Application of biological modifiers to a multiplexed, human cell-based DNA damage assay provides mechanistic information on genotoxicity and molecular targets

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Data may be preliminary. 18 February 2025 V1 Latest version Share on Application of biological modifiers to a multiplexed, human cell-based DNA damage assay provides mechanistic information on genotoxicity and molecular targets Authors : Steven Bryce , Svetlana Avlasevich , Adam Conrad , Nikki Hall , Kyle Tichenor , Erica Briggs , Stephen Dertinger , and Jeffrey Bemis 0000-0002-3328-1668 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.173989664.44387167/v1 Published Environmental and Molecular Mutagenesis Version of record Peer review timeline 335 views 272 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This laboratory has reported that the combined use of MicroFlow and MultiFlow assays provides information regarding chemicals’ genotoxic mode of action (MoA). In an effort to go beyond MoA assessments, we incorporated a panel of biological response modifiers that elicit specific effects on the assays’ biomarker response profiles. This was done to pursue our hypothesis that such perturbation signatures would reveal information on clastogenic mechanisms and molecular targets. For this proof-of-concept study, we exposed TK6 cells to 20 previously identified clastogens. TK6 cells in 96-well plates in the presence and absence of each of four modifying agents at one optimized concentration: talazoparib (PARP inhibitor), MK-8776 (CHK1 inhibitor), AZD-7648 (DNA-PK inhibitor), or a cocktail of reactive oxygen species scavengers. In parallel, cells were also exposed to each of the test chemicals for 4 hr, at which time cells were washed and allowed to recover for an additional 20 hours. For each of these treatment conditions, sample processing and flow cytometric analyses were performed using standard MicroFlow and MultiFlow procedures to measure micronuclei, γH2AX, phosphohistone-H3 (p-H3), p53 activation, and relative nuclei counts. The resulting biomarker response data were processed with PROAST benchmark dose (BMD) software, with modifying agent as a covariate. Unsupervised hierarchical clustering of the collective potency metrics for various combinations of biomarkers showed that clastogens with similar genotoxic mechanisms grouped together. Overall, this study shows that in combination with biological response modifiers, MultiFlow and MicroFlow biomarkers can provide mechanistic insights into chemical-induced genotoxicity. Introduction: In vitro micronucleus (MN) scoring via flow cytometry is an established approach that is acknowledged in regulatory test guidelines (OECD, 2023). In particular, the In Vitro MicroFlow methodology is characterized by rapid and efficient processing and analysis that has been successfully adapted to multiple formats, including 96 well plates. More recently, a complimentary DNA damage assay was developed in our laboratory to serve as an efficient screening tool for genotoxicity mode of action (MoA) assessment (Bryce et al, 2018; Bryce et al 2016). The MultiFlow assay is characterized by an add-and-read process that liberates cell nuclei for rapid labeling of specific nuclear epitopes and subsequent analysis via flow cytometry. The MultiFlow suite of biomarkers permits one to categorize compounds’ predominate genotoxic mode of action (MoA) as being clastogenic or aneugenic. When these two assays are conducted in parallel, they can provide a wealth of information to aid in the interpretation of potential genotoxicity. We have previously reported on adaptations of the MicroFlow and MultiFlow assays that provide further insight into clastogenic (Bryce et al., 2021) or aneugenic mechanism(s) (Hall et al, 2022). When an aneugenic MoA is identified with the base assay, application of the Aneugen Molecular Mechanism (AMM) modification can determine whether a test compound is a tubulin stabilizer, a tubulin destabilizer or an Aurora Kinase B inhibitor – a subclass of mitotic protein kinase inhibitors known for their aneugenic activity (Hall et al., 2022, Bernacki et al, 2019). We also demonstrated in a recent publication that clastogens can be categorized as being alkylators/crosslinkers or not based on their ability to maintain DNA damage responses such as elevated γH2AX following a washout protocol (Bryce et al, 2021). Both of these modifications to the MultiFlow methodology support the concept of a flexible, modular approach that allows one to gain deeper levels of insight into genotoxic activity of test compounds, as necessary. We report here on the latest advancements in this modular approach whereby both assays are run in parallel, make use of the above-mentioned washout protocol, and furthermore involve treating cells with clastogens in the presence and absence of several DNA damage response modifiers. This highly informative approach examines alterations in the response patterns of clastogenic biomarkers used as part of the base MultiFlow