Evaluation of a statistics-based Ames mutagenicity QSAR model and interpretation of the results obtained

Barber, Chris; Cayley, Alex; Hanser, Thierry; Harding, A P; Hegheş, Crina; Vessey, Jonathan D.; Werner, Stéphane; Weiner, Sandy; Wichard, Joerg; Giddings, Amanda; Glowienke, Susanne; Parenty, Alexis; Brigo, Alessandro; Spirkl, Hans-Peter; Amberg, Alexander; Kemper, Ray; Greene, Nigel

doi:10.1016/j.yrtph.2015.12.006

Cited by 43 publications

(20 citation statements)

References 21 publications

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“…In another study on the variability of performance (Barber et al, 2016), the statistical prediction system Sarah Nexus (version 1.1) was validated against 14 private Ames test data sets supplied by nine pharmaceutical companies ( Figure 3). The data sets sizes ranged 100 to 2100 chemicals.…”

Section: Evaluations 3 Systemsmentioning

confidence: 99%

Evaluation of the applicability of existing (Q)SAR models for predicting the genotoxicity of pesticides and similarity analysis related with genotoxicity of pesticides for facilitating of grouping and read across

Benigni¹,

Battistelli²,

Bossa³

et al. 2019

EFS3

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To facilitate the practical implementation of the guidance on the residue definition for dietary risk assessment, EFSA has organized an evaluation of applicability of existing in silico models for predicting the genotoxicity of pesticides and their metabolites, including analysis of the impact on genotoxicity of the metabolic structural changes. The prediction ability of (Q)SARs for in vitro and in vivo tests were evaluated. For the Ames test, all (Q)SAR models generated statistically significant predictions, comparable with the experimental variability of the test; instead, the reliability of the (Q)SAR models for assays / endpoints different from in vitro bacterial mutagenicity appears to be quite far from optimality. Secondly, two new Read Across approaches were applied to predict Ames mutagenicity and in vitro Chromosomal Aberrations: Read Across was largely successful for predicting the Ames test results, but much less for in vitro Chromosomal Aberrations. The worse results for endpoints different from Ames may be attributable to the several revisions of experimental protocols and evaluation criteria of results that have made the databases qualitatively non‐homogeneous, and poorly suitable for modelling. A third dimension of this research is the evaluation of the impact of the structural changes‐in result of metabolic or degradation processes‐on the genotoxic potential of the substances. Parent/Metabolite structural differences (beyond the known Structural Alerts) that may, or may not cause changes in the Ames mutagenicity were identified and catalogued. In addition, Structural Alerts analysis applied under human expert supervision permitted the rationalization of the large majority of the changes of patterns of genotoxicity. The findings from this work are suitable for being integrated into Weight‐of‐Evidence and Tiered evaluation schemes. The importance of the human expert knowledge is particularly emphasized.

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Section: Evaluations 3 Systemsmentioning

confidence: 99%

Evaluation of the applicability of existing (Q)SAR models for predicting the genotoxicity of pesticides and similarity analysis related with genotoxicity of pesticides for facilitating of grouping and read across

Benigni¹,

Battistelli²,

Bossa³

et al. 2019

EFS3

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show abstract

“…QSAR methods are currently one of the most popular ligand based methods for predicting not only metabolic but other forms of toxicity like genotoxicity, skin sensitization, cardiac toxicity, neurotoxicity, and carcinogenicity . In these methods there are a number of pitfalls that one can fall into if proper consideration is not given to a number of points like applicability domain, well defined end points, predictivity, robustness and possibility of mechanistic interpretations etc.…”

Section: Computational Methods For Som Prediction For Cyp450 Mediatementioning

confidence: 99%

Advances in Computational Prediction of Regioselective and Isoform-Specific Drug Metabolism Catalyzed by CYP450s.

Dixit

Deshpande

2016

ChemistrySelect

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Prediction of drug metabolism is an important step in the early lead optimization phase and preclinical studies. Its main purpose is to avoid late stage (Phase I–III) or post‐marketing withdrawals which usually results in a significant financial loss to a pharmaceutical company. Computational methods for identification of soft‐spots in the molecules (site of metabolism; SOM) and CYP450 isoforms responsible for drug metabolism have over the past ten years proved an indispensable tool towards achieving this goal. These methods can be broadly classified into i) ligand based, ii) structure based and iii) hybrid methods. In this review we discuss some of the most successful methods of SOM and isoform specificity prediction, their application domain, advantages, and limitations along with recent developments in the last 5 years in this area. Recent applications of molecular dynamics (MD), quantum mechanics (QM) and quantum mechanics/molecular mechanics (QM/MM) methodology for regioselective and isoform specific drug metabolism predictions are also discussed. Finally, a summary of these methods along with expected developments, future challenges and opportunities are discussed.

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“…DEREK Nexus™ is a knowledge-based commercial software developed by Lhasa Limited, UK [9,10]. The software includes knowledge rules created by considering insights related to structural alert, chemical compound examples, and metabolic activations and mechanisms.…”

Section: Commercial Qsar Toolsmentioning

confidence: 99%

Development of a new quantitative structure–activity relationship model for predicting Ames mutagenicity of food flavor chemicals using StarDrop™ auto-Modeller™

Kasamatsu

Kitazawa

Tajima³

et al. 2021

Genes and Environ

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Background Food flavors are relatively low molecular weight chemicals with unique odor-related functional groups that may also be associated with mutagenicity. These chemicals are often difficult to test for mutagenicity by the Ames test because of their low production and peculiar odor. Therefore, application of the quantitative structure–activity relationship (QSAR) approach is being considered. We used the StarDrop™ Auto-Modeller™ to develop a new QSAR model. Results In the first step, we developed a new robust Ames database of 406 food flavor chemicals consisting of existing Ames flavor chemical data and newly acquired Ames test data. Ames results for some existing flavor chemicals have been revised by expert reviews. We also collected 428 Ames test datasets for industrial chemicals from other databases that are structurally similar to flavor chemicals. A total of 834 chemicals’ Ames test datasets were used to develop the new QSAR models. We repeated the development and verification of prototypes by selecting appropriate modeling methods and descriptors and developed a local QSAR model. A new QSAR model “StarDrop NIHS 834_67” showed excellent performance (sensitivity: 79.5%, specificity: 96.4%, accuracy: 94.6%) for predicting Ames mutagenicity of 406 food flavors and was better than other commercial QSAR tools. Conclusions A local QSAR model, StarDrop NIHS 834_67, was customized to predict the Ames mutagenicity of food flavor chemicals and other low molecular weight chemicals. The model can be used to assess the mutagenicity of food flavors without actual testing.

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Evaluation of a statistics-based Ames mutagenicity QSAR model and interpretation of the results obtained

Cited by 43 publications

References 21 publications

Evaluation of the applicability of existing (Q)SAR models for predicting the genotoxicity of pesticides and similarity analysis related with genotoxicity of pesticides for facilitating of grouping and read across

Evaluation of the applicability of existing (Q)SAR models for predicting the genotoxicity of pesticides and similarity analysis related with genotoxicity of pesticides for facilitating of grouping and read across

Advances in Computational Prediction of Regioselective and Isoform-Specific Drug Metabolism Catalyzed by CYP450s.

Development of a new quantitative structure–activity relationship model for predicting Ames mutagenicity of food flavor chemicals using StarDrop™ auto-Modeller™

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