Chapter 14
Predictive Modeling of Tox21 Data
Ruili Huang
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 cellbased 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.
Keywords Computational modeling · Human toxicity · Animal toxicity ·
Adverse drug effect · In vitro assay · High-throughput screening
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
R. Huang (B)
National Center for Advancing Translational Sciences, NIH, 9800 Medical Center Drive,
Rockville, MD 20850, USA
e-mail: huangru@mail.nih.gov
© This is a U.S. government work and not under copyright protection in the U.S.; foreign
copyright protection may apply 2019
H. Hong (ed.), Advances in Computational Toxicology, Challenges and Advances
in Computational Chemistry and Physics 30,
https://doi.org/10.1007/978-3-030-16443-0_14
279
Predictive Modeling of Tox21 Data
Ruili Huang
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 cellbased 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.
Keywords Computational modeling · Human toxicity · Animal toxicity ·
Adverse drug effect · In vitro assay · High-throughput screening
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
R. Huang (B)
National Center for Advancing Translational Sciences, NIH, 9800 Medical Center Drive,
Rockville, MD 20850, USA
e-mail: huangru@mail.nih.gov
© This is a U.S. government work and not under copyright protection in the U.S.; foreign
copyright protection may apply 2019
H. Hong (ed.), Advances in Computational Toxicology, Challenges and Advances
in Computational Chemistry and Physics 30,
https://doi.org/10.1007/978-3-030-16443-0_14
279