Journal of Research in Pharmacy

Öz

The main aim of traditional drug discovery is to find specific ligands. The theory of “one gene, one drug, one disease” contains the hypothesis that the main drug action is only on a single target—the stronger drug selectivity, the more specific mechanism of action, and the higher degree of correlation with the target phenotype. Therefore, discovering specific ligands plays a vital role in drug design [1]. This approach has led researchers working on natural products to the isolation and structure determination of bioactive natural compounds from plants. Some diseases are caused by a variety of factors with very complex pathologies, and the use of a single drug cannot solve these problems. Many effective compounds act via the modulation of multiple proteins rather than a single protein [2]. A new perspective, the concept of network pharmacology was first proposed by Andrew L. Hopkins in 2007 who combined network biology with polypharmacology. Network pharmacology focuses on understanding the internal mechanism and drug action of complex diseases and syndromes [3-4]. The therapeutic efficacy of plant extract is rooted in their complexity of chemical composition and molecular mechanism. Consequently, network pharmacology studies are mostly applied to TCM formulas [5]. Methodologies of network pharmacology are based on two essential sections; network construction and network analysis. The networks are composed of nodes and edges. The nodes are investigated subjects or entries. The edges are node connections and node connections can be based on different data sources such as knowledge-based, experiment-based, and computation-based. Network analysis lets us identify novel biomarkers and discriminate effective pathways. Topological metrics analysis focused on the topological characteristics of a network or its components. The network module is expressed enrichment analysis such as the detection of pathways or gene ontology [6]. Predominantly, databases are widely used in network pharmacology studies as data sources. Traditional Chinese Medicine databases (SymMap, BATMAN-TCM, TCMID, etc.) contain much information such as the plants in the mixtures, the compounds in the plants, and the bioavailability information of the compounds. Biological databases (OMIM, DisGeNET, MalaCards, etc.) contain clinical and basic research results and it is also described as disease phenotype and genotype association database. Gene targets databases (TTD, PDB, KEGG, GeneCards, etc.) included comprehensive information about genes, proteins, or compounds. Protein interaction databases (BioGRID, DIP, IntAct, STITCH, etc.) are included information on gene-protein or chemical-protein interaction [7]. Online or offline softwares are used in this method. Cytoscape is a well-known and commonly used visualization software. This is an open-source platform suitable for visualizing molecular interaction networks and biological pathways [8]. Various issues can be often researched in network pharmacology studies. In target discovery studies, complex diseases occur with the change of multi-targets in biological systems. The content of plant extract shows a similar effect when it gets into the body. These affected targets are used for the discovery of new targets. In the bioactive compounds screening studies, network pharmacology provides an easy method for seeking potential bioactive compounds by mapping chemical compounds into the disease-gene network and mechanism research. In toxicity evaluation, network pharmacology can break the limitations of traditional methods to find toxic compounds and mechanisms of toxicity rapidly [5,9]. Network pharmacology gives a general aspect of view and provides the prediction of the key metabolic pathways.