771
Views
2
CrossRef citations to date
0
Altmetric
Original Articles

Benchmark dose modeling of transcriptional data: a systematic approach to identify best practices for study designs used in radiation research

ORCID Icon, , , , ORCID Icon, , ORCID Icon & ORCID Icon show all
Pages 1832-1844 | Received 06 Jan 2022, Accepted 06 Jul 2022, Published online: 22 Aug 2022

References

  • Ankley GT, Bennett RS, Erickson RJ, Hoff DJ, Hornung MW, Johnson RD, Mount DR, Nichols JW, Russom CL, Schmieder PK, et al. 2010. Adverse outcome pathways: a conceptual framework to support ecotoxicology research and risk assessment. Environ Toxicol Chem. 29(3):730–741.
  • Bhat VS, Hester SD, Nesnow S, Eastmond DA. 2013. Concordance of transcriptional and apical benchmark dose levels for conazole-induced liver effects in mice. Toxicol Sci. 136(1):205–215.
  • Cassman M. 2005. Barriers to progress in systems biology. Nature. 438(7071):1079.
  • Brockmeier EK, Hodges G, Hutchinson TH, Butler E, Hecker M, Tollefsen KE, Garcia-Reyero N, Kille P, Becker D, Chipman K, et al. 2017. The role of omics in the application of adverse outcome pathways for chemical risk assessment. Toxicol Sci. 158(2):252–262.
  • Chauhan V, Kuo B, McNamee JP, Wilkins RC, Yauk CL. 2016. Transcriptional benchmark dose modeling: exploring how advances in chemical risk assessment may be applied to the radiation field. Environ Mol Mutagen. 57(8):589–604.
  • Chauhan V, Rowan-Carroll A, Gagné R, Kuo B, Williams A, Yauk CL. 2019a. The use of in vitro transcriptional data to identify thresholds of effects in a human lens epithelial cell-line exposed to ionizing radiation. Int J Radiat Biol. 95(2):156–169.
  • Chauhan V, Said Z, Daka J, Sadi B, Bijlani D, Marchetti F, Beaton D, Gaw A, Li C, Burtt J, et al. 2019b. Is there a role for the adverse outcome pathway framework to support radiation protection? Int J Radiat Biol. 95(2):225–223.
  • Chauhan V, Stricklin D, Cool D. 2020a. The integration of the adverse outcome pathway framework to radiation risk assessment. Int J Radiat Biol. 97(1):60–67.
  • Chauhan V, Sherman S, Said Z, Yauk CL, Stainforth R. 2020b. A case example of a radiation-relevant adverse outcome pathway to lung cancer. Int J Radiat Biol. 97(1):68–84.
  • Chauhan V, Villeneuve D, Cool D. 2021a. Collaborative efforts are needed among the scientific community to advance the adverse outcome pathway concept in areas of radiation risk assessment. Int J Radiat Biol. 20:1–12.
  • Chauhan V, Adam N, Kuo B, Williams A, Yauk CL, Wilkins R, Stainforth, R. 2021b. Meta-analysis of transcriptomic datasets using benchmark dose modeling shows value in supporting radiation risk assessment. Int J Rad Biol. 97(2):1–36.
  • Chepelev NL, Moffat ID, Labib S, Bourdon-Lacombe J, Kuo B, Buick JK, Lemieux F, Malik AI, Halappanavar S, Williams A, et al. 2015. Integrating toxicogenomics into human health risk assessment: lessons learned from the benzo[a]pyrene case study. Crit Rev Toxicol. 45(1):44–52.
  • Gannon AM, Moreau M, Farmahin R, Thomas RS, Barton-Maclaren TS, Nong A, Curran I, Yauk CL. 2019. Hexabromocyclododecane (HBCD): a case study applying tiered testing for human health risk assessment. Food Chem Toxicol. 131:110581.
