A NETWORK PHARMACOLOGY OF BELUNTAS ( Pluchea Indica ) ON IMMUNITY CASES

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Background
COVID-19 (Corona Virus Disease 2019) is a case of initial immunity that appeared in Wuhan at the end of 2019 (Lena et al., 2023).The first COVID-19 case in Indonesia was reported on March 2, 2020.Until now, in Indonesia, there have been 6,738,225 positive people, and the death total is 160,941 as of March 10, 2023(Johns Hopkins University, 2023).The spread of the COVID-19 virus happens directly or indirectly through droplet transmission or through contact with physical / objects that have direct contact with patients exposed to COVID-19 (Priani, 2021).The immune system is a system whose job is to protect and defend the body from dangerous pathogens and even destroy the foreign cells that have entered the body (Priani, 2021).Studies that have been done previously show that certain plants can increase the immune system (Setiawan et al., 2021).One of them is Pluchea Indica (Nurhalimah, 2014;Sahara and Pristya, 2022).However, no explanatory information exists on how P. indica can increase immunity.Therefore, this research will find or demonstrate the mechanism of molecules that occur in humans when given the treatment of extract P. indica.Testing activity of a compound can done through three approaches: in silico, in vitro, and in vivo tests.In vivo and in vitro tests require lots of time and cost compared with an in silico test (Makatita et al., 2020).The method used is a descriptive study using computer models (Adelina, 2018;Bajorath, 2015).The step in this method is prediction, hypothesis, and providing a new outlook on treatment in the field of medical and therapeutic (Bare et al., 2019).In-silico tests were carried out in networking pharmacology, a term used for the first time in 2007 (Hopkins, 2007).Networking pharmacology gives the basis for complex biology systems from the network perspective.We can understand the circumstances of health and disease in the body by determining and analyzing network biology and using it as a target for designing effective drug intervention methods (Wang et al., 2021).In silico test is scientifically valid, relatively new, and highly accurate (Istyastono et al., 2020)

Research Method
Identification of plant secondary metabolite compounds was obtained using the Dr. Duke's Phytochmeical and Ethnobotanical Databases, then searching for SMILES code for each compound using PubChem and entered to SwissADME to see bioavailability prediction by using Boiled-EGG method (Daina et al., 2017;Daina and Zoete, 2016).SwissTargetPrediction was used to predict the interaction of compounds with proteins targeted in research with compounds that pass the Boiled-EGG method (Daina et al., 2019).The target proteins of immunomodulators were searched using GeneCards (Stelzer et al., 2016).Then, look for intersections of proteins predicted to bind to compounds from plants using Venny (Oliveros J, 2015).The list of proteins that appear is then entered into StringDB (Szklarczyk et al., 2021).After that, we look for predictions of proteins that are interrelated with the immune system using KEGG (Kanehisa et al., 2023).To see which proteins interact most with pathways related to the immune system, we then looked at the secondary metabolite compounds of P. indica that interact with those proteins.

Identification of secondary metabolites of P. indica
Secondary metabolites of P. indica were obtained using Dr. Duke's Phytochemicals and Ethnobotanical Databases.This database was widely used to characterize plant bioactive compounds (Nguyen-Vo et al., 2020).234 compounds were identified in Dr. Duke's Phytochemicals and Ethnobotanical Databases (Table 1).

Bioavailabity Prediction of secondary metabolites of P. indica
Bioavailability is an important parameter for determining the amount and level of drug absorption in the body (Labibah and Rusdiana, 2022).Therefore, determining bioavailability is more important than stating whether a compound has medicinal potential.Bioavailability prediction was carried out using the SwissADME web server with the Boiled-EGG method (brain or intestinal estimated permeation method).This method was proposed as an accurate prediction model that accounts for the lipophilicity and polarity of small molecules (García-Beltrán et al., 2023;Panova et al., 2023).The Boiled-EGG model provides a rapid, intuitive, easily reproducible, yet statistically unprecedented powerful method for predicting passive gastrointestinal absorption and brain access of small molecules useful for drug discovery and development (Feng et al., 2020;Naveed et al., 2023).This method uses an image model (Figure 1) to classify compound absorption.On the other hand, the yolk area demonstrates the potential of compounds like Lagerine, Meliantriol, Neo-Beta-Carotene-U to cross the blood-brain barrier, as indicated by their wLogP and TPSA values that describe their lipophilicity and polarity (Daina and Zoete, 2016).Out of a total of 234 compounds, 126 have been found to have high bioavailability, while 109 compounds have been shown to have low bioavailability.(Table 2).
Figure 1.Bioavailability prediction of of the secondary metabolite of P. indica using BOILED-Egg method.

Table 2. Bioavailability prediction of the secondary metabolite of P. indica using BOILED-Egg
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Immunomodulatory Proteins that Associated With Secondary Metabolites of P. indica
After obtaining the bioavailability prediction of each secondary metabolite compound of P. indica, the next step was target proteins prediction that can interact with the compounds carried out by SwissTargetPrediction (Lawal et al., 2021).The results show that 1,317 proteins were predicted to interact with secondary metabolites of P. indica.In order to obtain related proteins with immunomodulators, it was carried out by GeneCards (Safran et al., 2021).The results show that 1,340 related proteins were connected with immunomodulators.
Venny was used to find the intersection between secondary metabolites-linked and immunomodulator-linked proteins.Based on the interaction results, 161 immunomodulator-linked proteins were predicted for interaction with secondary metabolites of P. indica.From the interaction results, 304 immunomodulator-linked proteins were predicted to interact with secondary metabolites of P. indica (Figure 2).

Network Pharmacology Analysis
Protein obtained from the intersection Venn diagram was then analyzed using StringDB (Figure 3), which aims to make network interaction between selected proteins target.This step determines the connection between the selected protein and analyzes the biological pathways influenced by this protein (Veda et al., 2023).StringDB is a database with over nine million proteins that are known and predicted to integrate linkage functional data from various sources (Grabowski and Rappsilber, 2019;Jung et al., 2021).It delivers a very easy and fast way to see groups of related genes/proteins functionally (Grabowski and Rappsilber, 2019) After that, KEGG enrichment analysis was carried out.From the analysis results, the pathways associated with the immunomodulator were searched, and five pathways were selected with the highest strength (Veda et al., 2023).KEGG (Kyoto Encyclopedia of Genes and Genomes) was used for bioinformatics research and education in drug development (Hehenberger, 2020) KEGG is a collection of pathway maps drawn manually, representing our knowledge about molecular interaction (Kanehisa et al., 2023).PD-L1 expression and PD-1 checkpoint pathway in cancer 1.31 2.

Conclusion
According to the results of network pharmacology analysis conducted on beluntas (Pluchea Indica), it has been found that 17 different compounds play a significant role in the immune system.This is due to the fact that these compounds have been shown to interact with two proteins that are related to immunomodulatory pathways.

Figure 2 .
Figure 2. Venn diagram of protein that predicted linked with P. indica and immunomodulatorlinked protein