使用FDA不良事件报告系统数据库和机器学习开发与药物相关的骨瘤的预测模型
Shinya Toriumi1,2, Komei Shimokawa2, Munehiro Yamamoto3
1Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Kiyose 204-8588, Japan.
Pharmaceuticals (Basel, Switzerland)
|March 27, 2025
概括
一个新的模型使用药物化学结构预测与药物相关的下巴骨髓缩 (MRONJ). 极地表面积高的药物是潜在的MRONJ诱导剂,有助于药物安全性评估.
科学领域:
- 药物监督 药物监督 药物监督
- 药用化学 医学化学
- 计算毒理学计算毒理学
背景情况:
- 与药物相关的下巴骨硬化 (MRONJ) 是一种严重的药物不良事件.
- 预测MRONJ风险对于患者安全至关重要.
- 目前的预测方法缺乏基于结构的洞察力.
研究的目的:
- 开发基于化学结构的MRONJ诱导的预测模型.
- 为了确定与MRONJ相关的关键化学描述符.
- 为了利用机器学习来评估药物安全性.
主要方法:
- 来自FAERS的4815种药物的定量结构-活性关系 (QSAR) 分析.
- 机器学习算法 (随机森林,梯度增强,人工神经网络) 被比较.
- 化学结构描述符被计算并以预测能力排名.
主要成果:
- 使用八个描述符的人工神经网络模型实现了高ROC值 (0.778).
- 极地表面总面积 (ASA_P) 是一个显著的描述因素,MRONJ阳性药物显示较高的值.
- 最终的模型实现了0.693平衡精度和0.852的特异性.
结论:
- 使用化学结构信息开发了一个新的MRONJ预测模型.
- 极地表面积是与MRONJ诱导相关的关键属性.
- 这种方法提高了药物安全性评估,并简化了药物查.
关键词:
美国FDA不良事件报告系统数据库 (FAERS)人工神经网络的人工神经网络双酸盐是一种双酸盐.不成比例性分析分析流行病学研究 流行病学研究在分析中,分析.机器学习是机器学习.与药物相关的下巴骨硬化 (MRONJ)量化结构与活动关系 (QSAR)自发报告数据库自发报告数据库更多相关视频
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