INTRODUCTION:

Hyperlipidemia is increasing lipids in a body that are risk factors for cardiovascular disease that increased last over 30 years¹. The causes major is obesity, with the major cause of insulin mechanisms in peripheral tissue². Some sign that obesity can increase cardiovascular disease risk is high LDL cholesterol, high triglyceride, high blood pressure, and low HDL cholesterol, which all be associated with p, or-inflammation in the adipose tissue that can lead to a flux of fatty acid in hepatic².

Obesity can change some mediators that link with macrophages in adipose tissue.This can make various molecular mechanisms have been implicated in induced inflammation and hypertrophy, such as modulating the peroxide, some proliferator-activates-receptor (PPARs)³. PPARs are a class of ligand-activated nuclear hormone receptor that binds with PPAR-responsive regulatory element (PPRE) and heterodimerization with retinoid X receptors (RXR) which contribute to specific genes⁴. In regulating some biology, lipids, glucose metabolism, and overall energy homeostasis are responsible for PPARs³.

Lampeni, Ardisia humilis Vahl has sinonim such asAnguillariasolanacea ex A.DC⁵. Lampeni was an Indonesian name for a plantisund in Peucang, Ujungkulon National Park island⁶. A woody plant is known as Lampeni, also known as Ardisia humilis Vahl, a member of the Magnoliatae Magnoliatae family member. Folk medicine experts utilized this plant for various illnesses, such as vertigo, as a stimulant for skin ulcers, antioxidant, and anti-diarrheall^7,8. Samal, 2013 study found that feeding Swiss albino rats alcohol extract with a dose of 200mg/kg BW could decrease LDL, triglycerides, total cholesterol, and VLDL and increase HDL. It is considered that the phytochemicals and -amyrin, which are found in Lampeni leaves, may be potential for this biological activity⁹.

Currently, natural products have a large portion as pharmacological agents, particularly in disease therapies¹⁰. The pharmacological natural product remedies have been screened using high throughput approaches in the drug discovery process. Utilizing a mix of chromatographic and spectral techniques, the constituents of numerous medicinal plants have been investigated, extracted, isolated, and identified.Most phytopharmaceuticals have an incompletely understood exact mechanism of action¹¹. Predict the gene networks that are regulative components of therapeutic a novel technique to gain knowledge about how active substances carry out tolerance. Drug development is currently experiencing an effectiveness crisis exacerbated by ineffective, primarily single-target and symptom-based methods instead of mechanistic ones¹². Instead,pharmacology, which Hopkins first used in 2007, is predicated on the idea that numerous highly effective medications impact many targets rather than just one¹³.

Through the integration of systematic medicine and information science, network pharmacology is becoming a cutting-edge field in the study of drugs.Network pharmacology is integrated with the in-silico method to build a "protein-compound/disease-gen" network and uncover the processes behind the collaborative therapeutic advantages of conventional drugs¹⁴. An overview of the technique to highlight the latest developments in molecular docking approaches with time and more advancements in processing capacity and hardware ability, these recent innovations will hopefully finally reach the full potential of this discipline while progressively improving accuracy¹⁵.

MATERIALS AND METHODS:

Identification of Lampenileaf'ss bioactive phytoconstituents and target screening:

Identification of Lampeni leaf bioactive phytoconstituents from the KNApSAcK Family database (http://www.KNApSAcKfamily.com/) and Dr.Duke'ss Phytochemical, Ethnobotanical Databases (https://phytochem.nal.usda.gov/phytochem/search) and some journals.The PubChem database was used to verify the chemical composition of the metabolites of Lampeni leaf (https://pubchem.ncbi.nlm.nih.gov), and SMILES (Canonical Simplified Molecular Input Line Entry System) were extracted¹⁶. The Marvin Sketch software v. 5.2.5.1 was used to build the chemical structure.

Biological activity and protein targets of the phytoconstituents of Lampeni leaf (Ardisia humilis):

The biological activity and target proteins of Lampeni leaf phytoconstituents in hyperlipidemia were predicted with the Way2Drug PASS Online (http://way2drug.com/passo online/) in binding with a probability activator (Pa)score of 0,7 (70%).Pa value > 0,7 denotes a high degree of similarity between the input chemicals and those demonstrated in the database to treat hyperlipidemia effectively.

