In silico the Ames Mutagenicity Predictive Model of Environment
DOI:
https://doi.org/10.20535/ibb.2025.9.2.316239Keywords:
mutation, genotoxicity, QSAR model, molecular descriptors, machine learning modelsAbstract
Background. The classical in vitro and in vivo methods developed and widely used in the past decades to assess the genetic effects of environmental factors are complex in view of their implementation, are expensive, long-lasting, have the problem of reproducibility of the results of experiment in different laboratories and may face ethical problems of using warm-blooded animals in experiments.
Objective. Development, optimisation and testing of effective in silico models for assessment of Ames mutagenicity of environmental factors.
Methods. The genetic assessment of the impact of environmental factors was carried out in accordance with a set of chemical compounds for which information on potential mutagenic activity was obtained experimentally, using the in vitro Ames Salmonella/microsome test. Four machine learning models were developed to solve the problem of binary classification to form two classes of xenobiotics (mutagen/non-mutagen). The total sample is represented by a set of 8,083 xenobiotics.
Results. We developed four machine learning models with 85% accuracy, matching the reproducibility of Ames test data across laboratories. In addition, we have proposed a binary classifier that subject to dimensionality reduction of the input data, taking into account the qualitative composition of molecular descriptors, allows us to improve the accuracy of in silico prediction of genotoxicity of chemicals.
Conclusions. The necessity of updating and expanding the list of effective and more productive methods and approaches for assessing the genotoxic effects of environmental factors is substantiated, which allows avoiding the use of warm-blooded animals in the experiment, saving time and reducing the number of false-negative and false-positive results. The possibility of increase the accuracy of predictive machine learning models for assessing the genotoxic potential of environmental factors in conditions of dimensionality reduction of the data set is presented.
References
Honma M. An assessment of mutagenicity of chemical substances by (quantitative) structure-activity relationship. Genes and Environment. 2020;42:23. DOI: 10.1186/s41021-020-00163-1
Burgess JT, Rose M, Boucher D, Plowman J, Molloy C, Fisher M, et al. The Therapeutic Potential of DNA Damage Repair Pathways and Genomic Stability in Lung Cancer. Frontiers in Oncology. 2020;10:1256. DOI: 10.3389/fonc.2020.01256
Milanese C, Cerri S, Ulusoy A, Gornati SV, Plat A, Gabriels S, et al. Activation of the DNA damage response in vivo in synucleinopathy models of Parkinson's disease. Cell Death & Disease. 2018;9(8):818. DOI: 10.1038/s41419-018-0848-7
Waller R, Murphy M, Garwood CJ, Jennings L, Heath PR, Chambers A, et al. Metallothionein-I/II expression associates with the astrocyte DNA damage response and not Alzheimer-type pathology in the aging brain. Glia. 2018;66(11):2316-23. DOI: 10.1002/glia.23465
Wang H, Li S, Oaks J, Ren J, Li L, Wu X. The concerted roles of FANCM and Rad52 in the protection of common fragile sites. Nature Communications. 2018;9(1):2791. DOI: 10.1038/s41467-018-05066-y
Yalovenko OI, Ostanina NV, Dugan OM. Tinted balms in the system of staged testing of their potential mutagenic effect on genetically modified microorganisms. Cytology and Genetics. 2005;39(6):60-5.
Yalovenko OI. Assessment of the biological action of persistent hair dyes using alternative test systems. Environment and Health. 2006;(1):58-63.
Yalovenko OI, Raetska OV, Maistrenko ZY, Dugan OM. Assessment of mutagenic activity of individual ingredients and finished products (perfume, cosmetic, detergent) in alternative biological test systems. Environment and Health. 2005;(2):63-4.
