Literature DB >> 25700118

MOAtox: A comprehensive mode of action and acute aquatic toxicity database for predictive model development.

M G Barron1, C R Lilavois2, T M Martin3.   

Abstract

The mode of toxic action (MOA) has been recognized as a key determinant of chemical toxicity and as an alternative to chemical class-based predictive toxicity modeling. However, the development of quantitative structure activity relationship (QSAR) and other models has been limited by the availability of comprehensive high quality MOA and toxicity databases. The current study developed a dataset of MOA assignments for 1213 chemicals that included a diversity of metals, pesticides, and other organic compounds that encompassed six broad and 31 specific MOAs. MOA assignments were made using a combination of high confidence approaches that included international consensus classifications, QSAR predictions, and weight of evidence professional judgment based on an assessment of structure and literature information. A toxicity database of 674 acute values linked to chemical MOA was developed for fish and invertebrates. Additionally, species-specific measured or high confidence estimated acute values were developed for the four aquatic species with the most reported toxicity values: rainbow trout (Oncorhynchus mykiss), fathead minnow (Pimephales promelas), bluegill (Lepomis macrochirus), and the cladoceran (Daphnia magna). Measured acute toxicity values met strict standardization and quality assurance requirements. Toxicity values for chemicals with missing species-specific data were estimated using established interspecies correlation models and procedures (Web-ICE; http://epa.gov/ceampubl/fchain/webice/), with the highest confidence values selected. The resulting dataset of MOA assignments and paired toxicity values are provided in spreadsheet format as a comprehensive standardized dataset available for predictive aquatic toxicology model development. Published by Elsevier B.V.

Entities:  

Keywords:  Fish; Invertebrate; Mode of action; Model; QSAR; Toxicity

Mesh:

Substances:

Year:  2015        PMID: 25700118     DOI: 10.1016/j.aquatox.2015.02.001

Source DB:  PubMed          Journal:  Aquat Toxicol        ISSN: 0166-445X            Impact factor:   4.964


  18 in total

1.  Framework for Optimizing Selection of Interspecies Correlation Estimation Models to Address Species Diversity and Toxicity Gaps in an Aquatic Database.

Authors:  Adriana C Bejarano; Sandy Raimondo; Mace G Barron
Journal:  Environ Sci Technol       Date:  2017-07-06       Impact factor: 9.028

2.  A mechanism-based 3D-QSAR approach for classification and prediction of acetylcholinesterase inhibitory potency of organophosphate and carbamate analogs.

Authors:  Sehan Lee; Mace G Barron
Journal:  J Comput Aided Mol Des       Date:  2016-04-07       Impact factor: 3.686

3.  Acute sensitivity of a broad range of freshwater mussels to chemicals with different modes of toxic action.

Authors:  Ning Wang; Christopher D Ivey; Christopher G Ingersoll; William G Brumbaugh; David Alvarez; Edward J Hammer; Candice R Bauer; Tom Augspurger; Sandy Raimondo; M Christopher Barnhart
Journal:  Environ Toxicol Chem       Date:  2016-11-11       Impact factor: 3.742

4.  QSAR model for predicting the toxicity of organic compounds to fathead minnow.

Authors:  Qingzhu Jia; Yunpeng Zhao; Fangyou Yan; Qiang Wang
Journal:  Environ Sci Pollut Res Int       Date:  2018-10-22       Impact factor: 4.223

5.  Toward sustainable environmental quality: Priority research questions for Europe.

Authors:  Paul J Van den Brink; Alistair B A Boxall; Lorraine Maltby; Bryan W Brooks; Murray A Rudd; Thomas Backhaus; David Spurgeon; Violaine Verougstraete; Charmaine Ajao; Gerald T Ankley; Sabine E Apitz; Kathryn Arnold; Tomas Brodin; Miguel Cañedo-Argüelles; Jennifer Chapman; Jone Corrales; Marie-Agnès Coutellec; Teresa F Fernandes; Jerker Fick; Alex T Ford; Gemma Giménez Papiol; Ksenia J Groh; Thomas H Hutchinson; Hank Kruger; Jussi V K Kukkonen; Stefania Loutseti; Stuart Marshall; Derek Muir; Manuel E Ortiz-Santaliestra; Kai B Paul; Andreu Rico; Ismael Rodea-Palomares; Jörg Römbke; Tomas Rydberg; Helmut Segner; Mathijs Smit; Cornelis A M van Gestel; Marco Vighi; Inge Werner; Elke I Zimmer; Joke van Wensem
Journal:  Environ Toxicol Chem       Date:  2018-07-19       Impact factor: 3.742

6.  In silico prediction of pesticide aquatic toxicity with chemical category approaches.

Authors:  Fuxing Li; Defang Fan; Hao Wang; Hongbin Yang; Weihua Li; Yun Tang; Guixia Liu
Journal:  Toxicol Res (Camb)       Date:  2017-07-31       Impact factor: 3.524

7.  Mixed phylogenetic signal in fish toxicity data across chemical classes.

Authors:  Andrew Hylton; Ylenia Chiari; Isabella Capellini; Mace G Barron; Scott Glaberman
Journal:  Ecol Appl       Date:  2018-04       Impact factor: 4.657

8.  Application of Interspecies Correlation Estimation (ICE) models and QSAR in estimating species sensitivity to pesticides.

Authors:  S Raimondo; M G Barron
Journal:  SAR QSAR Environ Res       Date:  2019-11-14       Impact factor: 3.000

9.  Differentiating Pathway-Specific From Nonspecific Effects in High-Throughput Toxicity Data: A Foundation for Prioritizing Adverse Outcome Pathway Development.

Authors:  Kellie A Fay; Daniel L Villeneuve; Joe Swintek; Stephen W Edwards; Mark D Nelms; Brett R Blackwell; Gerald T Ankley
Journal:  Toxicol Sci       Date:  2018-06-01       Impact factor: 4.849

10.  Toxicity of oil spill response agents and crude oils to five aquatic test species.

Authors:  Mace G Barron; Adriana C Bejarano; Robyn N Conmy; Devi Sundaravadivelu; Peter Meyer
Journal:  Mar Pollut Bull       Date:  2020-02-07       Impact factor: 5.553

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.