دورية أكاديمية
أعرض في EDS

Investigating the Generalizability of the MultiFlow ® DNA Damage Assay and Several Companion Machine Learning Models With a Set of 103 Diverse Test Chemicals.

التفاصيل البيبلوغرافية
العنوان: Investigating the Generalizability of the MultiFlow ® DNA Damage Assay and Several Companion Machine Learning Models With a Set of 103 Diverse Test Chemicals.
المؤلفون: Bryce SM; Litron Laboratories, Rochester, New York., Bernacki DT; Litron Laboratories, Rochester, New York., Smith-Roe SL; Division of the National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina., Witt KL; Division of the National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina., Bemis JC; Litron Laboratories, Rochester, New York., Dertinger SD; Litron Laboratories, Rochester, New York.
المصدر: Toxicological sciences : an official journal of the Society of Toxicology [Toxicol Sci] 2018 Mar 01; Vol. 162 (1), pp. 146-166.
نوع المنشور: Journal Article; Research Support, N.I.H., Extramural
اللغة: English
بيانات الدورية: Publisher: Oxford University Press Country of Publication: United States NLM ID: 9805461 Publication Model: Print Cited Medium: Internet ISSN: 1096-0929 (Electronic) Linking ISSN: 10960929 NLM ISO Abbreviation: Toxicol Sci Subsets: MEDLINE
أسماء مطبوعة: Publication: 1999- : Cary, NC : Oxford University Press
Original Publication: Orlando, FL : Academic Press, c1998-
مواضيع طبية MeSH: DNA Damage* , Machine Learning*, Mutagenicity Tests/*methods , Mutagens/*chemistry , Mutagens/*toxicity, Biomarkers ; Cell Line, Tumor ; Flow Cytometry ; Histones/genetics ; Humans
مستخلص: 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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معلومات مُعتمدة: R44 ES024039 United States ES NIEHS NIH HHS; R44 ES029014 United States ES NIEHS NIH HHS
المشرفين على المادة: 0 (Biomarkers)
0 (Histones)
0 (Mutagens)
تواريخ الأحداث: Date Created: 20171107 Date Completed: 20190207 Latest Revision: 20230928
رمز التحديث: 20240628
مُعرف محوري في PubMed: PMC6059150
DOI: 10.1093/toxsci/kfx235
PMID: 29106658
قاعدة البيانات: MEDLINE
الوصف
تدمد:1096-0929
DOI:10.1093/toxsci/kfx235