中国药物警戒 ›› 2025, Vol. 22 ›› Issue (1): 16-22.
DOI: 10.19803/j.1672-8629.20240807

• 药源性血小板减少研究专栏 • 上一篇    下一篇

基于真实世界数据与定量构效关系的儿童药源性免疫性血小板减少症研究

聂晓璐1,2,3, 赵厚宇4, 霍东辉5,6, 阎爱侠6, 孙凤2,3, 彭晓霞1,3, 倪鑫7#, 詹思延2,*   

  1. 1国家儿童医学中心,首都医科大学附属北京儿童医院临床流行病与循证医学中心, 100045;
    2北京大学公共卫生学院流行病与卫生统计学系,北京 100091;
    3海南省真实世界数据研究院,海南 琼海 571437;
    4重庆大学医学院,重庆 400030;
    5中石化（大连）石油化工研究院有限公司,辽宁 大连 116045;
    6北京化工大学生命科学与技术学院,北京 100029;
    7国家儿童医学中心,首都医科大学附属北京儿童医院办公室,北京 100045
  • 收稿日期:2024-10-21 出版日期:2025-01-15 发布日期:2025-01-22
  • 通讯作者: *詹思延,教授·博导,药物流行病学与循证医学。E-mail: siyan-zhan@bjmu.edu.cn#为共同通信作者。
  • 作者简介:聂晓璐,女,副研究员,药物流行病学与循证医学。
  • 基金资助:
    国家自然科学基金资助项目(82204149); 北京市医院管理中心“青苗”计划专项经费资助（QML20231204）; 北京儿童医院科研苗圃计划（3-1-014-01-04）; 海南博鳌乐城国际医疗旅游先行区真实世界研究专项计划项目(HNLC 2022RWS015)

Pediatric Drug-Induced Immune Thrombocytopenia Based on Real World Data and Quantitative Structure-Activity Relationship

NIE Xiaolu1,2,3, ZHAO Houyu4, HUO Donghui5,6, YAN Aixia6, SUN Feng2,3, PENG Xiaoxia1,3, NI Xin7#, ZHAN Siyan2,*   

  1. 1Center for Clinical Epidemiology, Evidence-Based Medicine, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China;
    2Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100091, China;
    3Hainan Institute of Real World Data, Qionghai Hainan 571437, China;
    4School of Medicine, Chongqing University, Chongqing 400030, China;
    5SINOPEC (Dalian) Research Institute of Petroleum and Petrochemicals Co., Ltd., Dalian Liaoning 116045, China;
    6College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China;
    7Office of Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
  • Received:2024-10-21 Online:2025-01-15 Published:2025-01-22

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摘要： 目的 利用医院电子病历数据和药物的构效关系开展儿童药源性免疫性血小板（DIIT）风险信号挖掘及毒性评价研究。方法 使用北京儿童医院2009年1月1日至2020年12月31日电子病历数据开展真实世界研究,利用改良的实验室极端异常比例失衡法挖掘儿童DIIT潜在信号;基于多来源信息构建DIIT基准数据库,并利用3种机器学习算法支持向量机、随机森林和XGBoost算法和药物分子描述符（ECFP4和CORINA）,构建定量构效关系（QSAR）模型,基于药物理化性质和分子指纹开展DIIT的毒性评价研究。结果 基于电子病历数据挖掘 18个儿童DITP阳性信号药物。其中制霉菌素和拉氧头孢钠是儿童和成人的2个新的DITP阳性信号（DITP风险分别为OR：1.75, 95% CI：1.37~2.22和OR：1.61, 95% CI：1.38~1.88）,另发现6个儿童尚未报告的新的阳性信号药物：亚胺培南、替考拉宁、夫西地酸、头孢唑肟钠、头孢他啶和头孢吡肟。基于1 319种化合物所构建的9个不同的QSAR模型中效果最优的前3个模型分别为SVM-ECFP4+CORINA模型、RF-ECFP4+CORINA模型及XGBoost-ECFP4模型,其外部验证集上曲线下面积依次为0.747、0.732及0.712。经3个最优模型所组成的共识模型可准确预测出在第一步中发现的7种潜在阳性信号。结论 真实世界信号挖掘方法与QSAR模型相结合可进一步完善我国药品上市后安全性评价的方法学体系。

关键词: 药源性血小板减少症, 儿童, 药品不良反应, 安全性评价, 信号挖掘, 定量构效关系, 真实世界数据, 制霉菌素, 拉氧头孢钠

Abstract: Objective To conduct risk signal mining and toxicity evaluation related to drug-induced immune thrombocytopenia (DIIT) . Methods A real-world study was conducted using the electronic medical record data from Beijing Children's Hospital spanning from 2009 to 2020. Potential DIIT signals in children were mined using a modified laboratory extreme abnormal proportion imbalance method. Based on multi-source information, a DIIT benchmark database was constructed. Three machine learning algorithms-support vector machine (SVM), random forest (RF), and XGBoost-and drug molecular descriptors (ECFP4 and CORINA) were employed to establish quantitative structure-activity relationship (QSAR) models. Toxicity evaluation research on DIIT was conducted based on the physicochemical properties and molecular fingerprints of drugs. Results Eighteen positive signal drugs for pediatric drug-induced thrombocytopenia (DITP) were identified from the electronic medical record data. Among them, nystatin and latamoxef sodium emerged as two new positive DITP signals in both children and adults (DITP risks of OR: 1.75, 95%CI: 1.37 to 2.22 and OR: 1.61, 95%CI: 1.38 to 1.88, respectively). Additionally, six new positive signal drugs not previously reported in children were discovered: imipenem, teicoplanin, fusidic acid, cefotaxime sodium, ceftazidime, and cefepime. Among the nine different QSAR models constructed based on 1 319 compounds, the top three models with optimal performance were the SVM-ECFP4+CORINA model, the RF-ECFP4+CORINA model, and the XGBoost-ECFP4 model, with the area under the curve (AUC) values on the external validation set of 0.747, 0.732, and 0.712, respectively. A consensus model composed of these three optimal models accurately predicted the seven potential positive signals identified in the first step. Conclusion Combining real-world signal mining methods with QSAR models can enhance the methodological framework for post-marketing drug safety evaluation in China.

Key words: Drug-Induced Immune Thrombocytopenia (DIIT), Pediatric, Adverse Drug Reactions, Safety Evaluation, Signal Detection, Quantitative Structure-Activity Relationship Study, Real-World, Nystatin, Latamoxef Sodium

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