Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity
Top Cited Papers
- 14 March 2019
- journal article
- research article
- Published by American Chemical Society (ACS) in Chemical Research in Toxicology
- Vol. 32 (4), 536-547
- https://doi.org/10.1021/acs.chemrestox.8b00393
Abstract
In 2016, the Frank R. Lautenberg Chemical Safety for the 21st Century Act became the first US legislation to advance chemical safety evaluations by utilizing novel testing approaches that reduce the testing of vertebrate animals. Central to this mission is the advancement of computational toxicology and artificial intelligence approaches to implementing innovative testing methods. In the current big data era, the terms volume (amount of data), velocity (growth of data), and variety (the diversity of sources) have been used to characterize the currently available chemical, in vitro, and in vivo data for toxicity modeling purposes. Furthermore, as suggested by various scientists, the variability (internal consistency or lack thereof) of publicly available data pools, such as PubChem, also presents significant computational challenges. The development of novel artificial intelligence approaches based on public massive toxicity data is urgently needed to generate new predictive models for chemical toxicity evaluations and make the developed models applicable as alternatives for evaluating untested compounds. In this procedure, traditional approaches (e.g., QSAR) purely based on chemical structures have been replaced by newly designed data-driven and mechanism-driven modeling. The resulting models realize the concept of adverse outcome pathway (AOP), which can not only directly evaluate toxicity potentials of new compounds, but also illustrate relevant toxicity mechanisms. The recent advancement of computational toxicology in the big data era has paved the road to future toxicity testing, which will significantly impact on the public health.Keywords
Funding Information
- Colgate-Palmolive Company
- Center for Alternatives to Animal Testing, Johns Hopkins Bloomberg School of Public Health
- National Institute of Environmental Health Sciences (R15ES023148)
This publication has 127 references indexed in Scilit:
- Profiling 976 ToxCast Chemicals across 331 Enzymatic and Receptor Signaling AssaysChemical Research in Toxicology, 2013
- High-throughput genotoxicity assay identifies antioxidants as inducers of DNA damage response and cell deathProceedings of the National Academy of Sciences of the United States of America, 2012
- The future of toxicity testing: a focus on in vitro methods using a quantitative high-throughput screening platformDrug Discovery Today, 2010
- Trust, But Verify: On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling ResearchJournal of Chemical Information and Modeling, 2010
- An overview of the PubChem BioAssay resourceNucleic Acids Research, 2009
- Database resources of the National Center for Biotechnology InformationNucleic Acids Research, 2009
- PubChem: a public information system for analyzing bioactivities of small moleculesNucleic Acids Research, 2009
- Database resources of the National Center for Biotechnology InformationNucleic Acids Research, 2009
- Quantitative high-throughput screening: A titration-based approach that efficiently identifies biological activities in large chemical librariesProceedings of the National Academy of Sciences of the United States of America, 2006
- Gene Expression Omnibus: NCBI gene expression and hybridization array data repositoryNucleic Acids Research, 2002