Development of a new quantitative structure–activity relationship model for predicting Ames mutagenicity of food flavor chemicals using StarDrop™ auto-Modeller™
Abstract 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. Th...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2021-04-01
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| Series: | Genes and Environment |
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| Online Access: | https://doi.org/10.1186/s41021-021-00182-6 |
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| author | Toshio Kasamatsu Airi Kitazawa Sumie Tajima Masahiro Kaneko Kei-ichi Sugiyama Masami Yamada Manabu Yasui Kenichi Masumura Katsuyoshi Horibata Masamitsu Honma |
| author_facet | Toshio Kasamatsu Airi Kitazawa Sumie Tajima Masahiro Kaneko Kei-ichi Sugiyama Masami Yamada Manabu Yasui Kenichi Masumura Katsuyoshi Horibata Masamitsu Honma |
| author_sort | Toshio Kasamatsu |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-14T23:53:22Z |
| format | Article |
| id | doaj.art-231f1099647a4bfc9b82478cbe5b95a7 |
| institution | Directory Open Access Journal |
| issn | 1880-7062 |
| language | English |
| last_indexed | 2024-12-14T23:53:22Z |
| publishDate | 2021-04-01 |
| publisher | BMC |
| record_format | Article |
| series | Genes and Environment |
| spelling | doaj.art-231f1099647a4bfc9b82478cbe5b95a72022-12-21T22:43:11ZengBMCGenes and Environment1880-70622021-04-0143111710.1186/s41021-021-00182-6Development of a new quantitative structure–activity relationship model for predicting Ames mutagenicity of food flavor chemicals using StarDrop™ auto-Modeller™Toshio Kasamatsu0Airi Kitazawa1Sumie Tajima2Masahiro Kaneko3Kei-ichi Sugiyama4Masami Yamada5Manabu Yasui6Kenichi Masumura7Katsuyoshi Horibata8Masamitsu Honma9Division of Genetics and Mutagenesis, National Institute of Health SciencesDivision of Genetics and Mutagenesis, National Institute of Health SciencesHULINKS Inc.HULINKS Inc.Division of Genetics and Mutagenesis, National Institute of Health SciencesDivision of Genetics and Mutagenesis, National Institute of Health SciencesDivision of Genetics and Mutagenesis, National Institute of Health SciencesDivision of Genetics and Mutagenesis, National Institute of Health SciencesDivision of Genetics and Mutagenesis, National Institute of Health SciencesDivision of Genetics and Mutagenesis, National Institute of Health SciencesAbstract 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.https://doi.org/10.1186/s41021-021-00182-6Quantitative structure–activity relationship (QSAR)Food flavorsMutagenicity Ames testStarDrop™ auto-Modeller™Machine learning |
| spellingShingle | Toshio Kasamatsu Airi Kitazawa Sumie Tajima Masahiro Kaneko Kei-ichi Sugiyama Masami Yamada Manabu Yasui Kenichi Masumura Katsuyoshi Horibata Masamitsu Honma Development of a new quantitative structure–activity relationship model for predicting Ames mutagenicity of food flavor chemicals using StarDrop™ auto-Modeller™ Genes and Environment Quantitative structure–activity relationship (QSAR) Food flavors Mutagenicity Ames test StarDrop™ auto-Modeller™ Machine learning |
| title | Development of a new quantitative structure–activity relationship model for predicting Ames mutagenicity of food flavor chemicals using StarDrop™ auto-Modeller™ |
| title_full | Development of a new quantitative structure–activity relationship model for predicting Ames mutagenicity of food flavor chemicals using StarDrop™ auto-Modeller™ |
| title_fullStr | Development of a new quantitative structure–activity relationship model for predicting Ames mutagenicity of food flavor chemicals using StarDrop™ auto-Modeller™ |
| title_full_unstemmed | Development of a new quantitative structure–activity relationship model for predicting Ames mutagenicity of food flavor chemicals using StarDrop™ auto-Modeller™ |
| title_short | Development of a new quantitative structure–activity relationship model for predicting Ames mutagenicity of food flavor chemicals using StarDrop™ auto-Modeller™ |
| title_sort | development of a new quantitative structure activity relationship model for predicting ames mutagenicity of food flavor chemicals using stardrop™ auto modeller™ |
| topic | Quantitative structure–activity relationship (QSAR) Food flavors Mutagenicity Ames test StarDrop™ auto-Modeller™ Machine learning |
| url | https://doi.org/10.1186/s41021-021-00182-6 |
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