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Benchmark Dose Modeling of In Vitro Genotoxicity Data: a Reanalysis

  • Guo, Xiaoqing (Division of Genetic and Molecular Toxicology, National Center for Toxicological Research) ;
  • Mei, Nan (Division of Genetic and Molecular Toxicology, National Center for Toxicological Research)
  • Received : 2018.08.10
  • Accepted : 2018.08.30
  • Published : 2018.10.15

Abstract

The methods of applied genetic toxicology are changing from qualitative hazard identification to quantitative risk assessment. Recently, quantitative analysis with point of departure (PoD) metrics and benchmark dose (BMD) modeling have been applied to in vitro genotoxicity data. Two software packages are commonly used for BMD analysis. In previous studies, we performed quantitative dose-response analysis by using the PROAST software to quantitatively evaluate the mutagenicity of four piperidine nitroxides with various substituent groups on the 4-position of the piperidine ring and six cigarette whole smoke solutions (WSSs) prepared by bubbling machine-generated whole smoke. In the present study, we reanalyzed the obtained genotoxicity data by using the EPA's BMD software (BMDS) to evaluate the inter-platform quantitative agreement of the estimates of genotoxic potency. We calculated the BMDs for 10%, 50%, and 100% (i.e., a two-fold increase), and 200% increases over the concurrent vehicle controls to achieve better discrimination of the dose-responses, along with their BMDLs (the lower 95% confidence interval of the BMD) and BMDUs (the upper 95% confidence interval of the BMD). The BMD values and rankings estimated in this study by using the EPA's BMDS were reasonably similar to those calculated in our previous studies by using PROAST. These results indicated that both software packages were suitable for dose-response analysis using the mouse lymphoma assay and that the BMD modeling results from these software packages produced comparable rank orders of the mutagenic potency.

