Predictions of genotoxic potential, mode of action, molecular targets, and potency via a tiered multiflow® assay data analysis strategy.

The in vitro MultiFlow® DNA Damage Assay multiplexes γH2AX, p53, phospho-histone H3, and polyploidization biomarkers into a single flow cytometric analysis. The current report describes a tiered sequential data analysis strategy based on data generated from exposure of human TK6 cells to a previously described 85 chemical training set and a new pharmaceutical-centric test set (n = 40). In each case, exposure was continuous over a range of closely spaced concentrations, and cell aliquots were removed for analysis following 4 and 24 hr of treatment. The first data analysis step focused on chemicals' genotoxic potential, and for this purpose, we evaluated the performance of a machine learning (ML) ensemble, a rubric that considered fold increases in biomarkers against global evaluation factors (GEFs), and a hybrid strategy that considered ML and GEFs. This first tier further used ML output and/or GEFs to classify genotoxic activity as clastogenic and/or aneugenic. Test set results demonstrated the generalizability of the first tier, with particularly good performance from the ML ensemble: 35/40 (88%) concordance with a priori genotoxicity expectations and 21/24 (88%) agreement with expected mode of action (MoA). A second tier applied unsupervised hierarchical clustering to the biomarker response data, and these analyses were found to group certain chemicals, especially aneugens, according to their molecular targets. Finally, a third tier utilized benchmark dose analyses and MultiFlow biomarker responses to rank genotoxic potency. The relevance of these rankings is supported by the strong agreement found between benchmark dose values derived from MultiFlow biomarkers compared to those generated from parallel in vitro micronucleus analyses. Collectively, the results suggest that a tiered MultiFlow data analysis pipeline is capable of rapidly and effectively identifying genotoxic hazards while providing additional information that is useful for modern risk assessments-MoA, molecular targets, and potency. Environ. Mol. Mutagen. 60:513-533, 2019. © 2019 Wiley Periodicals, Inc.

Dertinger SD ，Kraynak AR ，Wheeldon RP ，Bernacki DT ，Bryce SM ，Hall N ，Bemis JC ，Galloway SM ，Escobar PA ，Johnson GE ... -  《-》

Interlaboratory evaluation of a multiplexed high information content in vitro genotoxicity assay.

We previously described a multiplexed in vitro genotoxicity assay based on flow cytometric analysis of detergent-liberated nuclei that are simultaneously stained with propidium iodide and labeled with fluorescent antibodies against p53, γH2AX, and phospho-histone H3. Inclusion of a known number of microspheres provides absolute nuclei counts. The work described herein was undertaken to evaluate the interlaboratory transferability of this assay, commercially known as MultiFlow® DNA Damage Kit-p53, γH2AX, Phospho-Histone H3. For these experiments, seven laboratories studied reference chemicals from a group of 84 representing clastogens, aneugens, and nongenotoxicants. TK6 cells were exposed to chemicals in 96-well plates over a range of concentrations for 24 hr. At 4 and 24 hr, cell aliquots were added to the MultiFlow reagent mix and following a brief incubation period flow cytometric analysis occurred, in most cases directly from a 96-well plate via a robotic walk-away data acquisition system. Multiplexed response data were evaluated using two analysis approaches, one based on global evaluation factors (i.e., cutoff values derived from all interlaboratory data), and a second based on multinomial logistic regression that considers multiple biomarkers simultaneously. Both data analysis strategies were devised to categorize chemicals as predominately exhibiting a clastogenic, aneugenic, or nongenotoxic mode of action (MoA). Based on the aggregate 231 experiments that were performed, assay sensitivity, specificity, and concordance in relation to a priori MoA grouping were ≥ 92%. These results are encouraging as they suggest that two distinct data analysis strategies can rapidly and reliably predict new chemicals' predominant genotoxic MoA based on data from an efficient and transferable multiplexed in vitro assay. Environ. Mol. Mutagen. 58:146-161, 2017. © 2017 Wiley Periodicals, Inc.

Bryce SM ，Bernacki DT ，Bemis JC ，Spellman RA ，Engel ME ，Schuler M ，Lorge E ，Heikkinen PT ，Hemmann U ，Thybaud V ，Wilde S ，Queisser N ，Sutter A ，Zeller A ，Guérard M ，Kirkland D ，Dertinger SD ... -  《-》

Genotoxic mode of action predictions from a multiplexed flow cytometric assay and a machine learning approach.

