2020
DOI: 10.1021/acs.chemrestox.9b00497
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Going All In: A Strategic Investment in In Silico Toxicology

Abstract: As vast numbers of new chemicals are introduced to market annually, we are faced with the grand challenge of protecting humans and the environment while minimizing economically and ethically costly animal testing. In silico models promise to be the solution we seek, but we find ourselves at crossroads of future development efforts that would ensure standalone applicability and reliability of these tools. A conscientious effort that prioritizes experimental testing to support the needs of in silico models (vers… Show more

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Cited by 29 publications
(67 citation statements)
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“…Another more effective way to build a model relied on deeply understanding the biological mechanism to predict the biochemical processes and the bioactivity of the novel compounds. The model based on mechanism does not need a large amount of training set, but it needs highly accurate experimental data ( Kostal and Voutchkova-Kostal, 2020 ). Accordingly, it is of necessity to implement data curation prior to model development by removing those assay data obtained from impurity or mixture to maintain data integrity.…”
Section: Future Perspectivesmentioning
confidence: 99%
“…Another more effective way to build a model relied on deeply understanding the biological mechanism to predict the biochemical processes and the bioactivity of the novel compounds. The model based on mechanism does not need a large amount of training set, but it needs highly accurate experimental data ( Kostal and Voutchkova-Kostal, 2020 ). Accordingly, it is of necessity to implement data curation prior to model development by removing those assay data obtained from impurity or mixture to maintain data integrity.…”
Section: Future Perspectivesmentioning
confidence: 99%
“…As a result, chemicals can be grouped into toxicity categories based on their structural similarities. QSAR models are very useful for quickly grouping toxicants based on their physicochemical properties and their correlation within large datasets, but they neglect specificity and complexity of molecular interactions, which introduces significant uncertainty into the effective prediction of toxicity [123].…”
Section: In Silico Approaches Applying Metabolomicsmentioning
confidence: 99%
“…Further, these methods offer valuable mechanistic information for training and testing a new generation of in silico models that use big data or modeling of molecular interactions to identify problematic chemicals and design less hazardous alternatives. 9 Some of the most active comparative and predictive toxicology efforts in the US include the federal Toxicology in the 21 st Century (Tox21) and Toxicity Forecaster (ToxCast) programs (http://www. epa.gov/chemical-research/toxicologytesting-21st-century-tox21).…”
Section: Advances In Comparative and Predictive Toxicologymentioning
confidence: 99%