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.
Keywords
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- 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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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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DOI: https://doi.org/10.1007/978-3-030-16443-0_14
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