative predictions: A case study using acute oral toxicity data.

Authors:  George Helman; Imran Shah; Grace Patlewicz
Journal:  Comput Toxicol       Date:  2019-11-01

2.  Exploring current read-across applications and needs among selected U.S. Federal Agencies.

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Journal:  Regul Toxicol Pharmacol       Date:  2019-05-10       Impact factor: 3.271

3.  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

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Journal:  ACS Sustain Chem Eng       Date:  2021-06-01       Impact factor: 9.224

5.  Generalised Read-Across prediction using genra-py.

Authors:  Imran Shah; Tia Tate; Grace Patlewicz
Journal:  Bioinformatics       Date:  2021-03-27       Impact factor: 6.931

Review 6.  In silico toxicology: From structure-activity relationships towards deep learning and adverse outcome pathways.

Authors:  Jennifer Hemmerich; Gerhard F Ecker
Journal:  Wiley Interdiscip Rev Comput Mol Sci       Date:  2020-03-31

7.  Development of the InTelligence And Machine LEarning (TAME) Toolkit for Introductory Data Science, Chemical-Biological Analyses, Predictive Modeling, and Database Mining for Environmental Health Research.

Authors:  Kyle Roell; Lauren E Koval; Rebecca Boyles; Grace Patlewicz; Caroline Ring; Cynthia V Rider; Cavin Ward-Caviness; David M Reif; Ilona Jaspers; Rebecca C Fry; Julia E Rager
Journal:  Front Toxicol       Date:  2022-06-22

8.  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

9.  An Automated Fast Healthcare Interoperability Resources-Based 12-Lead Electrocardiogram Mobile Alert System for Suspected Acute Coronary Syndrome.

Authors:  Sujeong Hur; Jeanhyoung Lee; Taerim Kim; Jong Soo Choi; Mira Kang; Dong Kyung Chang; Won Chul Cha
Journal:  Yonsei Med J       Date:  2020-05       Impact factor: 2.759

10.  Clustering a Chemical Inventory for Safety Assessment of Fragrance Ingredients: Identifying Read-Across Analogs to Address Data Gaps.

Authors:  Mihir S Date; Devin O'Brien; Danielle J Botelho; Terry W Schultz; Daniel C Liebler; Trevor M Penning; Daniel T Salvito
Journal:  Chem Res Toxicol       Date:  2020-05-06       Impact factor: 3.739

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