INTRODUCTION:

Chronic Obstructive Pulmonary Disease (COPD) is a progressive lung disorder characterized by chronic inflammation of the airways and limited airflow, making breathing difficult. It includes several conditions, most notably chronic bronchitis and emphysema, which often occur together in individuals. COPD is known to affect the patient's quality of life.^1,2

COPD is mainly caused by long-term exposure to irritants such as cigarette smoke, air pollution, and workplace dust and chemicals. The inflammatory process in COPD plays a central role in the development and progression of the disease. Inflammation in COPD primarily affects the bronchial tubes and air sacs (alveoli) in the lungs. This inflammatory response is the result of the body's efforts to defend against irritation caused by inhaled harmful particles and gases. However, chronic exposure to these irritants causes an exaggerated and sustained inflammatory response that damages lung tissue.³ The inflammatory process involves various immune cells and chemical mediators.⁴ Macrophages, a type of immune cell, are the first line of defence. They ingest and attempt to neutralize inhaled particles, but chronic exposure can overwhelm them. This leads to the recruitment of other immune cells, such as neutrophils and T lymphocytes, which release inflammatory molecules such as cytokines and chemokines. These molecules trigger a chain of events, causing inflammation and tissue damage.³

Inflammation is a symptom of pathology that can be prevented, attenuated, and cured with pharmaceutical or herbal medicines.^5,6 Platostoma palustre (synonym Mesona chinensis Benth), commonly known as Chinese Mesona, is a plant species belonging to the mint family. It is native to Southern and Southeast Asia, including China, Taiwan, Japan, and Vietnam, and it may grow in ravines, grassy, dry, and sandy areas.⁷ P. palustre, also known as Cincau (Indonesian) or Xiancao (Chinese), is a plant that has been used and consumed as an herb. Based on previous research, P. palustre may inhibit fructose-mediated protein glycation and protein oxidation, which could be beneficial for preventing AGE-mediated diabetic complications.⁸ Another study published in Molecules has shown that P. palustre extract has antioxidant and hypolipidemic effects. The study found that the extract of P. plaustre reduces the levels of total cholesterol, triglycerides, and low-density lipoprotein cholesterol in the serum of rats with high-fat diet-induced hyperlipidemia.⁹ Based on this potential, we wanted to evaluate the potential anti-inflammatory bioactivity of P. palustre active compound based on inhibition of COPD related-key protein. This evaluation was carried out using the bioinformatics analysis method, which has been widely used in various studies and is useful for providing fast and reliable baseline research data.^10–12

Method:

This research was carried out using bioinformatics analysis methods that look for genes related to COPD and identify the active compounds of P. palustre through previous research. Then COPD-related genes were investigated for possible biological processes and evaluated for molecular activity that might occur from the interaction of the active compounds of P. palustre on related genes and proteins. (Figure 1).

Figure 1. Research workflow. This study uses four stages of analysis, starting with metabolite analysis to determine the ligand; DEG, PPI, and GO analysis to determine receptors; and molecular docking to determine the results of the interaction.

P. palustre active compound acquisition:

Information on the active compounds of P. palustre was obtained from research by Tang and colleagues (2023)¹³from the results of UHPLC-MS/MS analysis until the molecular structure and CAS identification number were recognized. Then the simplified molecular input line entry system (SMILES) and 3D canonical structures of the active compounds were obtained and downloaded from the PubChem database (https://pubchem.ncbi.nlm. nih.gov/) and the ChemSpider database (http://www.chemspider.com/). The 3D structure of the protein was obtained from the RCSB PDB database (https://www.rcsb.org/), and the protein structure was analyzed using the PDBSum webserver (http://www.ebi.ac.uk/thornton-srv/databases/ pdbsum/) to determine the Ramachandran and G-factor scores of each structure.¹⁴

Bioactivity prediction and QSAR screening analysis:

