Shirakuni Yuko, Okamoto Kousuke, Uejima Etuko, Inui Shigeki, Takahara Jun-Ichi, Ohgaru Takanori, Yamasaki Hiroyuki, Tian Yushi, Kawashita Norihito, Inoue Ran, Yasunaga Teruo, Takagi Tatsuya
1 Graduate School of Pharmaceutical Sciences, Osaka University, Osaka, Japan.
2 Department of Hospital Pharmacy Education, Osaka University, Osaka, Japan.
Ther Innov Regul Sci. 2013 Mar;47(2):235-241. doi: 10.1177/0092861512460759.
This study aimed to determine the potentially severe chemical properties of drugs that can cause adverse drug reactions (ADRs) such as erythema multiforme (EM), Stevens-Johnson syndrome (SJS), and toxic epidermal necrolysis (TEN) by using a data mining method. The study data were extracted from the Adverse Event Reporting System database of the FDA. EM was considered a mild reaction, and SJS and TEN were considered severe reactions. In this study, a new concept termed the "risk of aggravation" (ROA) was defined as whether a certain drug is more likely to cause severe adverse reactions than mild ones. Partial least squares and logistic regression analysis were applied using binary response variable ROAs. These analyses correctly predicted 50 of the 72 drugs associated with SJS and/or TEN and 28 of the 38 drugs associated with EM using binary chemical descriptors that are the same as those using the metric chemical descriptors.
本研究旨在通过数据挖掘方法，确定可能导致多形红斑（EM）、史蒂文斯-约翰逊综合征（SJS）和中毒性表皮坏死松解症（TEN）等药物不良反应（ADR）的药物潜在严重化学性质。研究数据取自美国食品药品监督管理局（FDA）的不良事件报告系统数据库。EM被视为轻度反应，SJS和TEN被视为严重反应。在本研究中，一个新的概念“加重风险”（ROA）被定义为某种药物是否比轻度不良反应更有可能引起严重不良反应。使用二元响应变量ROA进行偏最小二乘法和逻辑回归分析。这些分析使用与使用度量化学描述符相同的二元化学描述符，正确预测了72种与SJS和/或TEN相关药物中的50种，以及38种与EM相关药物中的28种。
