Ma Junlong, Chen Heng, Sun Ji, Huang Juanjuan, He Gefei, Yang Guoping
Center of Clinical Pharmacology, Third Xiangya Hospital, Central South University, Changsha, Hunan, China.
Department of Pharmacy, The First Hospital of Changsha, Central South University, Changsha, Hunan, China.
BMC Med Res Methodol. 2024 Dec 20;24(1):312. doi: 10.1186/s12874-024-02443-8.
Liver injury from drug-drug interactions (DDIs), notably with anti-tuberculosis drugs such as isoniazid, poses a significant safety concern. Electronic medical records contain comprehensive clinical information and have gained increasing attention as a potential resource for DDI detection. However, a substantial portion of adverse drug reaction (ADR) information is hidden in unstructured narrative text, which has yet to be efficiently harnessed, thereby introducing bias into the research. There is a significant need for an efficient framework for the DDI assessment.
Using a Chinese natural language processing (NLP) model, we extracted 25,130 adverse drug reaction (ADR) records, dividing them into sets for training an automated normalization model. The trained models, in conjunction with liver function laboratory tests, were used to thoroughly and efficiently identify liver injury cases. Ultimately, we applied a case-control study design to detect DDI signals increasing isoniazid's liver injury risk.
The Logistic Regression model demonstrated stable and superior performance in classification task. Based on laboratory criteria and NLP, we identified 128 liver injury cases among a cohort of 3,209 patients treated with isoniazid. Preliminary screening of 113 drug combinations with isoniazid highlighted 20 potential signal drugs, with antibacterials constituting 25%. Sensitivity analysis confirmed the robustness of signal drugs, especially in cardiac therapy and antibacterials.
Our NLP and machine learning approach effectively identifies isoniazid-related DDIs that increase the risk of liver injury, identifying 20 signal drugs, mainly antibacterials. Further research is required to validate these DDI signals.
药物相互作用（DDIs）导致的肝损伤，尤其是与异烟肼等抗结核药物的相互作用，引发了重大的安全担忧。电子病历包含全面的临床信息，作为药物相互作用检测的潜在资源，越来越受到关注。然而，相当一部分药物不良反应（ADR）信息隐藏在非结构化的叙述性文本中，尚未得到有效利用，从而给研究带来偏差。因此，迫切需要一个高效的药物相互作用评估框架。
我们使用中文自然语言处理（NLP）模型提取了25130条药物不良反应（ADR）记录，并将其分为用于训练自动标准化模型的数据集。经过训练的模型与肝功能实验室检测相结合，被用于全面、高效地识别肝损伤病例。最终，我们采用病例对照研究设计来检测增加异烟肼肝损伤风险的药物相互作用信号。
逻辑回归模型在分类任务中表现出稳定且优异的性能。基于实验室标准和自然语言处理，我们在3209例接受异烟肼治疗的患者队列中识别出128例肝损伤病例。对113种与异烟肼联用的药物组合进行初步筛选，突出了20种潜在的信号药物，其中抗菌药物占25%。敏感性分析证实了信号药物的稳健性，尤其是在心脏治疗和抗菌药物方面。
我们的自然语言处理和机器学习方法有效地识别了增加肝损伤风险的与异烟肼相关的药物相互作用，确定了20种信号药物，主要是抗菌药物。需要进一步研究来验证这些药物相互作用信号。