Screening was achieved by selecting active compounds with the highest bioactivity potential. Bioactivity prediction analysis based on Molinspiration (https://molinspiration.com/cgi/ properties) performs bioactivity scoring-related actions to act as GPCR ligands, ion channel modulators, kinase inhibitors, nuclear receptor ligands, protease inhibitors, and enzyme inhibitors. Quantitative structure-activity relationship (QSAR) analysis is a practical analysis that aims to calculate the results of quantifying chemical structures in certain biological interactions or chemical reactions. QSAR analysis was carried out via the PASSOnline webserver (https://www.way2drug.com/PASSOnline), which shows the Pa (probability to be active) and Pi (probability to be inactive) scores of a compound at a certain bioactivity. Compounds with a Pa prediction score > 0.7 are categorized as having good bioactivity potential.^15,16 In this analysis, bioactivity parameters used included anti- inflammatory, anti-inflammatory intestinal, anti-inflammatory ophthalmic, and non-steroidal anti-inflammatory agents. The average score of the two-parameter groups will also be calculated to assess their potential in a broader sense.

Gene data obtained and different expressed genes analysis

Two sequencing datasets were obtained from a public gene database, namely the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/gds/), namely GSE198740¹⁷ and GSE201465¹⁸, which are gene expression data in COPD patients. In GSE198740, the instrument used was GPL24676: Illumina NovaSeq 6000 (Homo sapiens), and the total number of samples was 18, consisting of 9 normal samples and 9 COPD patients.¹⁷ Whereas in GSE201465, the instrument used was GPL16791: Illumina HiSeq 2500 (Homo sapiens), and the total number of samples was 12, consisting of six normal samples and six COPD patients.¹⁸ In this study, five normal and COPD samples were selected for each GSE as representatives. Different expressed genes (DEG) analysis was performed using the GEO2R interactive tool (http://www.ncbi.nlm.nih.gov/geo/geo2r) provided by NCBI to compare data from a total of 10 samples from each GSE. The threshold used in this research is logFC > 1.5, a p- value of < 0.05 as statistical significance, and Benjamini & Hochberg (false discovery rate) as an adjustment of the p-value. Then, the significance gene was analyzed using MS Excel 2016 to separate the duplicate genes, and the list of significance genes was analyzed with Venn online software to obtain the same gene. The genes in common between the two GSEs are called DEGs and are used for further analysis.

Functional annotation of DEGs, gene enrichment, and protein interaction:

Functional annotation of DEGs was analyzed by the DAVID database (Database for Annotation, Visualization, and Integrated Discovery; https://david.ncifcrf.gov/) to find out the Gene Ontology biological process (BP), cellular component (CC), and molecular function (MF) (cutoff: p-value < 0.0001) of 54 DEGs. Then, the gene enrichment was done by adding 100 genes related to 54 DEGs whose interactions were known through the STRING database (https://string-db.org/) with a confidence score of 0.700 (high confidence). The network was analyzed using Cytoscape v.3.9.1 by the NetworkAnalyzer plug-in in order to obtain the betweenness centrality score of the network and the g:Profiler plug-in to collect gene ontology and pathway data from the Kyoto Encyclopedia of Genes and Genomes (KEGG; https://www.kegg.jp) and the Reactome database (https://reactome.org/). The ten-highest betweenness centrality score gene was evaluated for its availability as a molecular docking receptor.

