Advertisement

Mammalian Genome

, Volume 29, Issue 1–2, pp 182–189 | Cite as

Advancing chemical risk assessment decision-making with population variability data: challenges and opportunities

  • Weihsueh A. ChiuEmail author
  • Ivan Rusyn
Article

Abstract

Characterizing population variability, including identifying susceptible populations and quantifying their increased susceptibility, is an important aspect of chemical risk assessment, but one that is challenging with traditional experimental models and risk assessment methods. New models and methods to address population variability can be used to advance the human health assessments of chemicals in three key areas. First, with respect to hazard identification, evaluating toxicity using population-based in vitro and in vivo models can potentially reduce both false positive and false negative signals. Second, with respect to evaluating mechanisms of toxicity, enhanced ability to do genetic mapping using these models allows for the identification of key biological pathways and mechanisms that may be involved in toxicity and/or susceptibility. Third, with respect to dose–response assessment, population-based toxicity data can serve as a surrogate for human variability, and thus be used to quantitatively estimate the degree of human toxicokinetic/toxicodynamic variability and thereby increase confidence in setting health-protective exposure limits. A number of case studies have been published that demonstrate the potential opportunities for improving risk assessment and decision-making, and include studies using Collaborative Cross and Diversity Outbred mice, as well as populations of human cell lines from the 1000 Genomes project. Key challenges include the need to apply more sophisticated computational and statistical models analyzing population-based toxicity data, and the need to integrate these more complex analyses into risk assessments and decision-making.

Notes

Acknowledgements

This paper was supported, in part, by an NIH grant #P42 ES027704. Its contents are solely the responsibility of the grantee and do not necessarily represent the official views of the NIH. Further, the NIH does not endorse the purchase of any commercial products or services mentioned in this paper.

Compliance with ethical standards

Conflict of interest

On behalf of all the authors, the corresponding author states that there is no conflict of interest.

