Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS, United Kingdom.
Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS, United Kingdom.
Reprod Toxicol. 2022 Mar;108:43-55. doi: 10.1016/j.reprotox.2022.01.004. Epub 2022 Jan 25.
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.
(定量)构效关系 ((Q)SAR) 模型在生殖毒理学中的开发和应用仍然具有挑战性,因为终点的复杂性以及随后的决策所带来的风险。不良结局途径 (AOP) 组织知识并提供模型输出的背景,有助于风险评估人员的决策。我们以芳香酶为例,展示了如何使用 AOP 将各种 (Q)SAR 方法进行情境化处理。芳香酶抑制导致具有监管意义的不良结局的 AOP 被合成并标记了相关的检测、检测数据和 (Q)SAR 模型。由此产生的框架使部署不同类型的 (Q)SAR 模型成为可能,这些模型可预测途径中的关键事件。与仅使用体内毒性数据训练的模型相比,使用模型进行分子起始事件预测可使相关知识跨越更广泛的化学空间。在化合物优先级排序和标记过程中,结合其他检测数据和暴露信息使用此类方法,可能会改善风险评估策略。