Literature DB >> 33140634

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

Ann M Richard1, Ruili Huang2, Suramya Waidyanatha3, Paul Shinn2, Bradley J Collins3, Inthirany Thillainadarajah4, Christopher M Grulke1, Antony J Williams1, Ryan R Lougee1,5, Richard S Judson1, Keith A Houck1, Mahmoud Shobair1, Chihae Yang6,7, James F Rathman6,7, Adam Yasgar2, Suzanne C Fitzpatrick8, Anton Simeonov2, Russell S Thomas1, Kevin M Crofton1,9, Richard S Paules3,3, John R Bucher3, Christopher P Austin2, Robert J Kavlock1,10, Raymond R Tice3,11.   

Abstract

Since 2009, the Tox21 project has screened ∼8500 chemicals in more than 70 high-throughput assays, generating upward of 100 million data points, with all data publicly available through partner websites at the United States Environmental Protection Agency (EPA), National Center for Advancing Translational Sciences (NCATS), and National Toxicology Program (NTP). Underpinning this public effort is the largest compound library ever constructed specifically for improving understanding of the chemical basis of toxicity across research and regulatory domains. Each Tox21 federal partner brought specialized resources and capabilities to the partnership, including three approximately equal-sized compound libraries. All Tox21 data generated to date have resulted from a confluence of ideas, technologies, and expertise used to design, screen, and analyze the Tox21 10K library. The different programmatic objectives of the partners led to three distinct, overlapping compound libraries that, when combined, not only covered a diversity of chemical structures, use-categories, and properties but also incorporated many types of compound replicates. The history of development of the Tox21 "10K" chemical library and data workflows implemented to ensure quality chemical annotations and allow for various reproducibility assessments are described. Cheminformatics profiling demonstrates how the three partner libraries complement one another to expand the reach of each individual library, as reflected in coverage of regulatory lists, predicted toxicity end points, and physicochemical properties. ToxPrint chemotypes (CTs) and enrichment approaches further demonstrate how the combined partner libraries amplify structure-activity patterns that would otherwise not be detected. Finally, CT enrichments are used to probe global patterns of activity in combined ToxCast and Tox21 activity data sets relative to test-set size and chemical versus biological end point diversity, illustrating the power of CT approaches to discern patterns in chemical-activity data sets. These results support a central premise of the Tox21 program: A collaborative merging of programmatically distinct compound libraries would yield greater rewards than could be achieved separately.

Entities:  

Year:  2020        PMID: 33140634      PMCID: PMC7887805          DOI: 10.1021/acs.chemrestox.0c00264

Source DB:  PubMed          Journal:  Chem Res Toxicol        ISSN: 0893-228X            Impact factor:   3.739


  47 in total

Review 1.  Combinatorial compound libraries for drug discovery: an ongoing challenge.

Authors:  H Mario Geysen; Frank Schoenen; David Wagner; Richard Wagner
Journal:  Nat Rev Drug Discov       Date:  2003-03       Impact factor: 84.694

2.  New publicly available chemical query language, CSRML, to support chemotype representations for application to data mining and modeling.

Authors:  Chihae Yang; Aleksey Tarkhov; Jörg Marusczyk; Bruno Bienfait; Johann Gasteiger; Thomas Kleinoeder; Tomasz Magdziarz; Oliver Sacher; Christof H Schwab; Johannes Schwoebel; Lothar Terfloth; Kirk Arvidson; Ann Richard; Andrew Worth; James Rathman
Journal:  J Chem Inf Model       Date:  2015-02-19       Impact factor: 4.956

3.  ACToR--Aggregated Computational Toxicology Resource.

Authors:  Richard Judson; Ann Richard; David Dix; Keith Houck; Fathi Elloumi; Matthew Martin; Tommy Cathey; Thomas R Transue; Richard Spencer; Maritja Wolf
Journal:  Toxicol Appl Pharmacol       Date:  2008-07-11       Impact factor: 4.219

4.  A Data Analysis Pipeline Accounting for Artifacts in Tox21 Quantitative High-Throughput Screening Assays.

Authors:  Jui-Hua Hsieh; Alexander Sedykh; Ruili Huang; Menghang Xia; Raymond R Tice
Journal:  J Biomol Screen       Date:  2015-04-22

5.  Incorporating ToxCast and Tox21 datasets to rank biological activity of chemicals at Superfund sites in North Carolina.

Authors:  Sloane K Tilley; David M Reif; Rebecca C Fry
Journal:  Environ Int       Date:  2017-01-31       Impact factor: 9.621

6.  The US Federal Tox21 Program: A strategic and operational plan for continued leadership.

Authors:  Russell S Thomas; Richard S Paules; Anton Simeonov; Suzanne C Fitzpatrick; Kevin M Crofton; Warren M Casey; Donna L Mendrick
Journal:  ALTEX       Date:  2018-03-08       Impact factor: 6.043

7.  The NCGC pharmaceutical collection: a comprehensive resource of clinically approved drugs enabling repurposing and chemical genomics.

