Investigating drug repositioning opportunities in FDA drug labels through topic modeling.
Investigating drug repositioning opportunities in FDA drug labels through topic modeling.
复制标题
DOI：
10.1186/1471-2105-13-s15-s6
复制
发表时间：
2012
影响因子：
3
通讯作者：
Tong W
中科院分区：
文献类型：
--
作者：
Bisgin H;Liu Z;Kelly R;Fang H;Xu X;Tong W
药物重新定位提供了一个机会，通过为现有药物找到新的用途来重振放缓的药物发现管道。我们的假设是，副作用相似的药物很可能对相同的疾病有效，因此可以通过找到美国食品和药物管理局(FDA)批准的药物标签中记录的副作用相似的药物对来确定重新定位的机会。药物标签上的安全信息通常是在临床试验中获得的，并随着药物上市后使用的观察而增加。因此，与传统的从头定位方法相比，我们的药物重新定位方法可以利用更全面的安全信息。根据《管制活动医学词典》(MedDRA)中出现在870种药品标签的盒装警告、警告和预防措施以及不良反应部分中的术语，构建了概率主题模型。通过使用主题建模确定了52个独特的主题，每个主题包含一组术语。由此产生的概率主题关联被用来衡量药物之间的距离(相似性)。通过比较一种药物及其最近的邻居(即药物对)在药物标签的适应症和用法部分中发现的常见适应症来评估所提出的模型的成功。如果一种药物有三个以上的适应症，该模型的召回率为75%，这意味着75%的药物对具有一个或多个共同的适应症。这明显高于随机选择达到的22%的召回率。此外，召回率随着药物适应症的增加而迅速增长，11种适应症的药物召回率达到84%。分析还表明，65种带有盒装警告的药物可能会被没有盒装警告的更安全的替代品取代，这种警告表明存在严重和可能危及生命的不良反应的重大风险。此外，我们确定了两组治疗药物(肌肉骨骼系统和全身使用的抗感染药物)，其中超过80%的药物具有潜在的替代药物，具有很高的意义。通过检查FDA批准的药品标签中的不良事件术语，主题建模可以成为识别重新定位机会的强大工具。拟议的框架不仅建议可以重新定位的药物，还提供了对重新定位药物的安全性的洞察。
Drug repositioning offers an opportunity to revitalize the slowing drug discovery pipeline by finding new uses for currently existing drugs. Our hypothesis is that drugs sharing similar side effect profiles are likely to be effective for the same disease, and thus repositioning opportunities can be identified by finding drug pairs with similar side effects documented in U.S. Food and Drug Administration (FDA) approved drug labels. The safety information in the drug labels is usually obtained in the clinical trial and augmented with the observations in the post-market use of the drug. Therefore, our drug repositioning approach can take the advantage of more comprehensive safety information comparing with conventional de novo approach. A probabilistic topic model was constructed based on the terms in the Medical Dictionary for Regulatory Activities (MedDRA) that appeared in the Boxed Warning, Warnings and Precautions, and Adverse Reactions sections of the labels of 870 drugs. Fifty-two unique topics, each containing a set of terms, were identified by using topic modeling. The resulting probabilistic topic associations were used to measure the distance (similarity) between drugs. The success of the proposed model was evaluated by comparing a drug and its nearest neighbor (i.e., a drug pair) for common indications found in the Indications and Usage Section of the drug labels. Given a drug with more than three indications, the model yielded a 75% recall, meaning 75% of drug pairs shared one or more common indications. This is significantly higher than the 22% recall rate achieved by random selection. Additionally, the recall rate grows rapidly as the number of drug indications increases and reaches 84% for drugs with 11 indications. The analysis also demonstrated that 65 drugs with a Boxed Warning, which indicates significant risk of serious and possibly life-threatening adverse effects, might be replaced with safer alternatives that do not have a Boxed Warning. In addition, we identified two therapeutic groups of drugs (Musculo-skeletal system and Anti-infective for systemic use) where over 80% of the drugs have a potential replacement with high significance. Topic modeling can be a powerful tool for the identification of repositioning opportunities by examining the adverse event terms in FDA approved drug labels. The proposed framework not only suggests drugs that can be repurposed, but also provides insight into the safety of repositioned drugs.