MoA assessment, e.g., γH2AX, phosphohistone H3, p53, etc., along with information provided by MN induction. The following breakdown will examine the various aspects of this study design in terms of their specific functions within the multiplexed system. The washout protocol employed in this study has been previously reported on (Bryce et al., 2021) in the context of investigating clastogenic mechanism. In brief, test article is removed after a short-term treatment via centrifugation/aspiration and is replaced with fresh media. Differences in the maintenance or reduction of certain biomarker responses can be used to obtain information on whether a clastogen shows characteristics of a DNA alkylator vs a non-alkylator type mechanism of action. Note that for the purposes of this paper, the term “alkylator” signifies compounds that form covalent bonds with DNA, including cross-linkers. The use of reactive oxygen species (ROS) scavengers to reveal the activity of ROS-inducing compounds is well established in the literature (Yuan et al., 2020; Andre and Villian, 2017; Halasi et al., 2013) and observation of an attenuation of response with the inclusion of such compounds is often used as an indicator of chemicals that cause oxidative stress. Thus n-acetyl cysteine (NAC), catalase, l-glutathione, sodium pyruvate and others have been reported to block the effects of ROS in numerous cell and animal-based systems. Given that we are developing a screening tool that is meant for rapid and efficient assessment of genotoxicity potential, we employed a cocktail of multiple scavengers in this current study. See the Materials and Methods section for more details. The three DNA damage/repair pathway inhibitors employed for this proof-of-concept study are talazoparib, MK-8776 and AZD-7648. These agents block the activity of Poly (ADP-Ribose) Polymerase (PARP) (Ronson et al., 2018), Checkpoint Kinase 1 (CHK-1) (Parsels et al., 2018) or DNA-Protein Kinase (DNA-PK) (Dong et al., 2018), respectively. Based on the known activities of these agents, certain DNA repair pathways, e.g. base excision repair (BER) and non-homologous end joining (NHEJ), involved in the genotoxic activity of the test compounds can be implicated in the presence of select inhibitors. All of these aforementioned modifying conditions are employed in this combined assay design and the data are analyzed via unsupervised hierarchical clustering techniques and other data visualizations to group like-acting clastogens and identify potential molecular mechanisms responsible for the clastogenic activity. Our current study describes the conduct of this study in TK6 cells with 20 compounds of varying clastogenic potencies and molecular targets. Materials and Methods Chemicals, Cells, Culture Conditions The identity and source of the 20 test compounds, as well as established molecular mechanisms responsible for their activity, are provided in Table I. TK6 cells were purchased from ATCC® (cat. no. CRL-8015). Cells were grown in a humidified atmosphere at 37°C with 5% CO 2 , and maintained at or below 1 x 10^6 cells/mL. The culture medium consisted of RPMI 1640 with 200 µg/mL sodium pyruvate (both from Sigma-Aldrich, St. Louis, MO), 200 µM L-glutamine, 50 units/mL penicillin and 50 µg/mL streptomycin (from Mediatech Inc., Manassas, VA), and 10% v/v heat-inactivated horse serum (Gibco®, a Thermo Fisher Scientific Company, Waltham, MA). MN and MoA assessment The initial determination of genotoxicity and MoA of the study compounds was performed using the standard In Vitro MicroFlow and MultiFlow assay as conducted in previous studies. Briefly, these methods are based on the lysis of cells and liberation of nuclei and micronuclei which are then labeled and directly analyzed via flow cytometry. These approaches have been described in numerous publications and, for the purposes of this investigation, exclusively reflects on the identification of chemicals with clastogenic properties. Please refer to Bryce et al (2007) and Bryce et al (2021) respectively for a detailed description of processes involved in the MN and MoA characterizations that set up for the current study. Specific to the analyses reported here, we conducted both assays in parallel using the same treated wells as the source for both methods. This served as the basis for confirming the genotoxic activity in these studies and then extending onto treatments that involved the use of modifying conditions. Biological Modifier Approach This methodology is built on the combination of MultiFlow and In Vitro MicroFlow assays as described previously (Bryce et al., 2016), with the inclusion of specific modifying conditions to selectively alter biomarker response patterns. The first of the modifying conditions we explored was to utilize the washout procedure that has been described previously (Bryce et al., 2021). Thus, cells were treated with test article and after 4 hours washed free of compound and allowed to recover for 20 additional hours. In parallel, a separate set of wells were treated with compound