ADMELab v.2.0 (https://admetmesh.scbdd.com/), an internet service that estimates the likely druglike property based on Lipinski's rule of five, was used to predict the drug-likeness property of the phytoconstituents and Swiss Target Prediction (Probability >0) (http://www.swisstargetprediction.ch/) with the use of canonical SMILES.Eachprotein'ss gene ID was obtained from UniProt.The target protein that will be dockinghas to Ramachandran plot analysis¹⁷.

Network construction and analysis of GO functional enrichment:

The STRING database v.11.5 (https://stringdb.org/) was used to examine the protein interactions in the hyperexamine.The protein interactionsively determines the processes underlying biological processes and aids in obtaining more useful data on gene function.The pathways were identified using the KEGG (Kyoto Encyclopedia of Genes and Genomes) database (https://www.genome.jp/kegg/pathway.html). Cytoscape v.3.9.1 software was used to visualize the network pharmacology of Lampeni leaves (Ardisia humilis).Through the consideration of characteristics like degree centrality (DC), betweenness centrality (BC),closeness centrality (CC), Local average connectivity-based method (LAC), Eigenvector, and network.In this study, a node was regarded to occupy a significant position in the network and to represent the primary component or target if its three parameter values exceeded the associated median¹⁸.

Studies on ligand-protein docking:

As a way to save the ligands in a protein data bank (.pdb) file, MarvinSketch was used to retrieve the ligands from the PubChem chemical database in a Three Dimensional (3D) structure data format (.sdf), minimize them using the mmff94 force field, and then saved¹⁹. The generated protein followed water and unimportant ligand deletion, hydrogen atom addition, and optimization of missing atoms before being saved in pdbqtformat.The protein structure was constructed using Discovery Studio Visualizer v.2021 by eliminating water and heteroatoms.

Ligands were docked with the corresponding protein molecules using AutoDock v.4.2.The Grid Box on the Run Vina under Vina Wizard must be chosen; ensure the box is inside the protein's active site.Finally, have everything ready, start Vina, and wait to download binding affinity data.According to the outcome, the isolate with the lowest (highest negative binding affinity, kcal/mol).The YASARA software is used to validate the protein and determine its RMSD (Root Mean Square Devivalidates the protein and determines) to <2 was selected.The Discovery Studio Visualizer v.2021 was used to view the ligand-protein complex.This research was carried out by a laptop Lenovo Yoga 7-14ITL5 - Type 82BH.

RESULT AND DISCUSSION:

Analyze the Ardisia humilis Effective Compounds and Potential Targets:

The leaf part of Lampeni contained 64 metabolites predicted by the Ethnobotanical, Dr.Duke'ss Phytochemical, and KNApSAcK Family.KNApSAcK is a comprehensive database that details the connections between species, biological activities, and metabolites.The numerous advantages of Dr. Duke'ss phytochemical and ethnobotanical databases include free access, supporting references, and data that interact with ethnomedicinal evidence that demonstrates high significance²⁰.

Using the structure of organic compounds, whether old or new, Way2Drug PASS Online predicts the biological processes of nearly 4.000 categories of physical activities with an average accuracy of 95%, allowing for quick identification of adverse compounds and cheminformatics^21,22.In Figure 1., 10 compounds in Lampeni leaves used as hyperlipidemia was donePass server Way2 Drug.The highest mean probability answered compounds in Lampeni leaves, Pa Beta amyrin was there of lipid metabolism regulatory activities 0,896 was Beta amyrin.Lipid peroxidase inhibitors, insulin promoters, insulin sensitizers, fatty-acyl-CoA-synthase inhibitors, TNF expression inhibitors, interleukin-6 antagonists, and HMG-CoA synthase inhibitors are some of their additional activities.

Figure 1: Result compounds of Way2Drug PASS Online.

Provide experimental and computational methods for estimating solubility and permeability in discovery and development contexts.When there is the molecular weight (MWT) is larger than 500, more than 10 H-bond acceptors and 5 H-bond donors, and the estimated Log P (CLogP) is greater than 5 (or MlogP> 4.15), the called''the rule of five'' suggests that a higher likelihood of poor absorption or penetration²³. Table 1 mentions the compounds included inLipinsky'ss role of five.