Evaluation for the molecular docking receptors candidate and binding site analysis:

The evaluation was carried out by checking the availability of the 3D structure of proteins from laboratory sources. By this process, FN1 (PDB ID: 2CG6), CDH2 (PDB ID: 1NCH), and NOS3 (PDB ID: 1M9J) were chosen for the receptors because of their data availability. Then, the third has been analyzed for their 3D structure based on the Ramachandran and G-factor evaluation by PDBSum (http://www.ebi.ac.uk/thornton-srv/databases/pdbsum/). ¹⁴ The binding site analysis was performed by submitting the prepared 3D structure of the receptor to the COACH- D webserver (https://yanglab.nankai.edu.cn/ COACH-D/) to obtain the list of amino acid residues that were predicted as binding sites for the ligands.¹⁹

Molecular docking analysis:

Molecular docking was performed by interacting with the receptors with 33 P. palustre active compounds and six drugs. The receptor first minimized their energy by using Swiss PDBviewer v.4.1.0 (http://www.expasy.org/spdbv/). and the ligands were also prepared by the Open Babel plug-in in PyRx.²⁰ Then the docking was achieved by PyRx 0.9.7^21,22 with the grid location as follows: FN1, Center X: 13,594, Y: 3,288, Z: 43,690; Dimensions (Angstrom) X: 32,346, Y: 28,961, Z: 27,988. CDH2, Centers X: 0.364, Y: 19.897, Z: 12.293; Dimensions (Angstroms): X: 23,587, Y: 24,286; Z: 23,172. NOS3, Centers X: 13,379, Y: 10,694, Z: 54,724; Dimensions (Angstroms) X: 24,810, Y: 29,359, Z: 30,738. Docking visualization was performed by Discovery Studio v.19.1 to obtain detailed interactions of the interacted residue and 3D docked ligand poses.

RESULT:

P. palustre active compound bioactivity prediction result:

Based on research data by Tang et. al. (2023)¹³, 86 active compounds were obtained from the lipid group (23 compounds), organic acids (18 compounds), carbohydrates (16 compounds), steroids (14 compounds), vitamins and cofactors (8 compounds), hormones and transmitters (6 compounds), and antibiotics (5 compounds). Then, based on the anti-inflammatory bioactivity score, the active compound group from the lipid group tends to have the highest bioactivity score as anti-inflammatory and non-steroidal anti-inflammatory agents (NSAIA) of the active compounds from the other groups. Meanwhile, as a specific anti-inflammatory in ophthalmic and intestinal applications, active compounds from the organic acid group tend to have the highest bioactivity score. (Figure 2A-B)

Based on the results of the Molinspiration bioactivity score screening of the active compounds of P. palustre as GPCR ligands, ion channel modulators, kinase inhibitors, nuclear receptor ligands, protease inhibitors, and enzyme inhibitors, 18 active compounds were obtained, which were the three active compounds with the highest scores for each bioactivity. In addition, based on the Pa score of bioactivity as anti-inflammatory, anti- inflammatory intestinal, anti-inflammatory ophthalmic, and NSAIA, 20 active compounds were obtained, consisting of five active compounds with the highest Pa score for each bioactivity. (Figure 2C-D) From the results above, 33 active compounds were obtained, which will be used as ligands in molecular docking analysis. (Table 1)

Defining DEGs from GEO dataset:

First, median normalization of GSE198740 and GSE201465 was performed to improve the consistency of the data that will be analyzed next.²³ The results of the analysis of GSE198740 and GSE201465 obtained 58 DEGs from 1607 and 383 genes, respectively. Volcano plots are used to show genes that experience significant upregulation and downregulation. Based on 58 DEGs, two genes (LINC00886 and LINC02940) were excluded from further analysis because they were non-coding genes, so the remaining 56 DEGs were to be analyzed for functional annotation. The GO BP functional annotation results show that more than 20 DEGs play a role in cellular and developmental processes, and the GO CC functional annotation shows that 10 DEGs play a role in cell motility. (Figure 3A-E) More extensive functional annotation results will be presented from the gene enrichment results.

