Skip to main content
SpringerLink
Log in

In Silico Prediction of the Point of Departure (POD) with High-Throughput Data

  • Chapter
  • First Online:
Advances in Computational Toxicology

Part of the book series: Challenges and Advances in Computational Chemistry and Physics ((COCH,volume 30))

  • 1032 Accesses

Abstract

Determining the point of departure (POD) is a critical step in chemical risk assessment . Current approaches based on chronic animal studies are costly and time-consuming while being insufficient for providing mechanistic information regarding toxicity. Driven by the desire to incorporate multiple lines of evidence relevant to human toxicology and to reduce animal use, there has been a heightened interest in utilizing transcriptional and other high-throughput assay endpoints to infer the POD . In this review, we outline common data modeling approaches utilizing gene expression profiles from animal tissues to estimate the POD in comparison with obtaining PODs based on apical endpoints . Various issues in experiment design, technology platforms, data analysis methods, and software packages are explained. Potential choices for each step are discussed. Recent development for models incorporating in vitro assay endpoints is also examined, including PODs based on in vitro assays and efforts to predict in vivo PODs with in vitro data. Future directions and potential research areas are also discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Abbreviations

AC50:

Half-maximal effective concentration

AIC:

Akaike information criterion

AOP:

Adverse outcome pathway

BMD:

Benchmark dose

BMDL:

A statistical lower bound of BMD

BMR:

Benchmark risk

BRBZ:

Bromobenzene

Cmax:

Peak plasma concentration

EPA:

Environmental Protection Agency

EU:

European Union

HCI:

High content imaging

HZBZ:

Hydrazobenzene

IVIVE:

In vitro-in vivo extrapolation

KDMM:

Kernel density mean of M-component

KE:

Key event

KER:

Key event relationship

LOAEL:

Lowest-observed-adverse-effect level

MDMB:

4,4′-Methylenebis (N,Ndimethyl) benzenamine

MIE:

Molecular initiating event

MOA:

Mode of action

MSigDB:

Molecular Signature Database

NDPA:

N-Nitrosodiphenylamine

NOAEL:

No-observed-adverse-effect-level

POD:

Point of departure

REACH:

Registration, evaluation, authorization and restriction of chemical substances

RMA:

Robust Multi-array Average normalization method

RPKM:

Reads per kilobase per million mapped reads

TG-GATEs:

Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System

TLR:

Target learning region

TRBZ:

1,2,4-Tribromobenzene

TTCP:

2,3,4,6-Tetrachlorophenol

References

  1. NRC (National Research Council) (2007) Toxicity testing in the 21st century: a vision and a strategy. The National Academies Press, Washington, DC

    Google Scholar 

  2. NRC (National Research Council) (2017) Using 21st century science to improve risk-related evaluations. The National Academies Press, Washington, DC

    Google Scholar 

  3. Locke PA, Westphal M, Tischler J, Hessler K, Frasch P, Myers B, Krewski D (2017) Implementing toxicity testing in the 21st century: challenges and opportunities. Int J Risk Assess Manage 20(1–3):198–225

    Article  Google Scholar 

  4. Rudén C, Hansson SO (2010) Registration, evaluation, and authorization of chemicals (REACH) is but the first step. How far will it take us? Six further steps to improve the European chemicals legislation. Environ Health Perspect 118(1):6–10

    Article  Google Scholar 

  5. Igarashi Y, Nakatsu N, Yamashita T, Ono A, Ohno Y, Urushidani T, Yamada H (2015) Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res 43(D1):D921–D927

    Article  CAS  Google Scholar 

  6. Collins FS, Gray GM, Bucher JR (2008) Transforming environmental health protection. Science 319(5865):906–907

    Article  CAS  Google Scholar 

  7. Tice RR, Austin CP, Kavlock RJ, Bucher JR (2013) Improving the human hazard characterization of chemicals: a Tox21 update. Environ Health Perspect 121(7):756–765

    Article  Google Scholar 

  8. Judson R, Houck K, Martin M, Knudsen T, Thomas R, Sipes N, Shah I, Wambaugh J, Crofton K (2014) In vitro and modeling approaches to risk assessment from the U.S. environmental protection agency ToxCast program. Basic Clin Pharmacol Toxicol 115(1):69–76

