Abstract：
The aim of this paper was to provide a proof of concept demonstrating that molecular modelling methodologies can be employed as a part of an integrated strategy to support toxicity prediction consistent with the mode of action/adverse outcome pathway (MoA/AOP) framework. To illustrate the role of molecular modelling in predictive toxicology, a case study was undertaken in which molecular modelling methodologies were employed to predict the activation of the peroxisome proliferator-activated nuclear receptor γ (PPARγ) as a potential molecular initiating event (MIE) for liver steatosis. A stepwise procedure combining different in silico approaches (virtual screening based on docking and pharmacophore filtering, and molecular field analysis) was developed to screen for PPARγ full agonists and to predict their transactivation activity (EC50). The performance metrics of the classification model to predict PPARγ full agonists were balanced accuracy=81%, sensitivity=85% and specificity=76%. The 3D QSAR model developed to predict EC50 of PPARγ full agonists had the following statistical parameters: q2cv=0.610, Nopt=7, SEPcv=0.505, r2pr=0.552. To support the linkage of PPARγ agonism predictions to prosteatotic potential, molecular modelling was combined with independently performed mechanistic mining of available in vivo toxicity data followed by ToxPrint chemotypes analysis. The approaches investigated demonstrated a potential to predict the MIE, to facilitate the process of MoA/AOP elaboration, to increase the scientific confidence in AOP, and to become a basis for 3D chemotype development.
摘要：
本文的目的是提供一个概念证明，证明分子建模方法可以用作综合策略的一部分，以支持与作用模式/不良结果途径（MoA/AOP）框架一致的毒性预测。为了说明分子建模在预测毒理学中的作用，我们进行了一项案例研究,采用分子建模方法预测过氧化物酶体增殖物激活的核受体γ(PPARγ)的激活是肝脏脂肪变性的潜在分子起始事件(MIE).结合不同的计算机方法的逐步程序（基于对接和药效团过滤的虚拟筛选，和分子场分析）用于筛选PPARγ完全激动剂并预测其反式激活活性（EC50）。分类模型预测PPARγ完全激动剂的性能指标是平衡准确率=81%，敏感性=85%，特异性=76%。用于预测PPARγ完全激动剂EC50的3DQSAR模型具有以下统计参数：q2cv=0.610，Nopt=7，SEPcv=0.505，r2pr=0.552。为了支持PPARγ激动预测与前列腺增生潜能的联系，将分子建模与独立进行的有效体内毒性数据的机械挖掘相结合,然后进行ToxPrint化学型分析.所研究的方法证明了预测MIE的潜力，为了促进MoA/AOP阐述的过程，为了增加对AOP的科学信心，并成为3D化学型开发的基础。