Gene enrichment result:

Gene enrichment produces 100 additional genes to expand the main network of interacting DEGs. Based on the enrichment results, 10 DEGs with the highest betweenness centrality scores were obtained, namely FN1 (fibronectin-1), CDH2 (cadherin-2), NOS3 (nitric oxide synthase 3), DNAH9 (dynein axonemal heavy chain 9), DNAH11, CACNA1H (calcium voltage-gated channel subunit alpha1 H), CACNG4 (calcium voltage-gated channel auxiliary subunit gamma 4), TNC (tenascin-C), BAI1 (brain-specific angiogenesis inhibitor 1), and DNAAF1 (dynein axonemal assembly factor 1). In addition, based on the results of the Gene Ontology biological process analysis, it is known that 58 genes are involved in GO:0010959: Regulation of metal ion transport, 55 genes are involved in GO:0032879: Regulation of localization, and 41 genes are involved in GO:2000147: Positive regulation of cell motility. Based on the Gene Ontology cellular component, information was obtained that there were 64 genes related to GO:0005858: Axonemal dynein complex, 53 genes related to GO:0042383: Sarcolemma, and 31 genes related to GO:0005929: Cillium. Then, based on molecular function, 40 genes function as GO:0005102: Signaling receptor binding, 27 genes function as GO:0019900: Kinase binding, and 14 genes function as GO:0005178: Integrin binding. Furthermore, the results of pathway analysis show that there are 32 genes that play a role in hsa04066: HIF-1 signaling pathway, and 30 genes play a role in HSA-194138: Signaling by VEGF. (Figure 4)

Figure 2. P. palustre active compound. A) KEGG category of active compound based on literature. B) Comparison of top active compound based on its category. C) Bioactivity prediction score of the top three active compound based on Molinspiration database. D) Bioactivity prediction of the top five active compound based on Pa score by Way2Drug database. The plot and pie were constructed by SRPlot webservice (http://www. bioinformatics.com.cn/en)

Table 1. List of P. plustre active compounds and the drugs used as ligands. Identification number, CAS ID: downloaded from PubChem; CSID: downloaded from Chemspider; DB: downloaded from DrugBank

No. Metabolites as Ligand	Role as	Identification	Number
Brassinolide	Active compound	CAS ID	72962-43-7
D-(+)-Trehalose	Active compound	CAS ID	99-20-7
Linolenic Acid	Active compound	CAS ID	463-40-1
Diosgenin	Active compound	CAS ID	512-04-9
Capric acid	Active compound	CAS ID	334-48-5
Dodecanoic acid	Active compound	CAS ID	143-07-7
Arachidic acid	Active compound	CSID	10035
Butyric acid	Active compound	CAS ID	107-92-6
Prostaglandin B1	Active compound	CAS ID	13345-51-2
Succinic Acid	Active compound	CAS ID	110-15-6
2-Methoxyestradiol	Active compound	CAS ID	362-07-2
Glutaric Acid	Active compound	CAS ID	110-94-1
Gein	Active compound	CAS ID	585-90-0
Prostaglandin B2	Active compound	CAS ID	13367-85-6
Gamma-Linolenic acid	Active compound	CAS ID	506-26-3
Docosadienoate (22:2n6)	Active compound	CSID	4471979
S-Adenosylmethionine	Active compound	CAS ID	485-80-3
Prostaglandin E3	Active compound	CAS ID	802-31-3
PGF2alpha	Active compound	CAS ID	551-11-1
LysoPC(16:1(9Z)/0:0)	Active compound	CSID	24766525
Sucrose	Active compound	CAS ID	57-50-1
Geldanamycin	Active compound	CAS ID	30562-34-6
Mitomycin	Active compound	CAS ID	50-07-7
Cephamycin C	Active compound	CAS ID	38429-35-5
Estrone	Active compound	CAS ID	53-16-7
26 Neomycin sulfate	Active compound	CAS ID	1404-04-2
Dehydroepiandrosterone Sulfate	Active compound	CAS ID	651-48-9
Withanolide B	Active compound	CAS ID	56973-41-2
Thromboxane B2	Active compound	CAS ID	54397-85-2
Leukotriene B4	Active compound	CAS ID	71160-24-2
Pregnenolone	Active compound	CAS ID	145-13-1
4-Androstenediol	Active compound	CAS ID	1156-92-9
Ergocalciferol	Active compound	CAS ID	50-14-6
Roflumilast	Drug	DB	00207
Azithromycin	Drug	DB	00277
Naproxen	Drug	DB	01656
Ibuprofen	Drug	DB	01050
Aspirin	Drug	DB	00945
Theophylline	Drug	DB	00788

