Unité Modèles pour l'Ecotoxicologie et la Toxicologie (METO), Institut National de l'Environnement Industriel et des Risques (INERIS), Verneuil en Halatte, France.
Currently at S-IN Soluzioni Informatiche S.r.l., Vicenza, Italy.
Mol Inform. 2021 Mar;40(3):e2000072. doi: 10.1002/minf.202000072. Epub 2020 Nov 2.
The adipose tissue:blood partition coefficient is a key-endpoint to predict the pharmacokinetics of chemicals in humans and animals, since other organ:blood affinities can be estimated as a function of this parameter. We performed a search in the literature to select all the available rat in vivo data. This approach resulted into two improvements to existing models: a homogeneous definition of the endpoint and an expanded data collection. The resulting dataset was used to develop QSAR models as a function of linear and non-linear algorithms. Several applicability domain definitions were assessed and the definition corresponding to a good balance between performance and coverage was retained. We assessed the pertinence of combining single models into integrated approaches to increase the accuracy in predictions. The best integrated model outperformed the single models and it was characterized by an external mean absolute error (MAE) equal to 0.26, while preserving an adequate coverage (84 %). This performance is comparable to experimental variability and it highlights the pertinence of the integrated model.
脂肪组织与血液分配系数是预测化学物质在人类和动物体内药代动力学的关键终点,因为其他器官与血液的亲和力可以作为该参数的函数进行估算。我们在文献中进行了检索,以选择所有可用的大鼠体内数据。这种方法对现有模型进行了两项改进:终点的同质定义和扩展的数据收集。所得数据集用于开发 QSAR 模型,作为线性和非线性算法的函数。评估了几种适用性域定义,并保留了一种在性能和覆盖范围之间取得良好平衡的定义。我们评估了将单个模型组合成集成方法以提高预测准确性的相关性。最佳集成模型优于单个模型,其特征在于外部平均绝对误差(MAE)等于 0.26,同时保持适当的覆盖率(84%)。这种性能与实验变异性相当,突出了集成模型的相关性。