Several endpoints associated with cellular responses to DNA damage as well as overt cytotoxicity were multiplexed into a miniaturized, "add and read" type flow cytometric assay. Reagents included a detergent to liberate nuclei, RNase and propidium iodide to serve as a pan-DNA dye, fluorescent antibodies against γH2AX, phospho-histone H3, and p53, and fluorescent microspheres for absolute nuclei counts. The assay was applied to TK6 cells and 67 diverse reference chemicals that served as a training set. Exposure was for 24 hrs in 96-well plates, and unless precipitation or foreknowledge about cytotoxicity suggested otherwise, the highest concentration was 1 mM. At 4- and 24-hrs aliquots were removed and added to microtiter plates containing the reagent mix. Following a brief incubation period robotic sampling facilitated walk-away data acquisition. Univariate analyses identified biomarkers and time points that were valuable for classifying agents into one of three groups: clastogenic, aneugenic, or non-genotoxic. These mode of action predictions were optimized with a forward-stepping process that considered Wald test p-values, receiver operator characteristic curves, and pseudo R(2) values, among others. A particularly high performing multinomial logistic regression model was comprised of four factors: 4 hr γH2AX and phospho-histone H3 values, and 24 hr p53 and polyploidy values. For the training set chemicals, the four-factor model resulted in 94% concordance with our a priori classifications. Cross validation occurred via a leave-one-out approach, and in this case 91% concordance was observed. A test set of 17 chemicals that were not used to construct the model were evaluated, some of which utilized a short-term treatment in the presence of a metabolic activation system, and in 16 cases mode of action was correctly predicted. These initial results are encouraging as they suggest a machine learning strategy can be used to rapidly and reliably predict new chemicals' genotoxic mode of action based on data from an efficient and highly scalable multiplexed assay.

Bryce SM ，Bernacki DT ，Bemis JC ，Dertinger SD ... -  《-》

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

Bryce SM ，Bernacki DT ，Smith-Roe SL ，Witt KL ，Bemis JC ，Dertinger SD ... -  《-》

Classification of in vitro genotoxicants using a novel multiplexed biomarker assay compared to the flow cytometric micronucleus test.

Regulatory in vitro genotoxicity testing exhibits shortcomings in specificity and mode of action (MoA) information. Thus, the aim of this work was to evaluate the performance of the novel MultiFlow® assay composed of mechanistic biomarkers quantified in TK6 cells after treatment (4 and 24 hr): γH2AX (DNA double strand breaks), phosphorylated H3 (mitotic cells), translocated p53 (genotoxicity), and cleaved PARP1 (apoptosis). A reference dataset of 31 compounds with well-established MoA was studied using the MicroFlow® micronucleus assay. A positive call was raised following the earlier published criteria from Litron Laboratories. In the light of our data, these evaluation criteria should probably be adjusted since only 8/11 (73%) nongenotoxicants and 18/20 (90%) genotoxicants were correctly identified. Moreover, there is a need for new in vitro tools to delineate the predominant MoA as in the MicroFlow® assay only 5/9 (56%) aneugens and 4/11 (36%) clastogens were correctly classified. In contrast, the MultiFlow® assay provides more in-depth information about the MoA and therefore reliably discriminates clastogens, aneugens, and nongenotoxicants. By using a lab-specific, practical threshold for the aforementioned biomarkers, 10/11 (91%) nongenotoxicants and 19/20 genotoxicants (95%), 9/11 (82%) clastogens, and 8/9 (89%) aneugens were correctly categorized, suggesting a clear improvement over the MicroFlow® . Furthermore, the MultiFlow markers were benchmarked against established methods to assess the validity of the data. Altogether, these findings demonstrated good agreement between the MultiFlow® assay and the benchmarking methods. Finally, p21 may improve class discrimination given the correct identification of 4/4 (100%) aneugens and 2/5 (40%) clastogens. Environ. Mol. Mutagen. 58:662-677, 2017. © 2017 Wiley Periodicals, Inc.

Wilde S ，Dambowsky M ，Hempt C ，Sutter A ，Queisser N ... -  《-》