Table 2. Evaluation of receptor structure based on Ramachandran and the G-factor score

No	Gene	PDB ID	Most favoured Regions	Additional allowed regions	Generously allowed regions	Disallowed regions	G-factor overall average score
1	FN1	2CG6	91.7%	8.3%	0.0%	0.0%	-0.14
2	CDH2	1NCH	88.4%	10.3%	0.6%	0.6%	0.00
3	NOS3	1M9J	90.9%	8.9%	0.3%	0.0%	0.35

Table 3. The active site of the receptor based on COACH analysis

No	Gene	PDB ID	C-scores	Cluster	Active site
1 2	FN1 CDH2	2CG6 1NCH	0.19 0.88	11 113	Arg22, Lys24, Arg38, Arg40, Ile41, Cyc43, Thr44, Ile45, Ala46 Glu13, Asn14, Asp66, Glu68
3	NOS3	1M9J	0.95	363	Trp98, Ala101, Arg103, Cys104, Val105, Gly106, Leu113, Ser146, Met259, Phe273, Ser274, Gly275,
					Trp276, Met278, Glu281, Trp367, Phe393, Tyr395

Molecular docking analysis

Evaluating DEGs as receptors for molecular docking analysis Based on the 10 DEGs with the highest betweenness centrality scores, FN1, CDH2, and NOS3 were selected as receptors for molecular docking analysis due to the availability of their 3D structures in the database. However, an evaluation of the 3D structures of the three is still being carried out to increase the reliability and evidence of the research we are conducting. (Table 2). As the result, the three 3D receptor structures have more than 2% residues in the allowed region, so an energy minimization step is needed to obtain a model with better stereochemistry.²⁴ Following the active site prediction results, the active site residues of each protein are selected from the prediction results with the highest C-score. (Table 3)

The docking results are presented by comparing the binding affinity of the 33 active compounds of P. palustre to the three proteins compared to the binding affinity of the six drugs to the three proteins. The drugs used were roflumilast, azithromycin, naproxen, ibuprofen, aspirin, and theophylline. The drugs used were COPD drugs (roflumilast, azithromycin, and theophylline) and NSAID drugs (naproxen, ibuprofen, and aspirin). Based on the highest average binding affinity of each of the six active compounds and the highest drug, the six active compounds have the highest average binding affinity with a binding energy score of -7.5 kJ mol-1, compared to the average binding energy score of the six drugs of -6.0 kJ mol-1. The active compounds with the highest average binding affinity were withanolide B, diosgenin, DHEA-S, estrone, ergocalciferol, and gein. Meanwhile, the drugs with the highest average binding affinity were azithromycin, roflumilast, naproxen, ibuprofen, aspirin, and theophylline. Then, based on the binding affinity of each protein, withanolide B and DHEA-S are the two compounds that have the highest binding affinity. Based on the active compounds that interact, diosgenin and ergocalciferol are the other compounds with the highest binding affinity for FN1 (PDB ID: 2CG6) and CDH2 (PDB ID: 1NCH). Furthermore, estrone had the highest binding affinity on FN1 (PDB ID: 2CG6) and NOS3 (PDB ID: 1M9J), and Gein had the highest binding affinity on CDH2 (PDB ID: 1NCH) and NOS3 (PDB ID: 1M9J). The interesting thing is that only azithromycin, of the six drugs, is classified as having the highest binding affinity for NOS3 (PDB ID: 1M9J). (Figure 5)

Figure 3. Determining COPD-related genes. A) Median normalization result of sample data to be used (GSE198740 and GSE201465). B) Identification of significant gene expression from each sample on GSE. C) 58 Differentially Expressed Gene (DEG) were obtained from both GSEs. D) List of DEGs. E) GO term of DEG.

