In Silico Prediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects

In silico methods to predict toxicity include the use of (Quantitative) Structure-Activity Relationships ((Q)SARs) as well as grouping (category formation) allowing for read-across. A challenging area for in silico modelling is the prediction of chronic toxicity and the No Observed (Adverse) Effect...

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Bibliographic Details
Published in:Toxicological research (Seoul) 2017, 33(3), , pp.173-182
Main Authors: Cronin, Mark T. D., Enoch, Steven J., Mellor, Claire L., Przybylak, Katarzyna R., Richarz, Andrea-Nicole, Madden, Judith C.
Format: Article
Language:English
Subjects:
Biomedicine
Invited Review
Pharmacology/Toxicology
예방의학
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Summary:In silico methods to predict toxicity include the use of (Quantitative) Structure-Activity Relationships ((Q)SARs) as well as grouping (category formation) allowing for read-across. A challenging area for in silico modelling is the prediction of chronic toxicity and the No Observed (Adverse) Effect Level (NO(A)EL) in particular. A proposed solution to the prediction of chronic toxicity is to consider organ level effects, as opposed to modelling the NO(A)EL itself. This review has focussed on the use of structural alerts to identify potential liver toxicants. In silico profilers, or groups of structural alerts, have been developed based on mechanisms of action and informed by current knowledge of Adverse Outcome Pathways. These profilers are robust and can be coded computationally to allow for prediction. However, they do not cover all mechanisms or modes of liver toxicity and recommendations for the improvement of these approaches are given.
ISSN:1976-8257
2234-2753
DOI:10.5487/TR.2017.33.3.173