Figure 2: Venn diagram containing the targets connected to compounds and hyperlipidemia

891 GO items were taken from a database, including 534 BP items, 161 MF items, 70 CC items, and 126 KEGG pathways. We also chose the top 20 BP, CC, and MF catalogs for visualization (Figure 4). 534 BP The normal axis of the histogram represents the level of enrichment.Because of our BP results, the primary focus of the active Lampeni leaves components functions in hyperlipidemia was a response to lipids and hormones.Most of theduct'ss clear receptors have activity and ligand-activated transcription factor activity. The abundance of GO activities may perhaps explain leaves is effective in treating hyperlipidemia.

Result of Molecular docking:

The grid box is used as the docking target in this investigation, which uses oriented docking. Target proteins are rigid in oriented docking, whereas ligands are flexible. Protein targets and ligands that have already received Lamarckian Genetic Algorithm (LGA) methodology are needed for docking with AutoDock. Molecular anchoring data is utilized for predicting binding structure using bond energy, represented as binding affinity position and bond type²⁵. The result of binding affinity, nevertheless, could not predict the pharmacology and could not affectthe outcome; additional experimental verification is required, either by in vitro or in vivo experiments.However, docking is an important first step in developing and designing stages over in vitro and in vivo research²⁶.

Using the Autodock tools, we utilized molecular docking to determine that the core target and the Lampeni leaves active compounds can bind (Fig 6). A <-5.0kcal/mol binding affinity indicated good binding, and -7.0kcal/mol indicated significant binding activity²⁷. In Our research, active compounds of Lampeni leaves with the seven highest target predictions. The result showed Beta amyrin that significantly docked to EGFR as well as with MAPK3 were -9,6Kcal/mol, IL6 by -9,0 Kcal/mol, and Src by -8,9Kcal/mol (Table 2).

Table 2: Collect the binding affinity and match the atom for each ligand

S. No	Protein	Compound	Binding affinity (kcal/mol)	Match atom
1	IL6 (1alu)	TLA (control)	-8,0	12
		Ardisinol II	-5,5	21
		Bilobol	-5,8	23
		ArdisiphenolB	-6,0	29
		Maesaquinone	-5,7	32
		BetaAmiryn	-9,0	32
		Embelin	-5,3	23
2	HSP90AA1 (4bqg)	50Q (Control)	-8,3	14
		Ardisinol II	-8,5	21
		Bilobol	-8,2	23
		ArdisiphenolB	-8,4	29
		Maesaquinone	-7,3	32
		BetaAmiryn	-7,4	23
		Embelin	-7,2	23
3	EGFR (5uga)	8BM (Control)	-9,3	40
		Ardisinol II	-7,7	21
		Bilobol	-7,5	23
		ArdisiphenolB	-7,1	29
		Maesaquinone	-7,2	32
		BetaAmiryn	-9,6	32
		Embelin	-6,4	23
4	MAPK3 (2zoq)	5ID (Control)	-8,1	27
		Ardisinol II	-6,6	21
		Bilobol	-7,3	23
		ArdisiphenolB	-5,9	29
		Maesaquinone	-5,7	32
		BetaAmiryn	-9,6	32
		Embelin	-6,0	23
5	SRC(2h8h)	H8H (Control)	-9,6	41
		Ardisinol II	-7,1	21
		Bilobol	-7,4	23
		ArdisiphenolB	-7,7	29
		Maesaquinone	-7,4	32
		BetaAmiryn	-8,9	32
		Embelin	-7,1	23
6	PPARG(7awc)	BRL (Control)	-8,8	27
		Ardisinol II	-7,6	21
		Bilobol	-8,1	23
		ArdisiphenolB	-6,0	29
		Maesaquinone	-6,1	32
		BetaAmiryn	-8,1	32
		Embelin	-5,9	23
7	STAT3 (6njs)	KQV (Control)	-9,5	65
		Ardisinol II	-5,6	21
		Bilobol	-6,1	23
		ArdisiphenolB	-4,8	29
		Maesaquinone	-4,7	32
		BetaAmiryn	-7,6	32
		Embelin	-5,4	23

One of the most effective methods for evaluating and predicting the atomic-level binding reactions among receptors and small molecules is molecular docking²⁸. The docking result was improved by analyzing the RMSD and matching the atom in the YASARA view.The obtained RMSD value is 2 Å²⁹. To verify the docking technique, the RMSD is used to determine the success of a combination mode projection.PPARG (The peroxisome proliferated-activated receptor gamma) plays a crucial part in the development of atherosclerosis.Through many methods, including reducing inflammation, enhancing cholesterol efflux, and stabilizing atheroma plague³⁰.