    Article  CAS  Google Scholar 

  9. Richard A, Judson R, Houck K, Grulke C, Volarath P, Thillainadarajah I et al (2016) The ToxCast chemical landscape—paving the road to 21st century toxicology. Chem Res Toxicol 29(8):1225–1251

    Article  CAS  Google Scholar 

  10. Silva M, Pham N, Lewis C, Iyer S, Kwok E, Solomon G, Zeise L (2015) A comparison of ToxCast test results with in vivo and other in vitro endpoints for neuro, endocrine, and developmental toxicities: a case study using endosulfan and methidathion. Birth Defects Res B Dev Reprod Toxicol 104(2):71–89

    Article  CAS  Google Scholar 

  11. Liu J, Mansouri K, Judson RS, Martin MT, Hong H, Chen M, Xu X, Thomas RS, Shah I (2015) Predicting hepatotoxicity using ToxCast in vitro bioactivity and chemical structure. Chem Res Toxicol 28(4):738–751

    Article  CAS  Google Scholar 

  12. Huang R, Xia M, Sakamuru S, Zhao J, Shahane SA, Attene-Ramos M, Zhao T, Austin CP, Simeonov A (2016) Modelling the Tox21 10K chemical profiles for in vivo toxicity prediction and mechanism characterization. Nat Commun 7:10425. https://doi.org/10.1038/ncomms10425

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Kleinstreuer N, Ceger P, Watt E, Martin M, Houck K, Browne P, Thomas R, Casey W, Dix D, Allen D, Sakamuru S, Xia M, Huang R, Judson R (2017) Development and validation of a computational model for androgen receptor activity. Chem Res Toxicol 30(4):946–964

    Article  CAS  Google Scholar 

  14. Ankley GT, Bennett RS, Erickson RJ, Hoff DJ, Hornung MW, Johnson RD et al (2010) Adverse outcome pathways: a conceptual framework to support ecotoxicology research and risk assessment. Environ Toxicol Chem 29(3):730–741

    Article  CAS  Google Scholar 

  15. Villeneuve DL, Crump D, Garcia-Reyero N, Hecker M, Hutchinson TH et al (2014) Adverse outcome pathway (AOP) development I: strategies and principles. Toxicol Sci 142(2):312–320

    Article  CAS  Google Scholar 

  16. Thomas RS, Allen BC, Nong A, Yang L, Bermudez E, Clewell HJ III, Andersen ME (2007) A method to integrate benchmark dose estimates with genomic data to assess the functional effects of chemical exposure. Toxicol Sci 98(1):240–248

    Article  CAS  Google Scholar 

  17. Thomas RS, Wesselkamper SC, Wang NCY, Zhao QJ, Petersen DD et al (2013) Temporal concordance between apical and transcriptional points of departure for chemical risk assessment. Toxicol Sci 134(1):180–194

    Article  CAS  Google Scholar 

  18. Yang L, Allen BC, Thomas RS (2007) BMDExpress: a software tool for the benchmark dose analyses of genomic data. BMC Genom 8(1):387

    Article  Google Scholar 

  19. Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4(2):249–264

    Article  Google Scholar 

  20. Farmahin R, Williams A, Kuo B, Chepelev NL, Thomas RS, Barton-Maclaren TS, Curran IH, Nong A, Wade MG, Yauk CL (2017) Recommended approaches in the application of toxicogenomics to derive points of departure for chemical risk assessment. Arch Toxicol 91(5):2045–2065

    Article  CAS  Google Scholar 

  21. Kodell RL (2009) Replace the NOAEL and LOAEL with the BMDL01 and BMDL10. Environ Ecol Stat 16(1):3–12

    Article  CAS  Google Scholar 

  22. National Toxicology Program (2018) NTP research report on national toxicology program approach to genomic dose-response modeling. U.S. Department of Health and Human Services, Washington, DC

    Google Scholar 

  23. Zhou YH, Cichocki JA, Soldatow VY, Scholl EH, Gallins PJ, Jima D et al (2017) Comparative dose-response analysis of liver and kidney transcriptomic effects of trichloroethylene and tetrachloroethylene in B6C3F1 mouse. Toxicol Sci 160(1):95–110

