在胆固醇病的预测模型中整合机理学和毒动力学信息
Pablo Rodríguez-Belenguer1,2, Victor Mangas-Sanjuan2,3, Emilio Soria-Olivas4
1Research Programme on Biomedical Informatics (GRIB), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Hospital del Mar Medical Research Institute, 08003 Barcelona, Spain.
Journal of chemical information and modeling
|September 3, 2023
概括
这项研究通过将多个定量结构-活性关系 (QSAR) 模型与药理动力学 (PK) 数据相结合,改善了药物安全性评估. 增强的方法更好地预测复杂的毒性,如胆固醇,比传统方法提供优势.
科学领域:
- 计算毒理学计算毒理学
- 药物安全性评估 药物安全性评估
- 药理动力学 药理动力学
背景情况:
- 在毒理学 (IST) 提供了成本高效和时间高效的替代方案,以传统的实验方法在药物开发.
- 定量结构-活动关系 (QSAR) 模型擅长预测简单的毒性终点,但与复杂的现象作斗争.
- 整合多个QSAR模型和药理动力学 (PK) 数据可以提高对复杂毒性的预测.
研究的目的:
- 开发和验证一种新的方法来预测复杂的毒性终点,通过结合多个QSAR模型和定量化体外到体外抽取 (QIVIVE).
- 评估拟议方法在预测胆固醇病的有效性,这是一个复杂的毒性终点.
- 将综合方法的预测性能与传统的直接QSAR模型进行比较,特别是在化学相似性较低的条件下.
主要方法:
- 开发一种混合建模方法,整合多种定量结构-活动关系 (QSAR) 模型.
- 纳入药理动力学 (PK) 信息,使用量化体外与体内外推算 (QIVIVE) 模型.
- 混合模型与直接QSAR模型的应用和比较,用于预测胆固醇,包括具有较低化合物相似性的场景.
主要成果:
- 与直接QSAR模型相比,拟议的方法表明,对预测胆固醇定位的灵敏度显著增加.
- 综合方法保持了优越的预测性能,即使查询组合与培训数据集相似度较低.
- 在现实的预测场景中观察到混合方法的明显优势.
结论:
- 结合的QSAR和QIVIVE方法提供了更好的预测复杂的毒性终点,如胆固醇.
- 这种方法比直接的QSAR模型具有显著的优势,特别是在具有挑战性的预测情况下.
- 拟议的方法有可能增强现有的药物安全性评估策略,并应用于其他毒性终点.
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