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

  • In vitro systems
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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.

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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

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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.

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Authors and Affiliations

  1. Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR, 72079, USA

    Dong Wang

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Correspondence to Dong Wang.

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Wang, D. Infer the in vivo point of departure with ToxCast in vitro assay data using a robust learning approach. Arch Toxicol 92, 2913–2922 (2018). https://doi.org/10.1007/s00204-018-2260-6

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