ArticleToxicological sciences : an official journal of the Society of Toxicology2018
Investigating the Generalizability of the MultiFlow ® DNA Damage Assay and Several Companion Machine Learning Models With a Set of 103 Diverse Test Chemicals.
Article in Toxicological sciences : an official journal of the Society of Toxicology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Lack of Genotoxic and Carcinogenic Potential for Nonsugar Sweeteners: A Review of Animal and Mechanistic Evidence.Advances in nutrition (Bethesda, Md.) · 2025Review
- Application of Biological Modifiers to a Multiplexed, Human Cell-Based DNA Damage Assay Provides Mechanistic Information on Genotoxicity and Molecular Targets.Environmental and molecular mutagenesis · 2025Article
- Visualization strategies to aid interpretation of high-dimensional genotoxicity data.Environmental and molecular mutagenesis · 2024Article
- Scientific opinion on the renewal of the authorisation of Smoke Concentrate 809045 (SF-003) as a smoke flavouring Primary Product.EFSA journal. European Food Safety Authority · 2023Article
- Scientific opinion on the renewal of the authorisation of proFagus Smoke R709 (SF-008) as a smoke flavouring Primary Product.EFSA journal. European Food Safety Authority · 2023Article
- Review of machine learning and deep learning models for toxicity prediction.Experimental biology and medicine (Maywood, N.J.) · 2023Review
- Scientific opinion on the renewal of the authorisation of proFagus Smoke R714 (SF-001) as a smoke flavouring Primary Product.EFSA journal. European Food Safety Authority · 2023Article
- Genotoxicity evaluation of nitrosamine impurities using human TK6 cells transduced with cytochrome P450s.Archives of toxicology · 2022Article
- Comprehensive interpretation of in vitro micronucleus test results for 292 chemicals: from hazard identification to risk assessment application.Archives of toxicology · 2022Article
- In vitro human cell-based aneugen molecular mechanism assay.Environmental and molecular mutagenesis · 2022Article
- Implementing in vitro bioactivity data to modernize priority setting of chemical inventories.ALTEX · 2022Article
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- A deep neural network-based approach for prediction of mutagenicity of compounds.Environmental science and pollution research international · 2021Article
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- Performance of HepaRG and HepG2 cells in the high-throughput micronucleus assay for in vitro genotoxicity assessment.Journal of toxicology and environmental health. Part A · 2020Article
- Evidence for an Aneugenic Mechanism of Action for Micronucleus Induction by Black Cohosh Extract.Environmental and molecular mutagenesis · 2019Article
- Genetic toxicology in silico protocol.Regulatory toxicology and pharmacology : RTP · 2019Article
- Aneugen Molecular Mechanism Assay: Proof-of-Concept With 27 Reference Chemicals.Toxicological sciences : an official journal of the Society of Toxicology · 2019Article
- Identifying Compounds with Genotoxicity Potential Using Tox21 High-Throughput Screening Assays.Chemical research in toxicology · 2019Article
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Authors and funding
6 authors.
Funding
Abstract
The in vitro MultiFlow DNA Damage assay multiplexes p53, γH2AX, phospho-histone H3, and polyploidization biomarkers into 1 flow cytometric analysis (Bryce, S. M., Bernacki, D. T., Bemis, J. C., and Dertinger, S. D. (2016). Genotoxic mode of action predictions from a multiplexed flow cytometric assay and a machine learning approach. Environ. Mol. Mutagen. 57, 171-189). The work reported herein evaluated the generalizability of the method, as well as several data analytics strategies, to a range of chemical classes not studied previously. TK6 cells were exposed to each of 103 diverse chemicals, 86 of which were supplied by the National Toxicology Program (NTP) and selected based upon responses in genetic damage assays conducted under the Tox21 program. Exposures occurred for 24 h over a range of concentrations, and cell aliquots were removed at 4 and 24 h for analysis. Multiplexed response data were evaluated using 3 machine learning models designed to predict genotoxic mode of action based on data from a training set of 85 previously studied chemicals. Of 54 chemicals with sufficient information to make an a priori call on genotoxic potential, the prediction models' accuracies were 79.6% (random forest), 88.9% (logistic regression), and 90.7% (artificial neural network). A majority vote ensemble of the 3 models provided 92.6% accuracy. Forty-nine NTP chemicals were not adequately tested (maximum concentration did not approach assay's cytotoxicity limit) and/or had insufficient conventional genotoxicity data to allow their genotoxic potential to be defined. For these chemicals MultiFlow data will be useful in future research and hypothesis testing. Collectively, the results suggest the MultiFlow assay and associated data analysis strategies are broadly generalizable, demonstrating high predictability when applied to new chemicals and classes of compounds.
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