Table 3: The grid box size values and grid center are based on docking data.

No	Protein	Grid box			Grid center
No	Protein	x	Y	z	x	y	z
1	IL6	40	40	40	-7.722	- 12.940	0.048
2	EGFR	40	44	40	13.777	- 3.416	-32.497
3	MAPK3	49	40	41	52.575	22.575	83.072
4	Src	40	40	40	20.192	21.122	58.212

The protein-ligand complexes showed varied docking across each homolog,which was dynamically effective (Figure 5).

Figure 5: A topological representation of the hydrophobicity and residues interaction in the 2D graphic of the binding pocket areas of each protein with each Beta amyrin homolog shows molecular docking.(a) IL6-complex; (b) MAPK3-complex; (c) EGFR-complex (d) SRC-complex; hydrophobic: brown, hydrophilic: blue, neutral: white.

The interaction bonds between crucial residues and ligands were depicted in a 2D figure for several targets. The binding potentials were shown by the hydrogen bonds, van der Waals force, attractive charges, salt bridges, etc.Space tension that is not advised for introduction was caused to unfavorable bumps (red callout)³¹. The hydrophobic effect and hydrogen bonding are typically considered to role essential in stabilizing the target site, which also helps to change binding affinity and therapeutic efficacy³². Water and hydrogen bonds constantly compete in extracellular media.The mechanisms and amounts of hydrogen bonds contribution to protein-protein docking are not well known since water in bulk obstructs reusable biological reactions, and entropy-enthalpy reimbursement occurs when the process of hydrogen bond creation.A problem that persists with poorly understood mechanisms is whether the result of hydrogen bonds affects protein-protein docking³³.

Beta amyrin formed a hydrogen bond with Arg182 and five pi-Alkyl hydrophobic interactions with amino acids (Arg179, Lys171, Leu178, Leu33, and Lau 30) in target IL6.Different complex bonds between Beta amyrin and MAPK3, which has a hydrogen bond on Arg165 and Arg87. Residue interactions with EGFR targets include Ala722, Lys875, Phe723, Phe854, Cys797, Leu844, and Val 726.In contrast, the exchange of residues with SRC forms hydrogen bonds in Glu178 and Thr179.

Inflammation triggered by interleukin 6 can contribute to age-related illnesses like atherosclerosis. Atherosclerosis is a condition that develops with time and is defined by the accumulation of lipids³⁴. EGFR-mediated autophagy activation controls lipid levels³⁵. The tyrosine kinase epidermal growth factor receptor, also known as EGFR, is abundantly expressed outside many kinds of cells.And activates several important signaling pathways in differentiation and metastasis, cell proliferation, cell survival, and tolerant activation³⁶. The mammalian target of Rapamycin(mTOR) inhibition adversely impacts MAPK/ERK pathway (The mitogen-activated protein/extracellular signal-regulated kinase) in association with EGFR internalization and degradation.We further show that Src kinase stimulation demonstrates EGFR uptake in response to mTOR inhibition³⁷.

CONCLUSION:

A pharmacology network for lampeni leaves (Ardisia humilis) shows the relationship between the metabolic products they contain, theirtargets'' surface receptors and intracellular proteins, and hyperlipidemiasignaling pathways.Through the molecular docking procedure, the compounds obtained Beta amyrin is estimated to be successful against hyperlipidemia through IL6, MAPK3, EGFR, and Src pathway.This information could be utilized for designing and screening in silico new compounds in Lampeni leaves with improved antihyperlipidemic activity for in vitro experimental validation.Involving aspects of the biological, medical, and pharmaceutical fields is molecular pharmacology³⁸.

