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Updated: Oct 5, 2025

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
Published on: August 28, 2019
Using adverse outcome pathways to contextualise (Q)SAR predictions for reproductive toxicity - A case study with
Alun Myden1, Emma Hill1, Adrian Fowkes1
1Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS, United Kingdom.
Abstract:
The development and application of (quantitative) structure-activity relationship ((Q)SAR) models for reproductive toxicology remains challenging, given the complexity of the endpoint and the risks associated with subsequent decision making. Adverse outcome pathways (AOPs) organise knowledge and provide context of model outputs, aiding risk assessors' decision making. Using aromatase as an example, we demonstrate how AOPs can be used to contextualise a variety of (Q)SAR approaches. AOPs stemming from aromatase inhibition - leading to adverse outcomes of regulatory significance - were synthesised and annotated with relevant assays, assay data and (Q)SAR models. The resulting framework enabled the deployment of different types of (Q)SAR models that predict for key events along the pathway. The use of models for molecular initiating events enables relevant knowledge to span a wider area of chemical space - compared to using models trained solely on in vivo toxicity data. Utilising such methods, alongside additional assay data and exposure information, could lead to improved risk assessment strategies during compound prioritisation and labelling.
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