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Investigating drug repositioning opportunities in FDA drug labels through topic modeling

机译:通过主题建模研究FDA药品标签中的药物重新定位机会

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BackgroundDrug 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.MethodA 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.ResultsGiven 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.ConclusionTopic 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.
机译:BackgroundDrug的重新定位提供了一个机会,可以通过寻找当前现有药物的新用途来重振缓慢的药物发现流程。我们的假设是,具有相似副作用的药物可能对同一疾病有效,因此可以通过找到美国食品药品管理局(FDA)批准的药物标签中记载的具有相似副作用的药物对来确定重新定位的机会。药物标签中的安全性信息通常是在临床试验中获得的,并随着药物在上市后使用的观察而增加。因此,与常规的从头方法相比,我们的药物重新定位方法可以利用更全面的安全信息。方法根据盒装警告,警告中出现的《管制活动医学词典》(MedDRA)中的术语,构建概率主题模型。 870种药物的标签的“注意事项”和“注意事项”和“不良反应”部分。通过使用主题建模来识别52个唯一主题,每个主题包含一组术语。由此产生的概率主题关联用于衡量药物之间的距离(相似性)。通过比较一种药物及其最接近的邻居(即一对药物)在药物标签的“适应症和使用情况”部分中发现的常见适应症,评估了该模型的成功。结果给出具有三种以上适应症的药物,该模型得出了75%的召回率,这意味着75%的药物对具有一种或多种常见适应症。这明显高于通过随机选择获得的22%的查全率。此外,召回率随着药物适应症数量的增加而迅速增加,对于具有11种适应症的药物,召回率达到84%。分析还表明,有65种带框警告的药物表明有严重的严重危险甚至可能威胁生命的不良反应,可以用没有框式警告的更安全的替代方法代替。此外,我们确定了两种治疗药物(肌肉骨骼系统药物和全身抗感染药物),其中超过80%的药物具有潜在的替代作用,具有很高的意义。结论主题建模可以作为识别重新定位的有力工具通过检查FDA批准的药品标签中的不良事件术语来找到机会。拟议的框架不仅建议可以重新定位的药物,还可以洞察重新定位的药物的安全性。

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