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Archives of Toxicology

, Volume 92, Issue 9, pp 2913–2922 | Cite as

Infer the in vivo point of departure with ToxCast in vitro assay data using a robust learning approach

  • Dong Wang
In vitro systems
  • 167 Downloads

Abstract

The development and application of high throughput in vitro assays is an important development for risk assessment in the twenty-first century. However, there are still significant challenges to incorporate in vitro assays into routine toxicity testing practices. In this paper, a robust learning approach was developed to infer the in vivo point of departure (POD) with in vitro assay data from ToxCast and Tox21 projects. Assay data from ToxCast and Tox21 projects were utilized to derive the in vitro PODs for several hundred chemicals. These were combined with in vivo PODs from ToxRefDB regarding the rat and mouse liver to build a high-dimensional robust regression model. This approach separates the chemicals into a majority, well-predicted set; and a minority, outlier set. Salient relationships can then be learned from the data. For both mouse and rat liver PODs, over 93% of chemicals have inferred values from in vitro PODs that are within ± 1 of the in vivo PODs on the log10 scale (the target learning region, or TLR) and R2 of 0.80 (rats) and 0.78 (mice) for these chemicals. This is comparable with extrapolation between related species (mouse and rat), which has 93% chemicals within the TLR and the R2 being 0.78. Chemicals in the outlier set tend to also have more biologically variable characteristics. With the continued accumulation of high throughput data for a wide range of chemicals, predictive modeling can provide a valuable complement for adverse outcome pathway based approach in risk assessment.

Keywords

Point of departure ToxCast Tox21 Robust learning In vitro assays 

Abbreviations

AOP

Adverse outcome pathway

BMAD

Baseline median absolute deviation

BMD

Benchmark dose

BMDL

The statistical lower bound of BMD

EPA

Environmental Protection Agency

EU

European Union

FDA

Food and Drug Administration

LASSO

Least absolute shrinkage and selection operator

LEL

Lowest effect level

LOAEL

Lowest observed adverse effect level

NCATS

National Center for Advancing Translational Sciences

NIH

National Institute of Health

NOAEL

No observed adverse effect level

NTP

National Toxicology Program

POD

Point of departure

REACH

Registration, evaluation, authorization and restriction of chemical substances

TG-GATEs

Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System

TLR

Target learning region

Notes

Acknowledgements

The author wishes to thank Drs. Weida Tong, Zhichao Liu, Huixiao Hong, Qiang Shi, and Mei Nan at National Center for Toxicological Research for valuable discussion and comments. The author also wants to thank an anonymous referee for valuable comments. 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.

Compliance with ethical standards

Conflict of interest

The author declares that he has no conflict of interest.

Supplementary material

204_2018_2260_MOESM1_ESM.csv (7 kb)
Supplementary material 1 (CSV 7 KB)
204_2018_2260_MOESM2_ESM.csv (6 kb)
Supplementary material 2 (CSV 5 KB)

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Copyright information

© This is a U.S. government work and its text is not subject to copyright protection in the United States; however, its text may be subject to foreign copyright protection 2018

Authors and Affiliations

  1. 1.Division of Bioinformatics and Biostatistics, National Center for Toxicological ResearchUS Food and Drug AdministrationJeffersonUSA

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