Chen L, Li N, Liu Y, Faquet B, Alépée N, Ding C, et al. A new 3D model for genotoxicity assessment: EpiSkin™ Micronucleus Assay. Mutagenesis. 2021;36(1):51-61. DOI: 10.1093/mutage/geaa003
Chung YH, Gulumian M, Pleus RC, Yu IJ. Animal Welfare Considerations When Conducting OECD Test Guideline Inhalation and Toxicokinetic Studies for Nanomaterials. Animals. 2022;12(23):3305. DOI: 10.3390/ani12233305
Test No. 471: Bacterial Reverse Mutation Test. In: OECD Guidelines for the Testing of Chemicals. Paris: OECD Publishing, 2020. DOI: 10.1787/9789264071247-en
Test No. 473: In Vitro Mammalian Chromosomal Aberration Test. In: OECD Guidelines for the Testing of Chemicals. Paris: OECD Publishing, 2016. DOI: 10.1787/9789264264649-en
Test No. 487: In Vitro Mammalian Cell Micronucleus Test. In: OECD Guidelines for the Testing of Chemicals. Paris: OECD Publishing, 2023. DOI: 10.1787/9789264264861-en
Organisation for Economic Co-operation and Development (OECD). Test No. 489: In Vivo Mammalian Alkaline Comet Assay. In: OECD Guidelines for the Testing of Chemicals, Section 4. Paris: OECD Publishing; 2016. DOI: 10.1787/9789264264885-en
Test No. 474: Mammalian Erythrocyte Micronucleus Test. In: OECD Guidelines for the Testing of Chemicals. Paris: OECD Publishing, 2016. DOI: 10.1787/9789264264762-en
Test No. 475: Mammalian Bone Marrow Chromosomal Aberration Test. In: OECD Guidelines for the Testing of Chemicals. Paris: OECD Publishing, 2016. DOI: 10.1787/9789264264786-en
Test No. 476: In Vitro Mammalian Cell Gene Mutation Tests using the Hprt and xprt genes. In: OECD Guidelines for the Testing of Chemicals. Paris: OECD Publishing, 2016. DOI: 10.1787/9789264264809-en
Organisation for Economic Co-operation and Development (OECD). Test No. 478: Rodent Dominant Lethal Test. In: OECD Guidelines for the Testing of Chemicals, Section 4. Paris: OECD Publishing; 2016. DOI: 10.1787/9789264264823-en
Organisation for Economic Co-operation and Development (OECD). Test No. 485: Genetic toxicology, Mouse Heritable Translocation Assay. In: OECD Guidelines for the Testing of Chemicals, Section 4. Paris: OECD Publishing; 2016. DOI: 10.1787/9789264071506-en
Organisation for Economic Co-operation and Development (OECD). Test No. 486: Unscheduled DNA Synthesis (UDS) Test with Mammalian Liver Cells in vivo. In: OECD Guidelines for the Testing of Chemicals, Section 4. Paris: OECD Publishing; 1997. DOI: 10.1787/9789264071520-en
Lou C, Yang H, Deng H, Huang M, Li W, Liu G, et al. Chemical rules for optimization of chemical mutagenicity via matched molecular pairs analysis and machine learning methods. Journal of Cheminformatics. 2023;15(1):35. DOI: 10.1186/s13321-023-00707-x
Shinada NK, Koyama N, Ikemori M, Nishioka T, Hitaoka S, Hakura A, et al. Optimizing machine-learning models for mutagenicity prediction through better feature selection. Mutagenesis. 2022;37(3-4):191-202. DOI: 10.1093/mutage/geac010
Luan Y, Honma M. Genotoxicity testing and recent advances. Genome Instability & Disease. 2022;3(1):1-21. DOI: 10.1007/s42764-021-00058-7
Mohamed S, Sabita U, Rajendra S, Raman D. Genotoxicity: Mechanisms, Testing Guidelines and Methods. Global Journal of Pharmacy & Pharmaceutical Sciences. 2017;1(5):555575. DOI: 10.19080/GJPPS.2017.01.555575
International Conference on Harmonisation; guidance on S2(R1) Genotoxicity Testing and Data Interpretation for Pharmaceuticals intended for Human Use; availability. Notice. Federal Register. 2012;77(110):33748-9.
Lui R, Guan D, Matthews S. Mechanistic Task Groupings Enhance Multitask Deep Learning of Strain-Specific Ames Mutagenicity. Chemical Research in Toxicology. 2023;36(8):1248-54. DOI: 10.1021/acs.chemrestox.2c00385
Li T, Liu Z, Thakkar S, Roberts R, Tong W. DeepAmes: A deep learning-powered Ames test predictive model with potential for regulatory application. Regulatory Toxicology and Pharmacology. 2023;144:105486. DOI: 10.1016/j.yrtph.2023.105486
Kalian AD, Benfenati E, Osborne OJ, Gott D, Potter C, Dorne J-LCM, et al. Exploring Dimensionality Reduction Techniques for Deep Learning Driven QSAR Models of Mutagenicity. Toxics. 2023;11(7):572. DOI: 10.3390/toxics11070572
Guo W, Liu J, Dong F, Song M, Li Z, Khan MKH, et al. Review of machine learning and deep learning models for toxicity prediction. Experimental Biology and Medicine. 2023;248(21):1952-73. DOI: 10.1177/15353702231209421
Kislyak S, Duhan O, Yalovenko O. Systems for Genetic Assessment of the Impact of Environmental Factors. Innovative Biosystems and Bioengineering. 2024;8(2):3-27. DOI: 10.20535/ibb.2024.8.2.288127
Helma C, Schöning V, Drewe J, Boss P. A Comparison of Nine Machine Learning Mutagenicity Models and Their Application for Predicting Pyrrolizidine Alkaloids. Frontiers in Pharmacology. 2021;12:708050. DOI: 10.3389/fphar.2021.708050