in the presence or absence of several other modifying conditions. Briefly, cells were exposed to one the following modifier conditions at final concentrations of: a) talazoparib at 0.1 µM, b) MK-8776 at 0.2 µM, c)AZD-7648 at 1.0 µM, or d) the aforementioned ROS scavenger cocktail n-acetylcysteine (0.5 mM), glutathione (0.5 mM), ebselen (0.1 µM), D-mannitol (10 mM) and sodium pyruvate (10 mM), all dissolved in DMSO or aqueous buffer not to exceed 0.1% (v/v) of the final treatment culture. The exposure conditions to these agents in the absence of test article did not elicit any responses in the associated biomarker(s) or impact cell health (data not shown). The response modifying agents were used to pre-treat TK6 cells in T25 flasks for 60 minutes at 37ºC prior to seeding the cells into 96 well plates and exposing to test compounds for 24 continuous hours. Compound-induced responses were studied at 4 and 24 hours of exposure across 7 concentrations in duplicates in a single experimental replicate with two solvent control wells. For certain compounds, independent replicate experiments were performed and are indicated by the compound name followed by the number two, for example, “MMS_2”. All compounds were prepared in DMSO stock solutions and the top concentrations were based on previous standard MultiFlow analyses. Taking into account the amount of DMSO that was used in the delivery of the modifying agent, the final DMSO concentration in the treatment culture was 1.1% (v/v). Sample processing for MicroFlow and MultiFlow was performed as described in the respective, kit-supplied manuals. Samples were analyzed on a Beckton Dickinson FACSCanto™ II flow cytometer using FACSDiva™ v8.0.3 software. All subsequent data processing and analyses were performed using FlowJo (v10.6.2) software. Data analysis workflow The data were first converted to fold-change from each treatment’s solvent control mean value before further processing. As reported previously (Dertinger et al., 2024), we calculated normalized area-under-the-curve (AUC) data for certain MultiFlow biomarker/treatment combinations that were useful in the identification of alkylating agents. These included γH2AX, p-H3 at 4 and 24 hours. Additionally, the dose response data for all of the MultiFlow biomarkers at both 4 and 24 hours, relative nuclei count (RNC) at 24 hours, and MicroFlow MN and ethidium monoazide bromide (EMA - a dye used for monitoring membrane integrity in MicroFlow) at 24 hours, were analyzed via PROAST 70.0 (Varewyck et al., 2017) to yield point of departure values, i.e., benchmark dose (BMD), with a critical effect size of 0.5. This was performed under the “model averaging” function that included 50 bootstraps and generated the upper and lower limits of the BMD (BMDU and BMDL respectively). In certain circumstances, e.g. infinite upper bounds, BMDU/BMDL ratio ≥ 100, BMD higher than highest valid concentration tested, the BMD result was recoded to the top valid concentration of compound tested. It should be noted that for all of these BMD assessments, we avoided the use of covariates when the variables were considered dependent, such as timepoints or biomarkers measured in the same wells. However, where used appropriately, e.g. effect of presence or absence of inhibitor, covariates provide for more efficient and robust comparisons to be made. Once this initial data-reduction approach was performed, the BMD point estimate values from the Exponential models for each biomarker/timepoint were also normalized by creating a ratio between the value obtained from the control without modifier response divided by the matching response with the modifier. Note that the opposite was applied with the ROS scavenger since the direction of the modified effect was a reduction in activity. This is referred to as the “modulation factor” and enabled more direct comparisons across the various studies by allowing the results to be organized to show specific patterns of response and facilitating the use of an unsupervised hierarchical clustering technique. This machine learning approach was conducted in JMP Pro software (v18) and was used to group compounds according to the similarity of their pattern of responses across the multiple conditions. In this way, groups of like-performing compounds were identified. In Vitro Micronucleus and In Vitro MultiFlow – Standard Conduct In Vitro MicroFlow data were evaluated via standard approaches as defined in the OECD 487 test guideline (OECD, 2023). Thus, tests for dose-related trend and Dunnett’s pairwise comparisons confirmed positive MN responses within the 55 +/- 5% cytotoxicity limits for all the compounds. BMD values were then determined and used to rank the compounds in terms of potency for eliciting chromosome damage – see Figure 1. Supplementary Figure 1 shows the correlation between the MN responses and the p53 MultiFlow biomarker. Similar to above, the results from the MultiFlow biomarkers confirmed the clastogenic MoA for all the previously tested compounds (Bryce et al., 2021). Potassium