References

  1. Abdo N, Wetmore BA, Chappell GA, Shea D, Wright FA, Rusyn I (2015a) In vitro screening for population variability in toxicity of pesticide-containing mixtures. Environ Int 85:147–155CrossRefPubMedPubMedCentralGoogle Scholar
  2. Abdo N, Xia M, Brown CC, Kosyk O, Huang R, Sakamuru S, Zhou YH, Jack JR, Gallins P, Xia K, Li Y, Chiu WA, Motsinger-Reif AA, Austin CP, Tice RR, Rusyn I, Wright FA (2015b) Population-based in vitro hazard and concentration-response assessment of chemicals: the 1000 genomes high-throughput screening study. Environ Health Perspect 123:458–466PubMedPubMedCentralGoogle Scholar
  3. An Act to improve, strengthen, and accelerate programs for the prevention and abatement of air pollution (Clean Air Act) (1963), as amended 1990. U.S.C. 42 Section 7401 et seq.Google Scholar
  4. ATSDR (2013) Minimal risk levels (MRLs). U.S. Department of Health and Human Services, Agency for Toxic Substances and Disease Registry, AtlantaGoogle Scholar
  5. Aylor DL, Valdar W, Foulds-Mathes W, Buus RJ, Verdugo RA, Baric RS, Ferris MT, Frelinger JA, Heise M, Frieman MB, Gralinski LE, Bell TA, Didion JD, Hua K, Nehrenberg DL, Powell CL, Steigerwalt J, Xie Y, Kelada SN, Collins FS, Yang IV, Schwartz DA, Branstetter LA, Chesler EJ, Miller DR, Spence J, Liu EY, McMillan L, Sarkar A, Wang J, Wang W, Zhang Q, Broman KW, Korstanje R, Durrant C, Mott R, Iraqi FA, Pomp D, Threadgill D, de Villena FP, Churchill GA (2011) Genetic analysis of complex traits in the emerging collaborative cross. Genome Res 21, 1213–1222CrossRefPubMedPubMedCentralGoogle Scholar
  6. Beyer LA, Beck BD, Lewandowski TA (2011) Historical perspective on the use of animal bioassays to predict carcinogenicity: evolution in design and recognition of utility. Crit Rev Toxicol 41:321–338CrossRefPubMedGoogle Scholar
  7. Bradford BU, Lock EF, Kosyk O, Kim S, Uehara T, Harbourt D, DeSimone M, Threadgill DW, Tryndyak V, Pogribny IP, Bleyle L, Koop DR, Rusyn I (2011) Interstrain differences in the liver effects of trichloroethylene in a multistrain panel of inbred mice. Toxicol Sci 120:206–217CrossRefPubMedGoogle Scholar
  8. Chiu WA, Slob W (2015) A unified probabilistic framework for dose-response assessment of human health effects. Environ Health Perspect 123:1241–1254CrossRefPubMedPubMedCentralGoogle Scholar
  9. Chiu WA, Campbell JL, Clewell HJ, Zhou YH, Wright FA, Guyton KZ, Rusyn I (2014) Physiologically-based pharmacokinetic (PBPK) modeling of inter-strain variability in trichloroethylene metabolism in the mouse. Environ Health Perspect 122:456–463PubMedPubMedCentralGoogle Scholar
  10. Chiu WA, Wright FA, Rusyn I (2017) A tiered, Bayesian approach to estimating of population variability for regulatory decision-making. ALTEX 34(3):377–388.  https://doi.org/10.14573/altex.1608251 PubMedGoogle Scholar
  11. Churchill GA, Gatti DM, Munger SC, Svenson KL (2012) The Diversity Outbred mouse population. Mamm Genome 23:713–718CrossRefPubMedPubMedCentralGoogle Scholar
  12. Cichocki JA, Furuya S, Venkatratnam A, McDonald TJ, Knap AH, Wade T, Sweet S, Chiu WA, Threadgill DW, Rusyn I (2017) Characterization of variability in toxicokinetics and toxicodynamics of tetrachloroethylene using the collaborative cross mouse population. Environ Health Perspect 125:057006CrossRefPubMedPubMedCentralGoogle Scholar
  13. Federal Insecticide, Fungicide, and Rodenticide Act (1910), as amended 2012. U.S.C. 7 Section 136 et seq.Google Scholar
  14. Federal Water Pollution Control Act (Clean Water Act) (1972), as amended 2002. U.S.C. 33 Section 1251 et seq.Google Scholar
  15. French JE, Gatti DM, Morgan DL, Kissling GE, Shockley KR, Knudsen GA, Shepard KG, Price HC, King D, Witt KL, Pedersen LC, Munger SC, Svenson KL, Churchill GA (2015) Diversity outbred mice identify population-based exposure thresholds and genetic factors that influence benzene-induced genotoxicity. Environ Health Perspect 123:237–245PubMedGoogle Scholar
  16. Hanawalt PC (1996) Role of transcription-coupled DNA repair in susceptibility to environmental carcinogenesis. Environ Health Perspect 104(Suppl 3):547–551CrossRefPubMedPubMedCentralGoogle Scholar
  17. Harrill AH, McAllister KA (2017) New rodent population models may inform human health risk assessment and identification of genetic susceptibility to environmental exposures. Environ Health Perspect 125:086002CrossRefPubMedPubMedCentralGoogle Scholar
  18. Harrill AH, Ross PK, Gatti DM, Threadgill DW, Rusyn I (2009a) Population-based discovery of toxicogenomics biomarkers for hepatotoxicity using a laboratory strain diversity panel. Toxicol Sci 110:235–243CrossRefPubMedPubMedCentralGoogle Scholar
  19. Harrill AH, Watkins PB, Su S, Ross PK, Harbourt DE, Stylianou IM, Boorman GA, Russo MW, Sackler RS, Harris SC, Smith PC, Tennant R, Bogue M, Paigen K, Harris C, Contractor T, Wiltshire T, Rusyn I, Threadgill DW (2009b) Mouse population-guided resequencing reveals that variants in CD44 contribute to acetaminophen-induced liver injury in humans. Genome Res 19:1507–1515CrossRefPubMedPubMedCentralGoogle Scholar
  20. Kaeppler SM (1997) Quantitative trait locus mapping using sets of near-isogenic lines: relative power comparisons and technical considerations. Theor Appl Genet 95:384–392CrossRefGoogle Scholar