Keywords

References

  1. Crump, K.S. (1984) A new method for determining allow able daily intakes. Fundam. Appl. Toxicol., 4, 854-871. https://doi.org/10.1016/0272-0590(84)90107-6
  2. Cronin, M.T.D., Enoch, S.J., Mellor, C.L., Przybylak, K.R., Richarz, A.N. and Madden, J.C. (2017) In silico prediction of organ level toxicity: Linking chemistry to adverse effects. Toxicol. Res., 33, 173-182. https://doi.org/10.5487/TR.2017.33.3.173
  3. Brandon, E.F., Bulder, A.S., van Engelen, J.G., Mahieu, C.M., Mennes, W.C., Pronk, M.E., Rietveld, A.G., van de Ven, B.M., Ten Voorde, S.E., Wolterink, G., Slob, W., Zeilmaker, M.J. and Bessems, J.G. (2013) Does EU legislation allow the use of the Benchmark Dose (BMD) approach for risk assessment? Regul. Toxicol. Pharmacol., 67, 182-188.
  4. EPA (1994) Methods for derivation of inhalation reference concentrations and application of inhalation dosimetry. U.S. Environmental Protection Agency [Accessed on 2018 Aug 7]. Available from: https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=30001K30004C.PDF/.
  5. EPA (1999) What is benchmark dose software (BMDS)? U.S. Environmental Protection Agency [Accessed on 2018 Aug 7]. Available from: https://www.epa.gov/bmds/whatbenchmark-dose-software-bmds/.
  6. Barnes, D.G., Daston, G.P., Evans, J.S., Jarabek, A.M., Kavlock, R.J., Kimmel, C.A., Park, C. and Spitzer, H.L. (1995) Benchmark Dose Workshop: criteria for use of a benchmark dose to estimate a reference dose. Regul. Toxicol. Pharmacol., 21, 296-306. https://doi.org/10.1006/rtph.1995.1043
  7. Mattison, D.R. and Sandler, J.D. (1994) Summary of the workshop on issues in risk assessment: quantitative methods for developmental toxicology. Risk Anal., 14, 595-604. https://doi.org/10.1111/j.1539-6924.1994.tb00273.x
  8. Slikker, W., Jr., Crump, K.S., Andersen, M.E. and Bellinger, D. (1996) Biologically based, quantitative risk assessment of neurotoxicants. Fundam. Appl. Toxicol., 29, 18-30. https://doi.org/10.1006/faat.1996.0002
  9. Van Landingham, C.B., Allen, B.C., Shipp, A.M. and Crump, K.S. (2001) Comparison of the EU T25 single point estimate method with benchmark dose response modeling for estimating potency of carcinogens. Risk Anal., 21, 641-656. https://doi.org/10.1111/0272-4332.214141
  10. Burgoon, L.D. and Zacharewski, T.R. (2008) Automated quantitative dose-response modeling and point of departure determination for large toxicogenomic and high-throughput screening data sets. Toxicol. Sci., 104, 412-418. https://doi.org/10.1093/toxsci/kfn083
  11. Thomas, R.S., Clewell, H.J., 3rd, Allen, B.C., Wesselkamper, S.C., Wang, N.C., Lambert, J.C., Hess-Wilson, J.K., Zhao, Q.J. and Andersen, M.E. (2011) Application of transcriptional benchmark dose values in quantitative cancer and noncancer risk assessment. Toxicol. Sci., 120, 194-205. https://doi.org/10.1093/toxsci/kfq355
  12. EPA (2005) Guidelines for carcinogen risk assessment. U.S. Environmental Protection Agency [Accessed on 2018 Aug 7]. Available from: http://www2.epa.gov/sites/production/files/2013-2009/documents/cancer_guidelines_final_2013-2025-2005.pdf/.
  13. Guo, X., Heflich, R.H., Dial, S.L., Richter, P.A., Moore, M.M. and Mei, N. (2016) Quantitative analysis of the relative mutagenicity of five chemical constituents of tobacco smoke in the mouse lymphoma assay. Mutagenesis, 31, 287-296. https://doi.org/10.1093/mutage/gev039
  14. Hernandez, L.G., Slob, W., van Steeg, H. and van Benthem, J. (2011) Can carcinogenic potency be predicted from in vivo genotoxicity data? a meta-analysis of historical data. Environ. Mol. Mutagen., 52, 518-528. https://doi.org/10.1002/em.20651
  15. Cao, X., Mittelstaedt, R.A., Pearce, M.G., Allen, B.C., Soeteman-Hernandez, L.G., Johnson, G.E., Bigger, C.A. and Heflich, R.H. (2014) Quantitative dose-response analysis of ethyl methanesulfonate genotoxicity in adult gpt-delta transgenic mice. Environ. Mol. Mutagen., 55, 385-399. https://doi.org/10.1002/em.21854
  16. Paini, A., Scholz, G., Marin-Kuan, M., Schilter, B., O’Brien, J., van Bladeren, P.J. and Rietjens, I.M. (2011) Quantitative comparison between in vivo DNA adduct formation from exposure to selected DNA-reactive carcinogens, natural background levels of DNA adduct formation and tumour incidence in rodent bioassays. Mutagenesis, 26, 605-618. https://doi.org/10.1093/mutage/ger022
  17. Gollapudi, B.B., Johnson, G.E., Hernandez, L.G., Pottenger, L.H., Dearfield, K.L., Jeffrey, A.M., Julien, E., Kim, J.H., Lovell, D.P., Macgregor, J.T., Moore, M.M., van Benthem, J., White, P.A., Zeiger, E. and Thybaud, V. (2013) Quantitative approaches for assessing dose-response relationships in genetic toxicology studies. Environ. Mol. Mutagen., 54, 8-18. https://doi.org/10.1002/em.21727