bromate was the only compound we have not examined before and the responses were consistent with a clastogenic MoA (Data not shown). DNA Double Strand Break (DSB) Persistence Figure 2 shows the hierarchical cluster of the γH2AX and p-H3 values before and after washout. The ratio between the 4 and 24 hour γH2AX BMD values was also included here. Two groups were identified that separated compounds known to be direct, DNA reactive clastogens from those that are traditionally associated with indirect-acting mechanisms. Identification of ROS Contribution and DNA Repair Pathway in Clastogenicity The bulk of the figures in this section are structured similarly in an effort to efficiently show the patterns of response for the various combinations of biomarkers, timepoints and modifying agents. Furthermore, we have organized the compounds by the a priori groupings that are based on the mechanisms shown in Table 1. Thus, Figure 3 includes the small/complex alkylators separated into the individual panels (replicate studies of the same compound were paired together) with a specific modulator featured in each sub-panel and the biomarkers/timepoints organized on the X-axis. The biomarker response data show robust potentiation with the combination of test compound with talazoparib, and relatively little activity with the other modulators. Figure 4 includes the ROS generators and shows modulation of the biomarkers responses by the ROS scavenger, as well as some scattered responses is the talazoparib and AZD-7648 sub-panels. Figure 5 shows the topoisomerase I inhibitors and reveals biomarker activity predominantly for the combination with talazoparib. Figure 6 features the topoisomerase II inhibitors and strong biomarker responses are observed in the AZD-7648 subpanel. Figure 7 shows the DNA synthesis inhibitors and the predominant activity of the biomarkers with the combination of test compound with MK-8776. Finally, Figure 8 includes the crosslinkers and highlights a general lack of activity across the various biomarker/modulator arms. Figure 9 depicts the final collective output from the hierarchical clustering analysis that includes the same biomarkers and modulators shown in Figures 3-8. As this is an unsupervised approach, the organization of the compounds in the clustering was based on the similarity of the patterns of response in the biomarkers as interpreted by the machine learning algorithm. In this case we can identify several distinct groups of compounds that share similar types of clastogenic activity. For example, hydrogen peroxide and menadione, know ROS inducers, clustered together, while camptothecin, SN38 and topotecan, established topoisomerase I inhibitors, were in a separate group. It is worth noting that the independent replicate experiments of select compounds all clustered along with the original example of each compound. Discussion and Conclusions Understanding mechanisms of genotoxicity of test compounds, even at the in vitro level, is proving to be of great utility in risk assessment compared to historical approaches that simply provide binary yes/no calls (Menz et al., 2023). The MultiFlow assay was originally developed as a means to obtain genotoxic mode of action (MoA) information (Bryce et al., 2018; Bryce et al., 2016), and has been exceptionally useful in the interpretation of micronucleus data (Avlasevich et al., 2021). Here we are extending that coverage into deeper levels of information including mechanisms of DNA damage and potential target molecules/pathways. Based on the patterns of response and characterization of the data via our unsupervised approaches, we identified the predominant clastogenic mechanisms elicited by the test compounds. This does not mean that other pathways do not also contribute to their genotoxic profile, only that the ones we identified had the greatest effect in our system. In the context of the present work, along with the more standard executions of the MicroFlow and MultiFlow assays, we envision different tiers of assessment that lead to the categorization of clastogenic compounds based on their respective patterns of response in selected biomarkers. Initially one is able to establish the presence or absence of genotoxicity via MN induction and when followed up by inclusion of MultiFlow biomarkers, MoA information is obtained. Progressing further along the tiers, one can focus on the MoA categorization and go deeper into specific mechanisms or targets. Having these supplementary data on known DNA damage markers shows high utility in demonstrating true genotoxicity in vitro . Starting with the initial assessment provided by the standard MicroFlow and MultiFlow assays, we confirmed the genotoxicity of the test compounds and their associated MoA. By design, these were all clastogens, but this highlights the utility of each methodology to provide basic information on chromosome damaging potential and an initial MoA classification. The evaluation of the relationship between MN response and the p53 biomarker (Supplementary Figure 