    Article  CAS  Google Scholar 

  24. Black MB, Parks BB, Pluta L, Chu TM, Allen BC, Wolfinger RD, Thomas RS (2014) Comparison of microarrays and RNA-seq for gene expression analyses of dose-response experiments. Toxicol Sci 137(2):385–403

    Article  CAS  Google Scholar 

  25. Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1):139–140

    Article  CAS  Google Scholar 

  26. Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genome Biol 11(10):R106. https://doi.org/10.1186/gb-2010-11-10-r106, http://genomebiology.com/2010/11/10/R106/

    Article  CAS  Google Scholar 

  27. Crump K (2002) Critical issues in benchmark calculations from continuous data. Crit Rev Toxicol 32(3):133–153

    Article  CAS  Google Scholar 

  28. Law CW, Chen Y, Shi W, Smyth GK (2014) Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol 15(2):R29. http://genomebiology.com/2014/15/2/R29

    Article  Google Scholar 

  29. Liberzon A, Birger C, Thorvaldsdóttir H, Ghandi M, Mesirov JP, Tamayo P (2015) The molecular signatures database hallmark gene set collection. Cell Syst 1(6):417–425

    Article  CAS  Google Scholar 

  30. Moffat I, Chepelev NL, Labib S, Bourdon-Lacombe J, Kuo B 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

    Article  CAS  Google Scholar 

  31. Labib S, Williams A, Yauk CL, Nikota JK, Wallin H et al (2015) Nano-risk science: application of toxicogenomics in an adverse outcome pathway framework for risk assessment of multi-walled carbon nanotubes. Part Fibre Toxicol 13(1):15

    Article  Google Scholar 

  32. Dean JL, Zhao QJ, Lambert JC, Hawkins BS, Thomas RS, Wesselkamper SC (2017) Application of gene set enrichment analysis for identification of chemically induced, biologically relevant transcriptomic networks and potential utilization in human health risk assessment. Toxicol Sci 157(1):85–99

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Filer DL, Kothiya P, Setzer RW, Judson RS, Martin MT (2016) tcpl: the ToxCast pipeline for high-throughput screening data. Bioinformatics 33(4):618–620

    Google Scholar 

  34. Wang D (2018) Infer the in vivo point of departure with ToxCast in vitro assay data using a robust learning approach. Arch Toxicol 92(9):2913–2922

    Article  CAS  Google Scholar 

  35. Shah I, Setzer RW, Jack J, Houck KA, Judson RS, Knudsen TB et al (2016) Using ToxCast™ data to reconstruct dynamic cell state trajectories and estimate toxicological points of departure. Environ Health Perspect 124(7):910–9

    Article  CAS  Google Scholar 

  36. Sipes NS, Wambaugh JF, Pearce R, Auerbach SS, Wetmore BA et al (2017) An intuitive approach for predicting potential human health risk with the Tox21 10K library. Environ Sci Technol 51(18):10786–10796

    Article  CAS  Google Scholar 

  37. Pearce RG, Setzer RW, Strope CL, Sipes NS, Wambaugh JF (2017) Httk: R package for high-throughput toxicokinetics. J Stat Softw 79(4):1–26

    Article  Google Scholar 

  38. Mav D, Shah RR, Howard BE, Auerbach SS, Bushel PR et al (2018) A hybrid gene selection approach to create the S1500+ targeted gene sets for use in high-throughput transcriptomics. PLoS ONE 13(2):e0191105

    Article  Google Scholar 

Download references

Acknowledgements

The author would like to thank the editor and an anonymous reviewer for valuable suggestions. The opinions expressed in this paper are those of the author and do not necessarily reflect the views of the US Food and Drug Administration .

Author information

Authors and Affiliations

  1. Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration (FDA), Jefferson, AR, USA

    Dong Wang

Authors
  1. Dong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dong Wang .

Editor information

Editors and Affiliations

  1. National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA

    Huixiao Hong

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Wang, D. (2019). In Silico Prediction of the Point of Departure (POD) with High-Throughput Data. In: Hong, H. (eds) Advances in Computational Toxicology. Challenges and Advances in Computational Chemistry and Physics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-16443-0_15

Download citation

Publish with us

Policies and ethics

Search

Navigation