Kazius J, McGuire R, Bursi R. Derivation and validation of toxicophores for mutagenicity prediction. Journal of Medicinal Chemistry. 2005;48(1):312-20. DOI: 10.1021/jm040835a
Hansen K, Mika S, Schroeter T, Sutter A, ter Laak A, Steger-Hartmann T, et al. Benchmark data set for in silico prediction of Ames mutagenicity. Journal of Chemical Information and Modeling. 2009;49(9):2077-81. DOI: 10.1021/ci900161g
Scientific Opinion on Pyrrolizidine alkaloids in food and feed. EFSA Journal. 2011;9(11):2406. DOI: 10.2903/j.efsa.2011.2406
Du J. The Frontier of SGD and Its Variants in Machine Learning. Journal of Physics: Conference Series. 2019;1229(1):012046. DOI: 10.1088/1742-6596/1229/1/012046
Kingma DP, Ba JA. A method for stochastic optimization. In: 3rd International Conference for Learning Representations; 2015 May 7-9; San Diego. DOI: 10.48550/arXiv.1412.6980
Fan D, Yang H, Li F, Sun L, Di P, Li W, et al. In silico prediction of chemical genotoxicity using machine learning methods and structural alerts. Toxicol Res (Camb). 2017 Dec 15;7(2):211-20. DOI: 10.1039/c7tx00259a
Nguyen-Vo TH, Nguyen L, Do N, Le PH, Nguyen TN, Nguyen BP, et al. Predicting drug-induced liver injury using convolutional neural network and molecular fingerprint-embedded features. ACS Omega. 2020;5(39):25432-9. DOI: 10.1021/acsomega.0c03866
Nahm FS. Receiver operating characteristic curve: overview and practical use for clinicians. Korean J Anesthesiol. 2022 Feb;75(1):25-36. DOI: 10.4097/kja.21209
Guyon I, Elisseeff A, Kaelbling LP, editors. An introduction of variable and feature selection. J Machine Learn Res. 2003;3(708)1157-82. DOI: 10.1162/153244303322753616
Barylyak IR, Dugan OM. Ecological and genetic studies in Ukraine. Cytol Genet. 2002;5:3-10.
Sofuni T. Evolution of genotoxicity test methods in Japan. Genes Environ. 2017;39:15. DOI: 10.1186/s41021-016-0063-7
Furukawa A, Ono S, Yamada K, Torimoto N, Asayama M, Muto S. A local QSAR model based on the stability of nitrenium ions to support the ICH M7 expert review on the mutagenicity of primary aromatic amines. Genes Environ. 2022 Mar 21;44(1):10. DOI: 10.1186/s41021-022-00238-1
Rane R, Satpute B, Kumar D, Suryawanshi M, Prabhune AG, Gawade B, et al. Mutagenic and genotoxic in silico QSAR prediction of dimer impurity of gliflozins; canagliflozin, dapaglifozin, and emphagliflozin and in vitro evaluation by Ames and micronucleus test. Drug Chem Toxicol. 2025 Mar;48(2):416-25. DOI: 10.1080/01480545.2024.2378768
Honma M, Kitazawa A, Cayley A, Williams RV, Barber C, Hanser T, et al. Improvement of quantitative structure-activity relationship (QSAR) tools for predicting Ames mutagenicity: outcomes of the Ames/QSAR International Challenge Project. Mutagenesis. 2019 Mar 6;34(1):3-16. DOI: 10.1093/mutage/gey031
Yang X, Zhang Z, Li Q, Cai Y. Quantitative structure-activity relationship models for genotoxicity prediction based on combination evaluation strategies for toxicological alternative experiments. Sci Rep. 2021;11(1):8030. DOI: 10.1038/s41598-021-87035-y
Benigni R, Bassan A, Pavan M. In silico models for genotoxicity and drug regulation. Expert Opin Drug Metab Toxicol. 2020 Aug;16(8):651-62. DOI: 10.1080/17425255.2020.1785428
Yu X. Prediction of chemical toxicity to Tetrahymena pyriformis with four-descriptor models. Ecotoxicol Environ Saf. 2020 Mar 1;190:110146. DOI: 10.1016/j.ecoenv.2019.110146
Guan D, Fan K, Spence I, Matthews S. QSAR ligand dataset for modelling mutagenicity, genotoxicity, and rodent carcinogenicity. Data Brief. 2018 Feb 2;17:876-84. DOI: 10.1016/j.dib.2018.01.077
Votano JR, Parham M, Hall LH, Kier LB, Oloff S, Tropsha A, et al. Three new consensus QSAR models for the prediction of Ames genotoxicity. Mutagenesis. 2004 Sep;19(5):365-77. DOI: 10.1093/mutage/geh043
Galimberti F, Moretto A, Papa E. Application of chemometric methods and QSAR models to support pesticide risk assessment starting from ecotoxicological datasets. Water Res. 2020 May 1;174:115583. DOI: 10.1016/j.watres.2020.115583
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 The Author(s)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The ownership of copyright remains with the Authors.
Authors may use their own material in other publications provided that the Journal is acknowledged as the original place of publication and National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” as the Publisher.
Authors are reminded that it is their responsibility to comply with copyright laws. It is essential to ensure that no part of the text or illustrations have appeared or are due to appear in other publications, without prior permission from the copyright holder.
IBB articles are published under Creative Commons licence:- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under CC BY 4.0 that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