1) demonstrates the utility of indicator assays in comparison to the more established endpoint readout provided by the MicroFlow assay. The next tier examined in this study related to the response of clastogens following a washout at 4 hours followed by a 20 hr recovery. This approach has been reported previously (Bryce et al., 2021) and shown to differentiate compounds with characteristics such as DNA alkylation and/or cross-linking from those that do not induce such effects. Unlike the earlier report, the analysis performed here was based not only on AUC values but also included the comparison of BMD ratio values obtained before and after washout. This was applied to both γH2AX and p-H3 biomarkers, with compounds showing a maintained or further elevated γH2AX/reduced p-H3 after washout as being consistent with classification of alkylation as the mechanism for clastogenicity. The hierarchical clustering provided in Figure 2 showed the compounds that were found to exhibit these patterns, grouped separately from compounds without this specific mechanism of DNA interaction. This provides the first level of mechanistic categorization within the broad class of clastogens. The next level of analysis investigated the effects of ROS scavengers and DNA repair inhibitors on the response patterns of clastogenicity. Note that the ROS scavenger elicits reductions in the responses of clastogenic biomarkers compared to exposures without the cocktail. Figure 4 highlighted this feature in the context of clastogens known to operate via oxidative processes, i.e. hydrogen peroxide, menadione, bleomycin and potassium bromate. The remaining assessments involved the combination of the test compound with specific DNA repair inhibitors in order to go beyond MoA and elucidate mechanism(s) of clastogenic activity. Thus, talazoparib was added to block PARP activity that has been associated with numerous DNA repair processes, e.g. BER (Ronson et al., 2018). MK-8776 blocks CHK-1 activity which participates in DNA synthesis (Parsels et al., 2018) and AZD-7648 inhibits DNA-protein kinase (DNA-PK)(Dong et al., 2018). All of these repair pathway modifiers can have a potentiation-type of effect on the biomarker responses when exposed in combination with the test compounds, depending on the type of lesion formed and the pathway commonly recruited to repair it. This facilitates a rather simple, visually-guided examination of the response patterns of the various biomarkers that supports the organization of the compounds into similarly behaving groups. In the case of the alkylating agents shown in Figure 3, there is an obvious potentiation of 4 hr γH2AX and MN, EMA and RNC at 24 hours when the alkylator is combined with talazoparib. A similar effect can be seen in Figure 5 with the topoisomerase I inhibitors, however when judged in the context of the categorization provided by the DNA DSB persistence assessment, there is evidence to clearly separate these two classes of clastogens. The topoisomerase II inhibitors, Figure 6 shows a very distinct pattern of activation of clastogenic biomarkers in combination with AZD-7648 and minimal activity elsewhere. The group of DNA synthesis inhibitors shown in Figure 7 exhibited the most potentiation of responses when the compound was combined with MK-8776. The exception is stavudine, which showed very little activity for any biomarker/modulator combination other than for MN and EMA with the ROS scavenger. The last group that included the two cross-linking alkylators, chlorambucil and thiotepa (Figure 8), showed minimal consistent activity across any of the modulator arms and thus was grouped separately. However, similar to the other group of alkylators, conclusions regarding clastogenic mechanism need to be made with the context that these agents were clearly distinguished in the previous washout tier that examined direct DNA reactivity. This highlights the value of the stepwise approach employed here and reinforces the utility of this assay platform to provide in-depth information about genotoxic mechanism. The application of the hierarchical clustering shown in Figure 9, provided a slightly different arrangement of the test compounds, but showed good performance in categorizing clastogens with regard to their mechanism(s) of action and has the benefit of being a more objective, unsupervised analytical approach. The alkylating agents, including cross-linkers, grouped together, with the exception of MMS which can be explained by the exceptionally strong response this agent induced for the MN, EMA and RNC biomarkers. Topoisomerase I inhibitors, topoisomerase II inhibitors, DNA synthesis inhibitors and pure ROS inducers - menadione and hydrogen peroxide - were all clearly separated. Genistein and methotrexate occupied a group of their own, while one would likely predict that they would be classified with topoisomerase I and DNA synthesis inhibitors respectively. Bleomycin and potassium bromate, initially classified as ROS inducers, were included in the topoisomerase I group, but these compounds