CONFLICT OF INTEREST:

The author declares that they have no competing interests.

ACKNOWLEDGMENTS:

The author thanks the BRIN for funding the Indonesia Advances Innovation Research (IAIR) 2022-2023.

REFERENCES:

1. Pirillo A, Casula M, Olmastroni E, Norata GD, Catapano AL. Global epidemiology of dyslipidaemias. Nat Rev Cardiol. 2021; Oct;18(10):689-700. doi: 10.1038/s41569-021-00541-4. Epub 2021 Apr 8. PMID: 33833450.

2. Klop B, Elte JW, Cabezas MC. Dyslipidemia in obesity: mechanisms and potential targets. Nutrients. 2013 Apr 12;5(4):1218-40. doi: 10.3390/nu5041218. PMID: 23584084; PMCID: PMC3705344.

3. Stienstra R, Duval C, Müller M, Kersten S. PPARs, Obesity, and Inflammation. PPAR Res. 2007;doi: 10.1155/2007/95974. PMID: 17389767; PMCID: PMC1783744.

4. Corrales P, Vidal-Puig A, Medina-Gómez G. PPARs and Metabolic Disorders Associated with Challenged Adipose Tissue Plasticity. Int J Mol Sci. 2018 Jul 21;19(7):2124. doi: 10.3390/ijms19072124. PMID: 30037087; PMCID: PMC6073677.

5. Wiryono, WiryonoandMersyah, RohidinandTarantona, Mariska. Flora of Danau Dusun Besar conservation forest in Bengkulu Province, Indonesia. Biodiversitas Journal of Biological Diversity. 2020; 21. 10.13057/biodiv/d211209.

6. Tinambunan, fransiska. Phytochemical screening and platelet anti-aggregation test of lampeni leaf extract (Ardisia humilis) from Peucang Island, Ujung Kulon National Park. Journal. Bio. Unsoed. Ac. Id. 2022. DOI: 10.20884/1.sb.2022.9.1.1341

7. Khatun, A., Rahman, M., Kabir, S., Akter, M. N., and Chowdhury, S. A. Phytochemical and pharmacological properties of methanolic extract of Ardisia humilis Vahl. (Myrsinaceae). International Journal of Research in Ayurveda and Pharmacy. 2013;4(1), 38–41. https://doi.org/10.7897/2277-4343.04120

8. Samal, P. K. Evaluation of the Antioxidant activity of Ardisia solanacea in albino rats. Research J. Pharm. and Tech. 2013; 6(5), 585–588.Available on: https://rjptonline.org/AbstractView.aspx?PID=2013-6-5-14

9. Samal, P. K. Assessment of Hypolipidemic Effect of Ardisia solanacea in high-fat diet induced rats. Samal, Pradeep Kumar, 2013; 5(3).

10. Yahya, E., Afshin, hasanvand, Ali, V., Farzad, E., and Saber, A. Natural Antioxidants and Medicinal Plants Effective on Hyperlipidemia. Research Journal of Pharmacy and Technology. 2019. 12(3), 1457–1462. DOI: 10.5958/0974.2019.00242.7

11. Ahmad, S. Recent advances in Pharmacology and Toxicology of Phytopharmaceuticals. Asian Journal of Pharmaceutical Research.2017; 7(4): 222-224. doi: 10.5958/2231-5691.2017.00034.X Available on: https://asianjpr.com/AbstractView.aspx?PID=2017-7-4-2

12. Casas, A. I., Hassan, A. A., Larsen, S. J., Gomez-Rangel, V., Elbatreek, M., Kleikers, P. W., Guney, E., Egea, J., López, M. G., and Baumbach, J. J. From single drug targets to synergistic network pharmacology in ischemic stroke. Proc. Natl. Acad. Sci., 2019; 116, 7129–7136.

13. Hopkins, A. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol 4, 2008; 682–690. https://doi.org/10.1038/nchembio.118

14. Chandran U, Mehendale N, Patil S, Chaguturu R, Patwardhan B. Network Pharmacology. Innovative Approaches in Drug Discovery. 2017; 127–64. doi: 10.1016/B978-0-12-801814-9.00005-2. Epub 2016 Oct 14. PMCID: PMC7148629.

