综合性药物监测和基于人工智能的框架揭示了青少年异常性关节炎中潜在的药物触发因素
Qiang Luo1, Yuxiao Chen1, Dawei Liu1
1Department of Rheumatology and Immunology, Children's Hospital of Chongqing Medical University, Chongqing Key Laboratory of Child Rare Diseases in Infection and Immunity, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China.
兰索普拉可能会使儿童的青少年异常性关节炎 (JIA) 恶化. 这项研究使用药物监测和机器学习来识别药物风险,突出了需要小心地使用质子抑制剂在风险青少年.
科学领域:
- 药物监测和计算毒理学
- 儿科风湿病学和药物安全性
背景情况:
- 青少年无形性关节炎 (JIA) 管理通常涉及长期药物治疗.
- 人们担心某些药物可能会引发或加剧JIA.
- 缺乏系统的方法来检测儿童中药物诱导的JIA信号.
研究的目的:
- 使用大规模数据识别与JIA潜在的药物关联.
- 开发和验证用于预测高风险化合物的机器学习模型.
- 研究将非抗风湿药物与JIA恶化联系起来的分子机制.
主要方法:
- 使用四个不成比例算法选了1000万份FAERS报告.
- 用三种机器学习模型 (DMPNN,GCN,SVM) 来进行风险分层.
- 使用转录组数据 (批量和单细胞RNA-seq) 来自JIA患者的验证结果.
主要成果:
- 在算法和ML模型中确定了一致的药物-JIA信号.
- 兰索普拉和阿里皮普拉显示出强烈的JIA关联信号.
- 毒基因组分析揭示了与JIA途径重叠的免疫毒性模式.
- 兰索普拉的特征在全身性JIA患者的单细胞中得到了丰富.
结论:
- 兰索普拉被确定为系统性JIA的潜在触发物.
- 质子抑制剂在患有自身免疫风险的儿童中需要谨慎使用.
- 建立了一个罕见疾病药物监测框架.
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