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Green Chem. 2016 Aug 21;18(16):4348-4360. doi: 10.1039/C6GC01492E. Epub 2016 Jun 28.

Alarms about structural alerts.

Author information

1
Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA.
2
Laboratory for Molecular Modeling and Design, Department of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil.
3
Department of Chemical Technology, Odessa National Polytechnic University, Odessa, 65000, Ukraine.
4
Netflix, San Francisco, CA 94123, USA.
5
National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, Rockville, MD 20850, USA.
6
Sciome LLC, Research Triangle Park, NC 27709, USA.
7
Laboratory of Theoretical Chemistry, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080, Ukraine.
8
Department of Chemistry and Bioinformatics Research Center, North Carolina State University, Raleigh, NC, 27695, USA.

Abstract

Structural alerts are widely accepted in chemical toxicology and regulatory decision support as a simple and transparent means to flag potential chemical hazards or group compounds into categories for read-across. However, there has been a growing concern that alerts disproportionally flag too many chemicals as toxic, which questions their reliability as toxicity markers. Conversely, the rigorously developed and properly validated statistical QSAR models can accurately and reliably predict the toxicity of a chemical; however, their use in regulatory toxicology has been hampered by the lack of transparency and interpretability. We demonstrate that contrary to the common perception of QSAR models as "black boxes" they can be used to identify statistically significant chemical substructures (QSAR-based alerts) that influence toxicity. We show through several case studies, however, that the mere presence of structural alerts in a chemical, irrespective of the derivation method (expert-based or QSAR-based), should be perceived only as hypotheses of possible toxicological effect. We propose a new approach that synergistically integrates structural alerts and rigorously validated QSAR models for a more transparent and accurate safety assessment of new chemicals.

KEYWORDS:

QSAR; green chemistry; read-across; structural alerts; toxicity

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