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Environ Health Perspect. 2019 Apr;127(4):47001. doi: 10.1289/EHP3614.

Nonanimal Models for Acute Toxicity Evaluations: Applying Data-Driven Profiling and Read-Across.

Author information

1
1 Center for Computational and Integrative Biology, Rutgers University , Camden, New Jersey, USA.
2
2 Integrated Laboratory Systems (ILS) , Research Triangle Park, North Carolina, USA.
3
3 Department of Computer Science, Rutgers University , Camden, New Jersey, USA.
4
4 Johns Hopkins Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT) , Baltimore, Maryland, USA.
5
5 University of Konstanz, CAAT-Europe , Konstanz, Germany.
6
6 Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy, Rutgers University , Piscataway, New Jersey, USA.
7
7 Department of Chemistry, Rutgers University , Camden, New Jersey, USA.

Abstract

BACKGROUND:

Low-cost, high-throughput in vitro bioassays have potential as alternatives to animal models for toxicity testing. However, incorporating in vitro bioassays into chemical toxicity evaluations such as read-across requires significant data curation and analysis based on knowledge of relevant toxicity mechanisms, lowering the enthusiasm of using the massive amount of unstructured public data.

OBJECTIVE:

We aimed to develop a computational method to automatically extract useful bioassay data from a public repository (i.e., PubChem) and assess its ability to predict animal toxicity using a novel bioprofile-based read-across approach.

METHODS:

A training database containing 7,385 compounds with diverse rat acute oral toxicity data was searched against PubChem to establish in vitro bioprofiles. Using a novel subspace clustering algorithm, bioassay groups that may inform on relevant toxicity mechanisms underlying acute oral toxicity were identified. These bioassays groups were used to predict animal acute oral toxicity using read-across through a cross-validation process. Finally, an external test set of over 600 new compounds was used to validate the resulting model predictivity.

RESULTS:

Several bioassay clusters showed high predictivity for acute oral toxicity (positive prediction rates range from 62-100%) through cross-validation. After incorporating individual clusters into an ensemble model, chemical toxicants in the external test set were evaluated for putative acute toxicity (positive prediction rate equal to 76%). Additionally, chemical fragment -in vitro-in vivo relationships were identified to illustrate new animal toxicity mechanisms.

CONCLUSIONS:

The in vitro bioassay data-driven profiling strategy developed in this study meets the urgent needs of computational toxicology in the current big data era and can be extended to develop predictive models for other complex toxicity end points. https://doi.org/10.1289/EHP3614.

PMID:
30933541
DOI:
10.1289/EHP3614
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