have been reported to elicit effects on DNA via other mechanisms such as intercalation and direct DNA reactivity (Goodwin et al., 2023; Luan et al., 2007) While these studies are compelling, there are clearly limits to the experimental design employed here and indeed, not all of the test compounds behaved as one might expect based on their established mechanism(s). There are several explanations for this outcome. Perhaps foremost, is the acknowledgment that we only covered a few of the known DNA repair pathways and thus investigation of additional inhibitors that target other repair pathways are planned. The BMD metrics could benefit from a larger range of concentrations, ideally ten or more, and this could result in better dose modeling and improvements to the compound characterization. Finally, the number of compounds studied was limited for this proof-of-concept effort and future work will expand on the representative activities and example compounds. This will ideally also enable more sophisticated supervised approaches to be applied and facilitate additional data visualization techniques such as we have reported on before (Dertinger et al., 2024). Finally, given the specificity of the DNA repair inhibitors that we employed here, there is the potential to contextualize the mechanisms and activity of the potential repair pathways initiated by their actions. For example, PARP inhibitors such as talazoparib are known to inhibit the BER pathway (Chen, 2011). This pathway is implicated in the repair of DNA double strand breaks, lesions know to be induced by alkylating agents as well as topoisomerase I inhibitors (Fujii et al., 2022; Huang et al., 2017). This supports the clustering of these agents as shown in Figures 3 and 5. Similarly, the topoisomerase II inhibitors clustered together (Figures 5 and 9) and their associated damage is known to be repaired through NHEJ (Morimoto et al., 2019). This additional level of understanding relative to the specific targeting of DNA damage and the linked repair pathways can serve to better support the use of these assays in next generation hazard identification and risk assessment applications. To conclude, the combination of In Vitro MicroFlow for MN assessment and MultiFlow for multiplexed DNA damage biomarkers provides a wealth of information for comprehensive compound characterization. We expanded upon standard MoA assessment with the inclusion of several response-modifying conditions that gave further insight into the genotoxic mechanisms of select clastogens. This approach complements and extends the suite of assays developed by Litron that employ the process of lysing cells and using flow cytometry to examine nuclear events with specific biomarkers. The ultimate utility of this new approach methodology is likely best judged in the context of the breadth of information that can be obtained by the application of these methods. They can provide a level of detail that is of significant benefit to activities such as early screening, compound prioritization, follow up from regulatory tests and even risk assessment using an adverse outcome pathway approach. Acknowledgements: Funding from NIEHS SBIR Grants #R43ES035550 and #R44ES033138 contributed in part to the studies described in this manuscript. Conflicts of Interest: All authors at the time of submission were full-time employees of Litron Laboratories, a company that develops and markets kits and services such as the In Vitro MicroFlow and MultiFlow products reported on here. Author Contributions: JCB, SDD & SMB designed the experiments; SMB, SLA, AC, NEH, KT, & EB conducted the benchtop work; JCB provided the rough draft and all co-authors contributed to the final version. 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Luan Y, Suzuki T, Palanisamy R, Takashima Y, Sakamoto H, Sakuraba M, Koizumi T, Mika Saito, Matsufuji H, Yamagata K, Yamaguchi T, Hayashi M, Honma M. 2007). Potassium bromate treatment predominantly causes large deletions, but not GC>TA transversion in human cells. Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis, 619, 113-123. Figure Legends Figure 1 – Potency ranking of Benchmark Dose (BMD) values for 20 clastogens. BMD values were Log10 transformed and arranged with the most potent, i.e., lowest BMD, compound to the left. BMD upper (BMDU) and lower (BMDL) values are also shown. Figure 2 – Unsupervised hierarchical clustering with dendrogram and heatmap for 20 clastogens based on biomarker responses associated with DNA double strand break persistence. In addition to the γH2AX and p-H3 area under the curve values, the ratio between the 4 and 24 hour γH2AX BMD values was also included. The upper Group 1 compounds are associated with non-direct DNA reactive mechanisms, while Group 2 compounds are considered to be directly DNA reactive. Figure 3 – Histogram plots describing the activity of various biomarkers following exposure to ethyl methanesulfonate (EMS), ethylnitrosourea (ENU), methyl methanesulfonate (MMS) or temozolomide in the presence of specific