  • Gwinn WM, Auerbach SS, Parham F, Stout MD, Waidyanatha S, Mutlu E, Collins B, Paules RS, Merrick BA, Ferguson S, et al. 2020. Evaluation of 5-day in vivo rat liver and kidney with high-throughput transcriptomics for estimating benchmark doses of apical outcomes. Toxicol Sci. 176(2):343–354.
  • Haber LT, Dourson ML, Allen BC, Hertzberg RC, Parker A, Vincent MJ, Maier A, Boobis AR. 2018. Benchmark dose (BMD) modeling: current practice, issues, and challenges. Crit Rev Toxicol. 48(5):387–415.
  • Harrill J, Shah I, Setzer RW, Haggard D, Auerbach S, Judson R, Thomas RS. 2019. Considerations for strategic use of high-throughput transcriptomics chemical screening data in regulator decisions. Curr Opin Toxicol. 15:64–75.
  • Harrill JA, Viant MR, Yauk CL, Sachana M, Gant TW, Auerbach SS, Beger RD, Bouhifd M, O'Brien J, Burgoon L, et al. 2021. Progress towards an OECD reporting framework for transcriptomics and metabolomics in regulatory toxicology. Regul Toxicol Pharmacol. 125:105020.
  • Hasin Y, Seldin M, Lusis A. 2017. Multi-omics approaches to disease. Genome Biol. 18(1):83.
  • Hester S, Eastmond DA, Bhat VS. 2015. Developing toxicogenomics as a research tool by applying benchmark dose-response modelling to inform chemical mode of action and tumorigenic potency. IJBT. 14(1):28–46.
  • ICRP 2007. The 2007 recommendations of the international commission on radiological protection. ICRP Publication 103. Ann ICRP. 37(2–4):11–16. https://doi.org/10.1016/j.icrp.2007.10.003.
  • ICRP 2012. ICRP statement on tissue reactions/early and late effects of radiation in normal tissues and organs – threshold doses for tissue reactions in a radiation protection context. ICRP publication 118. Ann ICRP. 41(1/2):19–23. https://doi.org/10.1016/j.icrp.2012.02.001.
  • Jassal B, Matthews L, Viteri G, Gong C, Lorente P, Fabregat A, Sidiropoulos K, Cook J, Gillespie M, Haw R, et al. 2020. The reactome pathway knowledgebase. Nucleic Acids Res. 48(D1):D498–D503.
  • Johnson KJ, Auerbach SS, Costa E. 2020. A rat liver transcriptomic point of departure predicts a prospective liver or non-liver apical point of departure. Toxicol Sci. 176(1):86–102.
  • Kuljus K, von Rosen D, Sand S, Victorin K. 2006. Comparing experimental designs for benchmark dose calculations for continuous endpoints. Risk Anal. 26(4):1031–1043.
  • Lucas J, Dressman HK, Suchindran S, Nakamura M, Chao NJ, Himburg H, Minor K, Phillips G, Ross J, Abedi M, et al. 2014. A translatable predictor of human radiation exposure. PLOS One. 9(9):e107897.
  • McDonald JT, Stainforth R, Miller J, Cahill T, Silveira WAd, Rathi KS, Hardiman G, Taylor D, Costes SV, Chauhan V, et al. 2020. NASA GeneLab platform utilized for biological response to space radiation in animal models. Cancers. 12(2):381–387.
  • Madrigal P, Gabel A, Villacampa A, Manzano A, Deane CS, Bezdan D, Carnero-Diaz E, Medina FJ, Hardiman G, Grosse I, et al. 2020. Revamping space-omics in Europe. Cell Syst. 11(6):555–556.
  • Moffat I, Chepelev N, Labib S, Bourdon-Lacombe J, Kuo B, Buick JK, Lemieux F, Williams A, Halappanavar S, Malik A, et al. 2015. Comparison of toxicogenomics and traditional approaches to inform mode of action and points of departure in human health risk assessment of benzo[a]pyrene in drinking water. Crit Rev Toxicol. 45(1):1–43.
  • National Toxicology Program 2018. NTP research report on national toxicology program approach to genomic dose-response modeling. Research Triangle Park (NC): National Toxicology Program. Natl Toxicol Program Res Rep Ser. April;(RR-05):1-42.
