Network Pharmacology Approach for Herbal Drugs Intended for the Therapy of Diseases: A Comprehensive Review

Satwika Bonthu, Sarika Pulichintha, None Ganga Raju. M., None N. V. L. Suvarchala Reddy V.
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Abstract

The single drug/single target/single disease tactic to medicine detection currently faces many challenges in terms of welfare, efficiency and sustainability. Network biology and multipharmacology approaches have recently gained acceptance as approaches for omics documents incorporation and multi-target drug development, respectively. Combining these two approaches has created a new model termed network pharmacology (NP) that examines the effects of medications on both interaction and disease. Ayurveda, traditional Indian medicine, uses a scientific formula that contains many ingredients and numerous bioactive composites. Though, the scientific basis and methods are still largely unexplored. Network pharmacology is a prediction tool that helps in predicting the bioactives from different databases, respective genes from databases which are expressed during the disease. The genes are also ranked from cytohubba and genes with greater number have greater interactions with other genes. The mechanism can be predicted from different pathways like KEGG pathway. From the obtained data a network can be constructed using cytoscape and represented.
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治疗疾病的中草药网络药理学研究综述
单一药物/单一靶点/单一疾病的药物检测策略目前在福利、效率和可持续性方面面临许多挑战。网络生物学和多药理学方法最近分别被接受为组学文献整合和多靶点药物开发的方法。结合这两种方法创建了一个新的模型,称为网络药理学(NP),研究药物对相互作用和疾病的影响。印度传统医学阿育吠陀采用科学配方,含有多种成分和多种生物活性复合物。尽管如此,科学基础和方法在很大程度上仍未被探索。网络药理学是一种预测工具,它有助于预测来自不同数据库的生物活性,以及来自数据库中在疾病期间表达的各自基因。基因也从细胞壁排列,数量越多的基因与其他基因的相互作用越大。其机制可以从不同的途径来预测,如KEGG途径。从获得的数据中,可以使用细胞景观构建网络并表示。
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