用于预测口服生物可用性和分布体积的QSAR模型及其应用于绘制内分泌干扰物的TK空间的应用
Guillaume Ollitrault1, Marco Marzo2, Alessandra Roncaglioni2
1Inserm U1133, CNRS UMR 8251, Université de Paris Cité, 75013 Paris, France.
Journal of xenobiotics
|October 28, 2025
概括
开发了定量结构-活性关系 (QSAR) 模型,以预测毒动力学 (TK) 特性,如口服生物可用性和分布量. 这些模型确定了潜在的内分泌干扰化学物质 (EDC),具有对人类健康的高风险TK概况.
科学领域:
- 计算毒理学计算毒理学
- 药理动力学 药理动力学
- 药物发现 药物发现
背景情况:
- 毒动力学 (TK) 特性对于化学风险评估和药物开发至关重要.
- 了解化学物质在人体中的命运 (TK概况) 告知了安全评估.
- 定量结构-活动关系 (QSAR) 模型提供了TK属性的计算预测.
研究的目的:
- 开发和比较QSAR模型,以预测稳定状态 (VDss) 的口服生物可用性和分布量.
- 应用这些预测模型来识别潜在的内分泌干扰化学物质 (EDCs),其中包括涉及TK的个人资料.
主要方法:
- 精心策划的大型数据集 (1712种化学物质用于口服生物可用性,1591种用于VDss).
- 使用各种机器学习算法开发和比较回归和分类QSAR模型.
- 使用Q2F3和几何平均折叠误差 (GMFE) 等指标评估模型性能.
主要成果:
- 口服生物可用性的最佳回归模型实现了0.34的Q2F3 (R-CatBoost).
- 对VDss的最佳回归模型产生了2.35 (R-RF) 的GMFE.
- 模型被应用用于识别具有潜在危险的TK配置文件的EDC.
结论:
- 开发了强大的QSAR模型来预测关键的TK特性 (口服生物可用性,VDss).
- 确定了具有高风险TK配置文件的特定EDC,需要进一步调查.
- 提供了对影响EDC的TK特性结构因素的见解.
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