biological modifiers. The modulation factor on the Y axis shows the ratio of the Benchmark Dose values obtained from the condition of test compound without modifier to compound with the modifier (reactive oxygen species (ROS) is opposite). Responses in these metrics reveal a pattern of activity common to the group of compounds shown, in this case small/complex alkylators. Figure 4 – Histogram plots describing the activity of various biomarkers following exposure to hydrogen peroxide (H2O2), menadione, potassium bromate (KBrO3) or bleomycin in the presence of specific biological modifiers. The modulation factor on the Y axis shows the ratio of the Benchmark Dose values obtained from the condition of test compound without modifier to compound with the modifier (reactive oxygen species (ROS) is opposite). Responses in these metrics reveal a pattern of activity common to the group of compounds shown, in this case reactive oxygen species generators. Figure 5 – Histogram plots describing the activity of various biomarkers following exposure to SN-38, topotecan or camptothecin in the presence of specific biological modifiers. The modulation factor on the Y axis shows the ratio of the Benchmark Dose values obtained from the condition of test compound without modifier to compound with the modifier (reactive oxygen species (ROS) is opposite). Responses in these metrics reveal a pattern of activity common to the group of compounds shown, in this case topoisomerase I inhibitors. Figure 6 – Histogram plots describing the activity of various biomarkers following exposure to etoposide, doxorubicin or genistein in the presence of specific biological modifiers. The modulation factor on the Y axis shows the ratio of the Benchmark Dose values obtained from the condition of test compound without modifier to compound with the modifier (reactive oxygen species (ROS) is opposite). Responses in these metrics reveal a pattern of activity common to the group of compounds shown, in this case topoisomerase II inhibitors. Figure 7 – Histogram plots describing the activity of various biomarkers following exposure to hydroxyurea, methotrexate, cytarabine, or stavudine in the presence of specific biological modifiers. The modulation factor on the Y axis shows the ratio of the Benchmark Dose values obtained from the condition of test compound without modifier to compound with the modifier (reactive oxygen species (ROS) is opposite). Responses in these metrics reveal a pattern of activity common to the group of compounds shown, in this case DNA synthesis inhibitors. Figure 8 – Histogram plots describing the activity of various biomarkers following exposure to chlorambucil or thiotepa in the presence of specific biological modifiers. The modulation factor on the Y axis shows the ratio of the Benchmark Dose values obtained from the condition of test compound without modifier to compound with the modifier (reactive oxygen species (ROS) is opposite). Responses in these metrics reveal a pattern of activity common to the group of compounds shown, in this case DNA crosslinkers. Figure 9 – Unsupervised hierarchical clustering with dendrogram and heatmap for 20 clastogens based on biomarker responses associated with several biological response modifiers. Multiple groups can be discerned from the patterns of responses including compounds with topoisomerase II type activity, topoisomerase I activity, ROS activity, alkylators and DNA synthesis inhibitors. Supplementary Figure 1 – Assessment of the correlation between micronucleus induction and p53 activation by 20 clastogens. This analysis provides support for the utility of specific indicator biomarkers in relation to their ability to report on chromosome damage that is comparable to an established endpoint biomarker such as micronuclei. Supplementary Material File (table 1 compound list 250217.xlsx) Download 10.57 KB Information & Authors Information Version history V1 Version 1 18 February 2025 Peer review timeline Published Environmental and Molecular Mutagenesis Version of Record 23 May 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection Environmental and Molecular Mutagenesis Keywords genotoxicity in vitro micronucleus mechanism of action mode of action new approach methodology Authors Affiliations Steven Bryce Litron Labs View all articles by this author Svetlana Avlasevich Litron Laboratories View all articles by this author Adam Conrad Litron Laboratories View all articles by this author Nikki Hall Litron Laboratories View all articles by this author Kyle Tichenor Litron Laboratories View all articles by this author Erica Briggs Litron Laboratories View all articles by this author Stephen Dertinger Litron Laboratories View all articles by this author Jeffrey Bemis 0000-0002-3328-1668 [email protected] Litron Laboratories View all articles by this author Metrics & Citations Metrics Article Usage 335 views 272 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Steven Bryce, Svetlana Avlasevich, Adam Conrad, et al. 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