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An in silico algal toxicity model with a wide applicability potential for industrial chemicals and pharmaceuticals
Author(s) -
Önlü Serli,
Saçan Melek Türker
Publication year - 2017
Publication title -
environmental toxicology and chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 171
eISSN - 1552-8618
pISSN - 0730-7268
DOI - 10.1002/etc.3620
Subject(s) - prioritization , biochemical engineering , toxicity , aquatic toxicology , quantitative structure–activity relationship , in silico , environmental science , chemistry , computer science , engineering , management science , machine learning , biochemistry , organic chemistry , gene
The authors modeled the 72‐h algal toxicity data of hundreds of chemicals with different modes of action as a function of chemical structures. They developed mode of action–based local quantitative structure–toxicity relationship (QSTR) models for nonpolar and polar narcotics as well as a global QSTR model with a wide applicability potential for industrial chemicals and pharmaceuticals. The present study rigorously evaluated the generated models, meeting the Organisation for Economic Co‐operation and Development principles of robustness, validity, and transparency. The proposed global model had a broad structural coverage for the toxicity prediction of diverse chemicals (some of which are high–production volume chemicals) with no experimental toxicity data. The global model is potentially useful for endpoint predictions, the evaluation of algal toxicity screening, and the prioritization of chemicals, as well as for the decision of further testing and the development of risk‐management measures in a scientific and regulatory frame. Environ Toxicol Chem 2017;36:1012–1019. © 2016 SETAC