  18. Johnson, G.E., Soeteman-Hernandez, L.G., Gollapudi, B.B., Bodger, O.G., Dearfield, K.L., Heflich, R.H., Hixon, J.G., Lovell, D.P., MacGregor, J.T., Pottenger, L.H., Thompson, C.M., Abraham, L., Thybaud, V., Tanir, J.Y., Zeiger, E., van Benthem, J. and White, P.A. (2014) Derivation of point of departure (PoD) estimates in genetic toxicology studies and their potential applications in risk assessment. Environ. Mol. Mutagen., 55, 609-623. https://doi.org/10.1002/em.21870
  19. MacGregor, J.T., Frotschl, R., White, P.A., Crump, K.S., Eastmond, D.A., Fukushima, S., Guerard, M., Hayashi, M., Soeteman-Hernandez, L.G., Kasamatsu, T., Levy, D.D., Morita, T., Muller, L., Schoeny, R., Schuler, M.J., Thybaud, V. and Johnson, G.E. (2015) IWGT report on quantitative approaches to genotoxicity risk assessment I. Methods and metrics for defining exposure-response relationships and points of departure (PoDs). Mutat. Res. Genet. Toxicol. Environ. Mutagen., 783, 55-65. https://doi.org/10.1016/j.mrgentox.2014.09.011
  20. MacGregor, J.T., Frotschl, R., White, P.A., Crump, K.S., Eastmond, D.A., Fukushima, S., Guerard, M., Hayashi, M., Soeteman-Hernandez, L.G., Johnson, G.E., Kasamatsu, T., Levy, D.D., Morita, T., Muller, L., Schoeny, R., Schuler, M.J. and Thybaud, V. (2015) IWGT report on quantitative approaches to genotoxicity risk assessment II. Use of pointof-departure (PoD) metrics in defining acceptable exposure limits and assessing human risk. Mutat. Res. Genet. Toxicol. Environ. Mutagen., 783, 66-78. https://doi.org/10.1016/j.mrgentox.2014.10.008
  21. White, P.A. and Johnson, G.E. (2016) Genetic toxicology at the crossroads-from qualitative hazard evaluation to quantitative risk assessment. Mutagenesis, 31, 233-237. https://doi.org/10.1093/mutage/gew011
  22. Guo, X., Seo, J.E., Bryce, S.M., Tan, J.A., Wu, Q., Dial, S.L., Moore, M.M. and Mei, N. (2018) Comparative genotoxicity of TEMPO and 3 of its derivatives in mouse lymphoma cells. Toxicol. Sci., 163, 214-225. https://doi.org/10.1093/toxsci/kfy022
  23. Guo, X., Heflich, R.H., Dial, S.L., De, M., Richter, P.A. and Mei, N. (2018) Quantitative differentiation of whole smoke solution-induced mutagenicity in the mouse lymphoma assay. Environ. Mol. Mutagen., 59, 103-113. https://doi.org/10.1002/em.22151
  24. RIVM (2013) PROAST. The Dutch National Institute for Public Health and the Environment [Accessed on 2018 Aug 7]. Available from: http://www.rivm.nl/en/Documents_and_-publications/Scientific/Models/PROAST/.
  25. EPA (2012) Benchmark dose technical guidance. U.S. Environmental Protection Agency [Accessed on 2018 Aug 7]. Available from: https://www.epa.gov/risk/benchmark-dosetechnical-guidance/.
  26. Davis, J.A., Gift, J.S. and Zhao, Q.J. (2011) Introduction to benchmark dose methods and U.S. EPA's benchmark dose software (BMDS) version 2.1.1. Toxicol. Appl. Pharmacol., 254, 181-191. https://doi.org/10.1016/j.taap.2010.10.016
  27. Moore, M.M., Honma, M., Clements, J., Bolcsfoldi, G., Burlinson, B., Cifone, M., Clarke, J., Delongchamp, R., Durward, R., Fellows, M., Gollapudi, B., Hou, S., Jenkinson, P., Lloyd, M., Majeska, J., Myhr, B., O'Donovan, M., Omori, T., Riach, C., San, R., Stankowski, L.F., Jr., Thakur, A.K., Van Goethem, F., Wakuri, S. and Yoshimura, I. (2006) Mouse lymphoma thymidine kinase gene mutation assay: follow-up meeting of the International Workshop on Genotoxicity Testing--Aberdeen, Scotland, 2003--Assay acceptance criteria, positive controls, and data evaluation. Environ. Mol. Mutagen., 47, 1-5. https://doi.org/10.1002/em.20159
  28. Guo, X., Chen, S., Zhang, Z., Dobrovolsky, V.N., Dial, S.L., Guo, L. and Mei, N. (2015) Reactive oxygen species and c-Jun N-terminal kinases contribute to TEMPO-induced apoptosis in L5178Y cells. Chem. Biol. Interact., 235, 27-36. https://doi.org/10.1016/j.cbi.2015.04.009
  29. Guo, X., Mittelstaedt, R.A., Guo, L., Shaddock, J.G., Heflich, R.H., Bigger, A.H., Moore, M.M. and Mei, N. (2013) Nitroxide TEMPO: a genotoxic and oxidative stress inducer in cultured cells. Toxicol. In Vitro, 27, 1496-1502. https://doi.org/10.1016/j.tiv.2013.02.019
  30. EFSA (2017) Update: use of the benchmark dose approach in risk assessment. The EFSA Journal, 15, 4658.
  31. Wills, J.W., Johnson, G.E., Doak, S.H., Soeteman-Hernandez, L.G., Slob, W. and White, P.A. (2016) Empirical analysis of BMD metrics in genetic toxicology part I: in vitro analyses to provide robust potency rankings and support MOA determinations. Mutagenesis, 31, 255-263. https://doi.org/10.1093/mutage/gev085
  32. PHE (2014) Carcinogenic dose response: defining a point of departure and potency estimates. Public Health England [Accessed on 2018 Jul 5]. Available from: https://www.gov.uk/government/publications/carcinogenic-dose-responsedefining-a-point-of-departure-and-potency-estimates/.
  33. EFSA (2009) Guidance of the Scientific Committee on a request from EFSA on the use of the benchmark dose approach in risk assessment The EFSA Journal, 1150, 1-72.