  21. Lusis AJ, Seldin MM, Allayee H, Bennett BJ, Civelek M, Davis RC, Eskin E, Farber CR, Hui S, Mehrabian M, Norheim F, Pan C, Parks B, Rau CD, Smith DJ, Vallim T, Wang Y, Wang J (2016) The hybrid mouse diversity panel: a resource for systems genetics analyses of metabolic and cardiovascular traits. J Lipid Res 57:925–942CrossRefPubMedPubMedCentralGoogle Scholar
  22. Maronpot RR, Nyska A, Foreman JE, Ramot Y (2016) The legacy of the F344 rat as a cancer bioassay model (a retrospective summary of three common F344 rat neoplasms). Crit Rev Toxicol 46:641–675CrossRefPubMedPubMedCentralGoogle Scholar
  23. Maurizio PL, Ferris MT, Keele GR, Miller DR, Shaw GD, Whitmore AC, West A, Morrison CR, Noll KE, Plante KS, Cockrell AS, Threadgill DW, Pardo-Manuel de Villena F, Baric RS, Heise MT, Valdar W (2017) Bayesian diallel analysis reveals Mx1-dependent and Mx1-independent effects on response to influenza a virus in mice. G3 (Bethesda).  https://doi.org/10.1534/g3.117.300438 Google Scholar
  24. McElroy AK, Erickson BR, Flietstra TD, Rollin PE, Nichol ST, Towner JS, Spiropoulou CF (2014) Ebola hemorrhagic Fever: novel biomarker correlates of clinical outcome. J Infect Dis 210(4):558–566.  https://doi.org/10.1093/infdis/jiu088 CrossRefPubMedPubMedCentralGoogle Scholar
  25. Mosedale M, Kim Y, Brock WJ, Roth SE, Wiltshire T, Eaddy JS, Keele GR, Corty RW, Xie Y, Valdar W, Watkins PB (2017) Candidate risk factors and mechanisms for tolvaptan-Induced liver injury are identified using a collaborative cross approach. Toxicol Sci 156:438–454PubMedGoogle Scholar
  26. NAS (1983) Risk assessment in the federal government: Managing the process. National Academies Press, Washington, D.C.Google Scholar
  27. NAS (2007) Toxicity testing in the 21st century: A vision and a strategy. The National Academies Press, Washington, D.C.Google Scholar
  28. NAS (2009) Science and decisions: Advancing risk assessment. National Academies Press, Washington, DCGoogle Scholar
  29. NAS (2012) Exposure science in the 21st Century: A vision and a strategy. National Academies Press, Washington, DCGoogle Scholar
  30. NAS (2017) Using 21st Century science to improve risk-related evaluations. National Academies Press, Washington, DCGoogle Scholar
  31. Negi LM, Talegaonkar S, Jaggi M, Ahmad FJ, Iqbal Z, Khar RK (2012) Role of CD44 in tumour progression and strategies for targeting. J Drug Target 20:561–573CrossRefPubMedGoogle Scholar
  32. Oreper D, Cai Y, Tarantino LM, de Villena FP, Valdar W (2017) Inbred Strain Variant Database (ISVdb): A repository for probabilistically informed sequence differences among the collaborative cross strains and their founders. G3 (Bethesda) 7, 1623–1630Google Scholar
  33. Rasmussen AL, Okumura A, Ferris MT, Green R, Feldmann F, Kelly SM, Scott DP, Safronetz D, Haddock E, LaCasse R, Thomas MJ, Sova P, Carter VS, Weiss JM, Miller DR, Shaw GD, Korth MJ, Heise MT, Baric RS, de Villena FP, Feldmann H, Katze MG (2014) Host genetic diversity enables Ebola hemorrhagic fever pathogenesis and resistance. Science 346:987–991CrossRefPubMedPubMedCentralGoogle Scholar
  34. Rusyn I, Gatti DM, Wiltshire T, Kleeberger SR, Threadgill DW (2010) Toxicogenetics: population-based testing of drug and chemical safety in mouse models. Pharmacogenomics 11:1127–1136CrossRefPubMedPubMedCentralGoogle Scholar
  35. Threadgill DW, Churchill GA (2012) Ten years of the collaborative cross. Genetics 190:291–294CrossRefPubMedPubMedCentralGoogle Scholar
  36. U.S. EPA (1989) Risk assessment guidance for superfund volume I Human health evaluation manual (Part A). EPA/540/1–90/002. U.S. Environmental Protection Agency, Washington, DCGoogle Scholar
  37. U.S. EPA (2002.) A review of the reference dose and reference concentration processes. EPA/630/P-02/002F. U.S. Environmental Protection Agency, Washington, DCGoogle Scholar
  38. U.S. EPA (2005) Guidelines for carcinogen risk assessment. EPA/630/P-03/001F. U.S. Environmental Protection Agency, Washington, DCGoogle Scholar
  39. U.S. EPA (2012) Benchmark dose technical guidance. EPA/100/R-12/001. U.S. Environmental Protection Agency, Washington, DCGoogle Scholar
  40. Venkatratnam A, Furuya S, Kosyk O, Gold A, Bodnar W, Konganti K, Threadgill DW, Gillespie KM, Aylor DL, Wright FA, Chiu WA, Rusyn I (2017) Collaborative cross mouse population enables refinements to characterization of the variability in toxicokinetics of trichloroethylene and provides genetic evidence for the role of PPAR pathway in its oxidative metabolism. Toxicol Sci 158:48–62CrossRefPubMedGoogle Scholar
  41. Wetmore BA (2015) Quantitative in vitro-to-in vivo extrapolation in a high-throughput environment. Toxicology 332:94–101CrossRefPubMedGoogle Scholar
  42. WHO/IPCS (2014) Guidance document on evaluating and expressing uncertainty in hazard characterization. Geneve: World Health Organization International Program on Chemical SafetyGoogle Scholar
  43. Zeise L, Bois FY, Chiu WA, Hattis D, Rusyn I, Guyton KZ (2013) Addressing human variability in next-generation human health risk assessments of environmental chemicals. Environ Health Perspect 121:23–31PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical SciencesTexas A&M UniversityCollege StationUSA

Personalised recommendations