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Predictive Modeling of Tox21 Data

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Advances in Computational Toxicology

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

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Abstract

As an alternative to traditional animal toxicology studies, the toxicology for the twenty-first century (Tox21 ) program initiated a large-scale, systematic screening of chemicals against target-specific, mechanism-oriented in vitro assays aiming to predict chemical toxicity based on these in vitro assay data. The Tox21 library of ~10,000 environmental chemicals and drugs, representing a wide range of structural diversity, has been tested in triplicate against a battery of cell-based assays in a quantitative high-throughput screening (qHTS ) format generating over 85 million data points that have been made publicly available. This chapter describes efforts to build in vivo toxicity prediction models based on in vitro activity profiles of compounds. Limitations of the current data and strategies to select an optimal set of assays for improved model performance are discussed. To encourage public participation in developing new methods and models for toxicity prediction , a “crowd-sourcing” challenge was organized based on the Tox21 assay data with successful outcomes.

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Abbreviations

ADE:

Adverse Drug Effect

ACToR:

Aggregated Computational Toxicology Online Resource

AUC-ROC:

Area Under the Receiver Operating Characteristic curve

ASNN:

Associative Neural Networks

BLA:

Beta-lactamase

CEBS:

Chemical Effects in Biological Systems

CYP:

Cytochrome P450

DMSO:

Dimethylsulfoxide

DTA:

Drug Target Annotation

EPA:

Environmental Protection Agency

FN:

False Negative

FP:

False Positive

FDA:

Food and Drug Administration

GPCR:

G-Protein-Coupled Receptor

HTS:

High-Throughput Screening

NCATS:

National Center for Advancing Translational Sciences

NCCT:

National Center for Computational Toxicology

NIEHS:

National Institute of Environmental Health Sciences

NTP:

National Toxicology Program

NR:

Nuclear Receptor

QC:

Quality Control

qHTS:

Quantitative High-Throughput Screening

QSAR:

Quantitative Structure–Activity Relationship

ROC:

Receiver operating characteristic

SOM:

Self-Organizing Map

SR:

Stress Response

Tox21:

Toxicology for the twenty-first Century

TN:

True Negative

TP:

True Positive

WFS:

Weighted Feature Significance

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Authors and Affiliations

  1. National Center for Advancing Translational Sciences, NIH, 9800 Medical Center Drive, Rockville, MD, 20850, USA

    Ruili Huang

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  1. Ruili Huang
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Correspondence to Ruili Huang .

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Editors and Affiliations

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

    Huixiao Hong

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Huang, R. (2019). Predictive Modeling of Tox21 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_14

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