Authors:  Ruili Huang; Noel Southall; Yuhong Wang; Adam Yasgar; Paul Shinn; Ajit Jadhav; Dac-Trung Nguyen; Christopher P Austin
Journal:  Sci Transl Med       Date:  2011-04-27       Impact factor: 17.956

8.  Multidimensional in vivo hazard assessment using zebrafish.

Authors:  Lisa Truong; David M Reif; Lindsey St Mary; Mitra C Geier; Hao D Truong; Robert L Tanguay
Journal:  Toxicol Sci       Date:  2013-10-17       Impact factor: 4.849

Review 9.  Big data in chemical toxicity research: the use of high-throughput screening assays to identify potential toxicants.

Authors:  Hao Zhu; Jun Zhang; Marlene T Kim; Abena Boison; Alexander Sedykh; Kimberlee Moran
Journal:  Chem Res Toxicol       Date:  2014-09-16       Impact factor: 3.739

10.  PubChem Substance and Compound databases.

Authors:  Sunghwan Kim; Paul A Thiessen; Evan E Bolton; Jie Chen; Gang Fu; Asta Gindulyte; Lianyi Han; Jane He; Siqian He; Benjamin A Shoemaker; Jiyao Wang; Bo Yu; Jian Zhang; Stephen H Bryant
Journal:  Nucleic Acids Res       Date:  2015-09-22       Impact factor: 16.971

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  17 in total

1.  Cardiotoxicity Hazard and Risk Characterization of ToxCast Chemicals Using Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes from Multiple Donors.

Authors:  Sarah D Burnett; Alexander D Blanchette; Weihsueh A Chiu; Ivan Rusyn
Journal:  Chem Res Toxicol       Date:  2021-08-27       Impact factor: 3.739

2.  Current status and future directions for a neurotoxicity hazard assessment framework that integrates in silico approaches.

Authors:  Kevin M Crofton; Arianna Bassan; Mamta Behl; Yaroslav G Chushak; Ellen Fritsche; Jeffery M Gearhart; Mary Sue Marty; Moiz Mumtaz; Manuela Pavan; Patricia Ruiz; Magdalini Sachana; Rajamani Selvam; Timothy J Shafer; Lidiya Stavitskaya; David T Szabo; Steven T Szabo; Raymond R Tice; Dan Wilson; David Woolley; Glenn J Myatt
Journal:  Comput Toxicol       Date:  2022-03-17

3.  Prediction of drug-induced liver injury and cardiotoxicity using chemical structure and in vitro assay data.

Authors:  Lin Ye; Deborah K Ngan; Tuan Xu; Zhichao Liu; Jinghua Zhao; Srilatha Sakamuru; Li Zhang; Tongan Zhao; Menghang Xia; Anton Simeonov; Ruili Huang
Journal:  Toxicol Appl Pharmacol       Date:  2022-09-20       Impact factor: 4.460

Review 4.  Model systems and organisms for addressing inter- and intra-species variability in risk assessment.

Authors:  Ivan Rusyn; Weihsueh A Chiu; Fred A Wright
Journal:  Regul Toxicol Pharmacol       Date:  2022-05-28       Impact factor: 3.598

5.  A Quantitative High-Throughput Screening Data Analysis Pipeline for Activity Profiling.

Authors:  Ruili Huang
Journal:  Methods Mol Biol       Date:  2022

6.  High-Throughput Chemical Screening and Structure-Based Models to Predict hERG Inhibition.

Authors:  Shagun Krishna; Alexandre Borrel; Ruili Huang; Jinghua Zhao; Menghang Xia; Nicole Kleinstreuer
Journal:  Biology (Basel)       Date:  2022-01-28

Review 7.  A property-response perspective on modern toxicity assessment and drug toxicity index (DTI).

Authors:  Vaibhav A Dixit; Pragati Singh
Journal:  In Silico Pharmacol       Date:  2021-05-15

8.  Trade-off Predictivity and Explainability for Machine-Learning Powered Predictive Toxicology: An in-Depth Investigation with Tox21 Data Sets.

Authors:  Leihong Wu; Ruili Huang; Igor V Tetko; Zhonghua Xia; Joshua Xu; Weida Tong
Journal:  Chem Res Toxicol       Date:  2021-01-29       Impact factor: 3.739

9.  Assessing the calibration in toxicological in vitro models with conformal prediction.

Authors:  Ola Spjuth; Andrea Volkamer; Andrea Morger; Fredrik Svensson; Staffan Arvidsson McShane; Niharika Gauraha; Ulf Norinder
Journal:  J Cheminform       Date:  2021-04-29       Impact factor: 5.514

10.  MolData, a molecular benchmark for disease and target based machine learning.

Authors:  Arash Keshavarzi Arshadi; Milad Salem; Arash Firouzbakht; Jiann Shiun Yuan
Journal:  J Cheminform       Date:  2022-03-07       Impact factor: 5.514

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