GIGA-R Medical Genomics - BIO3 Systems Genomics, University of Liège, Liège, Belgium.
Laboratory of Pharmaceutical Analytical Chemistry, CIRM, University of Liège, Liège, Belgium.
PLoS One. 2022 May 24;17(5):e0267095. doi: 10.1371/journal.pone.0267095. eCollection 2022.
The outbreak of coronavirus health issues caused by COVID-19(SARS-CoV-2) creates a global threat to public health. Therefore, there is a need for effective remedial measures using existing and approved therapies with proven safety measures has several advantages. Dexamethasone (Pubchem ID: CID0000005743), baricitinib(Pubchem ID: CID44205240), remdesivir (PubchemID: CID121304016) are three generic drugs that have demonstrated in-vitro high antiviral activity against SARS-CoV-2. The present study aims to widen the search and explore the anti-SARS-CoV-2 properties of these potential drugs while looking for new drug indications with optimised benefits via in-silico research.
Here, we designed a unique drug-similarity model to repurpose existing drugs against SARS-CoV-2, using the anti-Covid properties of dexamethasone, baricitinib, and remdesivir as references. Known chemical-chemical interactions of reference drugs help extract interactive compounds withimprovedanti-SARS-CoV-2 properties. Here, we calculated the likelihood of these drug compounds treating SARS-CoV-2 related symptoms using chemical-protein interactions between the interactive compounds of the reference drugs and SARS-CoV-2 target genes. In particular, we adopted a two-tier clustering approach to generate a drug similarity model for the final selection of potential anti-SARS-CoV-2 drug molecules. Tier-1 clustering was based on t-Distributed Stochastic Neighbor Embedding (t-SNE) and aimed to filter and discard outlier drugs. The tier-2 analysis incorporated two cluster analyses performed in parallel using Ordering Points To Identify the Clustering Structure (OPTICS) and Hierarchical Agglomerative Clustering (HAC). As a result, itidentified clusters of drugs with similar actions. In addition, we carried out a docking study for in-silico validation of top candidate drugs.
Our drug similarity model highlighted ten drugs, including reference drugs that can act as potential therapeutics against SARS-CoV-2. The docking results suggested that doxorubicin showed the least binding energy compared to reference drugs. Their practical utility as anti-SARS-CoV-2 drugs, either individually or in combination, warrants further investigation.
由 COVID-19(SARS-CoV-2)引起的冠状病毒健康问题的爆发对公共卫生构成了全球性威胁。因此,需要使用现有的、已批准的、具有已证实安全性措施的疗法来采取有效的补救措施,这具有几个优点。地塞米松(Pubchem ID:CID0000005743)、巴利昔替尼(Pubchem ID:CID44205240)、瑞德西韦(Pubchem ID:CID121304016)是三种已证明具有针对 SARS-CoV-2 的体外高抗病毒活性的通用药物。本研究旨在扩大搜索范围,探索这些潜在药物的抗 SARS-CoV-2 特性,同时通过计算机研究寻找具有优化益处的新药物适应症。
在这里,我们设计了一种独特的药物相似性模型,以重新利用现有的抗 SARS-CoV-2 药物,使用地塞米松、巴利昔替尼和瑞德西韦的抗 COVID 特性作为参考。参考药物的已知化学-化学相互作用有助于提取具有改善的抗 SARS-CoV-2 特性的交互化合物。在这里,我们使用参考药物的交互化合物与 SARS-CoV-2 靶基因之间的化学-蛋白质相互作用来计算这些药物化合物治疗 SARS-CoV-2 相关症状的可能性。特别是,我们采用了两级聚类方法来生成药物相似性模型,以最终选择潜在的抗 SARS-CoV-2 药物分子。一级聚类基于 t 分布随机邻域嵌入(t-SNE),旨在过滤和丢弃异常药物。二级分析包括并行执行的两次聚类分析,即有序点识别聚类结构(OPTICS)和层次聚类(HAC)。结果,它识别了具有相似作用的药物簇。此外,我们还进行了对接研究,以进行计算机验证。
我们的药物相似性模型突出了包括参考药物在内的十种可作为 SARS-CoV-2 潜在治疗药物的药物。对接结果表明,与参考药物相比,多柔比星的结合能最小。它们作为抗 SARS-CoV-2 药物的实际用途,无论是单独使用还是联合使用,都值得进一步研究。