15. Bagal,A., Tai Borkar, Trupti Ghige, Anushka Kulkarni, Aakanksha Kumbhar, Ganesh Devane, Sachin Rohane. Molecular Docking – Useful Tool in Drug Discovery. Asian Journal of Research in Chemistry. 2022; 15(2):129-2. doi: 10.52711/0974-4150.2022.00020

16. Kim S, Thiessen PA, Bolton EE, Chen J, Fu G, Gindulyte A, Han L, He J, He S, Shoemaker BA, Wang J, Yu B, Zhang J, Bryant SH. PubChem Substance and Compound databases. Nucleic Acids Res. 2016 Jan 4;44(D1):D1202-13. doi: 10.1093/nar/gkv951. Epub 2015 Sep 22. PMID: 26400175; PMCID: PMC4702940.

17. Devgun, Manish. Homology modeling and molecular docking studies of DNA replication licensing factor minichromosome maintenance protein 5 (MCM5). Asian Journal of Pharmacy and Technology. 2015. 5. 10.5958/2231-5713.2015.00004.5.

18. Gao K, Song YP, Song A. Exploring active ingredients and function mechanisms of Ephedra-bitter almond for prevention and treatment of Corona virus disease 2019 (COVID-19) based on network pharmacology. BioData Min. 2020 Nov 10;13(1):19. doi: 10.1186/s13040-020-00229-4. PMID: 33292385; PMCID: PMC7653455.

19. Halgren TA. Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. J Comput Chem. 1996;17(5-6), 490–519.https://doi.org/10.1002/(SICI)1096-987X(199604)17:5/6<490::AID-JCC1>3.0.CO;2-P

20. Savithramma N, Yugandhar P, Prasad KS, Ankanna S, Chetty KM. Ethnomedicinal studies on plants used by Yanadi tribe of Chandragiri reserve forest area, Chittoor District, Andhra Pradesh, India. J IntercultEthnopharmacol. 2016 Jan 27;5(1):49-56. doi: 10.5455/jice.20160122065531. PMID: 27069725; PMCID: PMC4805147.

21. Filimonov DA, Lagunin AA, Gloriozova TA, et al. Prediction of the Biological Activity Spectra of Organic Compounds Using the Pass Online Web Resource. Chemistry of Heterocyclic Compounds. 2014;50(3):444-457. doi:10.1007/s10593-014-1496-1.

22. Upmanyu, N., Gupta, S., Prakash, B., Garg, G., and Mishra, P. Cheminformatics: A New Approach. Research Journal of Pharmacy and Technology, 2008;1(1), 2–5.

23. Lipinski CA, Lombardo F, Dominy BW, and Feeney PJ.Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews, 64(Suppl.). 2012; 4-17.

24. Zhang, L., Shi, X., Huang, Z., Mao, J., Mei, W., Ding, L., Zhang, L., Xing, R., and Wang, P. Network pharmacology approach to uncover the mechanism governing the effect of radix achyranthisbidentatae on osteoarthritis. BMC Complementary Medicine and Therapies, 2020; 20(1). https://doi.org/10.1186/s12906-020-02909-4

25. Anggraeni, V., Purwaniati, Budiana, W., and Nurdin, T. (2022). Molecular Docking Compounds In Methanol Extract Of Mango Leaves (Mangifera Indica L.) As Anti-Inflammatory Agent. Jurnal Kimia Riset (JKR), 7(1).

26. Brooijmans, N., Mobilio, D., Walker, G., Nilakantan, R., Denny, R. A., Feyfant, E., Diller, D., Bikker, J., and Humblet, C. A structural informatics approach to mine kinase knowledge bases, Drug Discovery Today. 2010;15,5-6, 203–209.

27. Saikia S, Bordoloi M. Molecular Docking: Challenges, Advances and its Use in Drug Discovery Perspective. Curr Drug Targets. 2019;20(5):501-521. doi: 10.2174/1389450119666181022153016. PMID: 30360733.

28. Suganya, J., T, V., Radha, M., and Marimuthu, N. In silico Molecular Docking studies to investigate interactions of natural Camptothecin molecules with diabetic enzymes. Research Journal of Pharmacy and Technology. 2017; 10(9), 2917–2922.

