Gates Lyndsey Elaine, Hamed Ahmed Abdeen
School of Nursing, Norwich University, Northfield, VT, United States.
School of Cybersecurity, Data Science, and Computing, Norwich University, Northfield, VT, United States.
J Med Internet Res. 2020 Aug 20;22(8):e21169. doi: 10.2196/21169.
Driven by the COVID-19 pandemic and the dire need to discover an antiviral drug, we explored the landscape of the SARS-CoV-2 biomedical publications to identify potential treatments.
The aims of this study are to identify off-label drugs that may have benefits for the coronavirus disease pandemic, present a novel ranking algorithm called CovidX to recommend existing drugs for potential repurposing, and validate the literature-based outcome with drug knowledge available in clinical trials.
To achieve such objectives, we applied natural language processing techniques to identify drugs and linked entities (eg, disease, gene, protein, chemical compounds). When such entities are linked, they form a map that can be further explored using network science tools. The CovidX algorithm was based upon a notion that we called "diversity." A diversity score for a given drug was calculated by measuring how "diverse" a drug is calculated using various biological entities (regardless of the cardinality of actual instances in each category). The algorithm validates the ranking and awards those drugs that are currently being investigated in open clinical trials. The rationale behind the open clinical trial is to provide a validating mechanism of the PubMed results. This ensures providing up to date evidence of the fast development of this disease.
From the analyzed biomedical literature, the algorithm identified 30 possible drug candidates for repurposing, ranked them accordingly, and validated the ranking outcomes against evidence from clinical trials. The top 10 candidates according to our algorithm are hydroxychloroquine, azithromycin, chloroquine, ritonavir, losartan, remdesivir, favipiravir, methylprednisolone, rapamycin, and tilorone dihydrochloride.
The ranking shows both consistency and promise in identifying drugs that can be repurposed. We believe, however, the full treatment to be a multifaceted, adjuvant approach where multiple drugs may need to be taken at the same time.
在新冠疫情的推动以及发现抗病毒药物的迫切需求下,我们探索了严重急性呼吸综合征冠状病毒2(SARS-CoV-2)生物医学出版物的领域,以确定潜在的治疗方法。
本研究的目的是识别可能对冠状病毒病大流行有益的非标签药物,提出一种名为CovidX的新型排名算法,以推荐现有药物用于潜在的重新利用,并利用临床试验中可用的药物知识验证基于文献的结果。
为实现这些目标,我们应用自然语言处理技术来识别药物和相关实体(如疾病、基因、蛋白质、化合物)。当这些实体相互关联时,它们会形成一个图谱,可以使用网络科学工具进一步探索。CovidX算法基于我们称之为“多样性”的概念。通过测量一种药物使用各种生物实体的“多样性”程度(无论每个类别中实际实例的数量)来计算给定药物的多样性得分。该算法验证排名,并奖励那些正在开放临床试验中研究的药物。开放临床试验背后的基本原理是为PubMed结果提供一种验证机制。这确保了提供关于这种疾病快速发展的最新证据。
通过对生物医学文献的分析,该算法识别出30种可能重新利用的候选药物,对它们进行了相应排名,并根据临床试验证据验证了排名结果。根据我们的算法,排名前十的候选药物是羟氯喹、阿奇霉素、氯喹、利托那韦、氯沙坦、瑞德西韦、法匹拉韦、甲泼尼龙、雷帕霉素和盐酸替洛隆。
该排名在识别可重新利用的药物方面既显示出一致性又有前景。然而,我们认为全面的治疗是一种多方面的辅助方法,可能需要同时服用多种药物。