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

Alarms About Structural Alerts

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Free PMC article

Alarms About Structural Alerts

Vinicius Alves et al. Green Chem. .
Free PMC article

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.

Conflict of interest statement

Conflict of interests The authors declare no actual or potential conflict of interests.

Figures

Figure 1
Figure 1
Contrasting alerts- and QSAR-based predictions in chemical safety assessment. Structural alerts are derived on small datasets and used in read-across for flagging unsafe compounds. QSAR models are developed on larger datasets and used to make binary, categorical, or quantitative prediction of compound toxicity.
Figure 2
Figure 2
Comparison of the profiles of the most important descriptors for two strong THR binders that differ by only one -CH2- fragment.
Figure 3
Figure 3
Evolution of toxicity of chloronitrobenzenes. The digits in the circles correspond to the positions of chlorines in aromatic ring.
Figure 4
Figure 4
Relative influence of structural fragments on toxicity of chlorosubstituted nitrobenzenes.
Figure 5
Figure 5
A workflow for generating QSAR-based structural alerts. As an example, QSAR-based structural alerts were used to guide molecular design of antiviral agents against human rhinovirus serotype 2.
Figure 6
Figure 6
Example of a structural transformation of pentanoic acid to octanoic acid to improve skin permeability (modified from Alves et al.).
Figure 7
Figure 7
Radial CBRA plot for eugenol. The central node representing the target compound eugenol is surrounded by biological (left side) and chemical (right side) neighbors. Jaccard distance is used to position the neighbors towards the target compound. Edges and nodes are colored according to the known activity classification (red = toxic, green = non-toxic).
Figure 8
Figure 8
Integrative approach for chemical safety assessment of new chemicals by combining structural alerts and QSAR models.

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