29. Saputra, D. P. D.Molecular Docking Sianidin dan Peonidin sebagaiAntiinflamasi pada Atherosclerosis Secara In Silico. Jurnal Farmasi Udayana. 2018; 7(1).

30. Khotimah, S., Kalim, H., Rohman, M. S., andSoeharto, S. Anti-Atherosclerotic Activity of Eleutherine americana Merr. as the Peroxisome Proliferated-Activated Receptor γ Agonist: In Silico Study. Research J. Pharm. and Tech, 2020;13(3), 1423–1428. DOI: 10.5958/0974-360X.2020.00260.7

31. Dai W, Chen HY, Chen CY. A Network Pharmacology-Based Approach to Investigate the Novel TCM Formula against Huntington's Disease and Validated by Support Vector Machine Model. Evid Based Complement Alternat Med. 2018 Dec 11;2018:6020197. doi: 10.1155/2018/6020197. PMID: 30643534; PMCID: PMC6311282.

32. Patil R, Das S, Stanley A, Yadav L, Sudhakar A, and Varma AK. Optimized hydrophobic interactions and hydrogen bonding at the target-ligand interface lead to the pathways of drug designing. PLoS One, 2010;16;5(8):e12029.

33. Chodera JD, Mobley DL. Entropy-enthalpy compensation: role and ramifications in biomolecular ligand recognition and design. Annu Rev Biophys. 2013;42:121-42. doi: 10.1146/annurev-biophys-083012-130318. PMID: 23654303; PMCID: PMC4124006.

34. Omoigui S. The Interleukin-6 inflammation pathway from cholesterol to aging--role of statins, bisphosphonates and plant polyphenols in aging and age-related diseases. Immun Ageing. 2007 Mar 20;4:1. doi: 10.1186/1742-4933-4-1. PMID: 17374166; PMCID: PMC1845171.

35. Sênos Demarco R, Uyemura BS, Jones DL. EGFR Signaling Stimulates Autophagy to Regulate Stem Cell Maintenance and Lipid Homeostasis in the Drosophila Testis. Cell Rep. 2020 Jan 28;30(4):1101-1116.e5. doi: 10.1016/j.celrep.2019.12.086. PMID: 31995752; PMCID: PMC7357864.

36. Kavitha K, Srinivasan N, Mohan S, and Suresh R. Insilico Design and Potential Cytotoxic Agents EGFR Inhibitors of 4(3H) Quinazolinone Derivatives. Research Journal of Pharmacy and Technology, 2021;14(9). DOI: 10.52711/0974-360X.2021.00842

37. Colella B, Colardo M, Iannone G, Contadini C, Saiz-Ladera C, Fuoco C, Barilà D, Velasco G, Segatto M, Di Bartolomeo S. mTOR Inhibition Leads to Src-Mediated EGFR Internalisation and Degradation in Glioma Cells. Cancers (Basel). 2020 Aug 13;12(8):2266. doi: 10.3390/cancers12082266. PMID: 32823532; PMCID: PMC7464593.

38. Saha, D., Jana, M., and Mandal, S. (2011). Target Discovery and Validation: Advances in Molecular Pharmacology. Asian Journal of Pharmaceutical Analysis, 1(2), 27–28. 1(2): April-June 2011; Page 27-28. Available on: https://ajpaonline.com/AbstractView.aspx?PID=2011-1-2-2

Received on 05.05.2023 Modified on 01.09.2023

Research J. Pharm. and Tech 2024; 17(5):2009-2017.

DOI: 10.52711/0974-360X.2024.00318

PubChem ID	Compounds	Lipinsky	Molecular Weight	Log P	HBA	HBD	BBB
6454482	Ardisinol II	Accepted	290.220	5.615	2	2	--
5281852	Bilobol	Accepted	318.260	6.631	2	2	---
1024897	Ardisiphenol B	Accepted	376.260	6.244	4	2	---
6383665	Maesaquinone	Accepted	418.310	8.023	4	4	---
73145	Beta amyrin	Accepted	426.390	7.713	1	1	---
73145	Embelin	Accepted	294.180	4.935	4	4	---