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Advances in Computational Toxicology pp 299–313Cite as

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

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

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.

Keywords

  • High-throughput assays
  • Microarrays
  • Point of departure
  • Predictive modeling
  • RNAseq
  • Toxicogenomics
  • Transcriptional profiling

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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

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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 .

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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

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  1. Dong Wang
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Correspondence to Dong Wang .

Editor information

Editors and Affiliations

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

    Dr. Huixiao Hong

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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

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