Laboratory of Pharmacoinformatics, Department of Pharmaceutical Sciences, Faculty of Pharmaceutical Sciences, Suzuka University of Medical Science, 3500-3 Minamitamagakichō, Suzuka, Mie Prefecture, 513-8670, Japan.
Daru. 2024 Jun;32(1):253-262. doi: 10.1007/s40199-024-00511-4. Epub 2024 Apr 5.
This study aims to develop an AI-based prediction model for injection drugs that cause anaphylactic shock using Japanese Real-World Data (JADER database) and chemical structure-based analysis.
Data sourced from the JADER database included adverse drug reaction reports from April 2004 to December 2020. Only drugs with an adverse reaction named "anaphylactic shock" were selected for analysis. For model building, various models were constructed to predict anaphylactic shock-inducing drugs, such as logistic regression, LASSO, XGBoost, RF, SVM, and NNW. These models used chemical properties and structural similarities as feature variables. Dimension reduction was applied using principal component analysis. The dataset was split into training (80%) and validation (20%) sets. Six different models were trained and optimized through fivefold cross-validation.
From April 2004 to December 2020, 947 drugs with the adverse reaction name "anaphylactic shock" were extracted from the JADER database. 320 drugs were excluded due to analytical challenges, and another 400 were removed due to their administration route. 227 drugs were finalized as target medicines. For model validation, the performance of each model was evaluated based on metrics like AUCs of ROC curve, sensitivity, and specificity. Additionally, two ensemble models, constructed from the six models were assessed using bootstrap sampling. Interestingly, it was identified that mepivacaine structural similarity had the highest importance in the final model.
The study successfully developed an AI-based prediction model for anaphylactic shock inducing-injection drugs. The model would offer potential for drug safety evaluation and anaphylactic shock risk assessment.
本研究旨在使用日本真实世界数据（JADER 数据库）和基于化学结构的分析方法，开发一种基于人工智能的预测注射类药物引发过敏反应的模型。
从 JADER 数据库中获取的数据包括 2004 年 4 月至 2020 年 12 月的药物不良反应报告。仅选择不良反应名称为“过敏性休克”的药物进行分析。在构建模型时，构建了各种模型来预测引发过敏性休克的药物，如逻辑回归、LASSO、XGBoost、RF、SVM 和 NNW。这些模型使用化学性质和结构相似性作为特征变量。使用主成分分析进行降维。数据集分为训练集（80%）和验证集（20%）。通过五重交叉验证训练和优化了六个不同的模型。
从 2004 年 4 月至 2020 年 12 月，从 JADER 数据库中提取了 947 种不良反应名称为“过敏性休克”的药物。由于分析挑战，320 种药物被排除在外，另有 400 种药物由于给药途径被排除在外。最终确定 227 种药物为目标药物。在模型验证方面，根据 ROC 曲线的 AUC、灵敏度和特异性等指标评估了每个模型的性能。此外，还使用 bootstrap 采样评估了由六个模型构建的两个集成模型。有趣的是，发现甲哌卡因结构相似性在最终模型中具有最高的重要性。
本研究成功开发了一种基于人工智能的预测引发过敏性休克的注射类药物的模型。该模型将为药物安全性评估和过敏性休克风险评估提供潜在的帮助。
