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首页> 外文期刊>Journal of medical Internet research >The Anatomy of the SARS-CoV-2 Biomedical Literature: Introducing the CovidX Network Algorithm for Drug Repurposing Recommendation
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The Anatomy of the SARS-CoV-2 Biomedical Literature: Introducing the CovidX Network Algorithm for Drug Repurposing Recommendation

机译:SARS-COV-2生物医学文献的解剖学:引入Covidx网络算法用于药物重新施加推荐

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Background 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. Objective 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. Methods 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. Results 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. Conclusions 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.
机译:背景技术由Covid-19大流行和急迫发现抗病毒药物,我们探讨了SARS-COV-2生物医学出版物的景观来识别潜在的治疗方法。目的本研究的目的是鉴定对冠状病毒疾病大流行有益的标签药物,提出一种名为Covidx的新型排名算法,以推荐用于潜在的药物的潜在药物,并验证有可用的药物知识的基于文献的结果临床试验。实现这种目标的方法,我们应用了自然语言处理技术来鉴定药物和链接实体(例如,疾病,基因,蛋白质,化学化合物)。当这些实体链接时,它们形成了一个可以使用网络科学工具进一步探索的地图。 Covidx算法基于我们称为“多样性”的概念。通过测量使用各种生物实体计算“多样化”药物(无论每个类别中实际实例的基数)计算给定药物的分集分数。该算法验证当前正在开放临床试验中正在调查的药物的排名和奖项。开放式临床试验背后的理由是提供了PubMed结果的验证机制。这确保了快速发展这种疾病的日期证据。分析的生物医学文献结果,该算法确定了30个可能的候选药物用于重新施加,相应地排名,并验证了来自临床试验的证据的排名结果。根据我们的算法的前10名候选者是羟氯喹,阿奇霉素,氯喹,Ritonavir,氯沙坦,Remdesivir,FaviPiravir,雷哌瓦啉,雷帕霉素和紫红素二盐酸盐。结论排名表明,在识别可以重新培养的药物中表明一致性和承诺。然而,我们认为,全部治疗是一种多方面,佐剂方法,可能需要同时服用多种药物。

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