  • NCRP 2018. Implications of recent epidemiologic studies for the linear-non threshold model and radiation protection, NCRP commentary. National Council on Radiation Protection and Measurements, 2018. 27.
  • NCRP 2015. Health effects of low doses of radiation: perspectives on integrating radiation biology and epidemiology, NCRP commentary No. 24, national council on radiation protection and measurements, Bethesda, MD.
  • OECD (Organisation for Economic Co-operation and Development) 2017. Revised guidance document on developing and assessing adverse outcome pathways. OECD environment, health and safety publications, series on testing and assessment, Paris, France. No. 184.
  • OECD (Organisation for Economic Co-operation and Development) 2018. Users’ handbook supplement to the guidance document for developing and assessing AOPs. OECD series on adverse outcome pathways No.1.
  • Phillips JR, Svoboda DL, Tandon A, Patel S, Sedykh A, Mav D, Kuo B, Yauk CL, Yang L, Thomas RS, et al. 2019. BMDExpress 2: enhanced transcriptomic dose-response analysis workflow. Bioinformatics. 35(10):1780–1782.
  • Qutob SS, Chauhan V, Kuo B, Williams A, Yauk CL, McNamee JP, Gollapudi B. 2018. The application of transcriptional benchmark dose modeling for deriving thresholds of effects associated with solar-simulated ultraviolet radiation exposure. Environ Mol Mutagen. 59(6):502–515.
  • Ramaiahgar SC, Auerbach SS, Saddler TO, Rice JR, Dunlap PE, Sipes NS, DeVito MJ, Shah RR, Bushel PR, Merrick BA, et al. 2019. The power of resolution: contextualized understanding of biological responses to liver injury chemical using high-throughput transcriptomics and benchmark concentration modeling. Toxicol Sci. 169(2):553–566.
  • Rowan-Carroll A, Reardon A, Leingartner K, Gagné R, Williams A, Meier MJ, Kuo B, Bourdon-Lacombe J, Moffat I, Carrier R, et al. 2021. High-throughput transcriptomic analysis of human primary hepatocyte spheroids exposed to per- and polyfluoroalkyl substances as a platform for relative potency characterization. Toxicol Sci. 181(2):199–214.
  • Slob W, Moerbeek M, Rauniomaa E, Piersma AH. 2005. A statistical evaluation of toxicity study designs for the estimation of the benchmark dose in continuous endpoints. Toxicol Sci. 84(1):167–185.
  • Slob W. 2014a. Benchmark dose and the three Rs. Part I. Getting more information from the same number of animals. Crit Rev Toxicol. 44(7):557–567.
  • Slob W. 2014b. Benchmark dose and the three Rs. Part II. Consequencies for study design and animal use. Crit Rev Toxicol. 44(7):568–580.
  • Spinu N, Cronin MTD, Enoch SJ, Madden JC, Worth AP. 2020. Quantitative adverse outcome pathway (qAOP) models for toxicity prediction. Arch Toxicol. 94(5):1497–1510.
  • Thomas RS, Wesselkamper SC, Wang NCY, Zhao QJ, Petersen DD, Lambert JC, Cote I, Yang L, Healy E, Black MB, et al. 2013. Temporal concordance between apical and transcriptional points of departure for chemical risk assessment. Toxicol Sci. 134(1):180–194.
  • United States Environmental Protection Agency (US EPA) 2012. Benchmark Dose Technical Guidance. (EPA/100/R-12/001). Risk Assessment Forum, Washington, DC. Available online: http://www.epa.gov/raf/publications/pdfs/benchmark_dose_guidance.pdf.
  • Webster AF, Zumbo P, Fostel J, Gandara J, Hester SD, Recio L, Williams A, Wood CE, Yauk CL, Mason CE, et al. 2015. Mining the archives: a cross-platform analysis of gene expression profiles in archival formalin-fixed paraffin-embedded tissues. Toxicol Sci. 148(2):460–472.