Figure 4. Network analysis. A) Protein-protein interaction network (PPI) analysis with Betweenness Centrality scores from DEGs and enriched genes. B) GeneOntology term based on the network. C) Top ten DEG's nodes with highest Betweenness Centrality score. D) Pathway enrichment.

Based on the bonds and interactions formed after the docking process, withanolide B forms the most hydrogen bonds in its interactions with CDH2 and NOS3, followed by DHEA-S, which also forms the most hydrogen bonds in its interactions with FN1 and NOS3. Meanwhile, in terms of total bonds and interactions, the most total bonds occurred in the results of docking compounds with NOS3. The NOS3-withanolide B formed 21 bonds with 7 van der Waals (vdW) interactions, followed by the NOS3-diosgenin, which formed 19 bonds with 6 vdW interactions, and the NOS3-DHEA-S, which formed 17 bonds with 2 vdW interactions. (Supplementary table 1, Supplementary figure 1)

Pathway related to the receptors:

We sorted the pathway enrichment results from the three receptors based on the p-value score, showing that NOS3 is known to play a role in R-HSA-9827857: Specification of Primordial Germ Cells and R-HSA-203754: NOSIP- mediated eNOS trafficking. CDH2 and FN1 are known to play a role in R-HSA-8957275: Post-translational protein phosphorylation and R-HSA-381426: Regulation of Insulin-like Growth Factor (IGF) transport and uptake by Insulin-like Growth Factor Binding Proteins (IGFBPs). Furthermore, FN1 is also known to play a role in other pathways, including ALK mutants binding TKIs, LDL remodelling, scavenging by Class H receptors, interleukin signalling, and RUNX regulation.

Not all of these pathways, such as specification of primordial germ cells, post-translational protein phosphorylation, regulation of Insulin-like Growth Factor (IGF) transport and uptake by Insulin-like Growth Factor Binding Proteins (IGFBPs), and ALK mutants binding TKIs, have research evidence that states their role in COPD. However, there are two pathways, namely NOSIP-mediated eNOS trafficking²⁵ and Interleukin-4 and Interleukin-13 signaling²⁶, which are reported to have a role in shaping or influencing the severity of COPD sufferers. eNOS (endothelial nitric oxide synthase) is an enzyme that plays a role in regulating NO production, where the NOSIP (eNOS interacted protein) pathway mediated eNOS trafficking is a pathway related to the interaction of NOSIP in the eNOS oxygenase domain, thereby inhibiting the activity of eNOS. In COPD cases, the presence of NO has an important role as a pulmonary vasodilator, meaning that disruption of eNOS activity, which regulates NO production, can affect COPD conditions.²⁷ However, NO also plays a role in inducing inflammation so that it becomes a target for anti-inflammatory therapy, including therapy for endothelial dysfunction. (Csoma et al., 2019) Similar to NO, IL-4 and IL-13 also have a major role in the inflammatory process, where inflammation is one of the factors causing the severity of COPD²⁶, so it is estimated that IL-4 and IL-13 can affect COPD in both directions. (Supplementary table 2)

Figure 5. The more negative the bond affinity score, the higher the probability of an interaction forming. A) Comparison of the five ligands with the lowest binding energy with their average binding energy to all receptors.

B) Comparison of the five drugs with their average binding energy to all receptors. C) The five ligands with the lowest binding energy to FN1 (PDB ID: 2CG6). D) The five ligands with the lowest binding energy to CDH2 (PDB ID: 1NCH) E) The five ligands with the lowest binding energy to NOS3 (PDB ID: 1M9J). The interesting one that was obtained from this molecular docking is that it is known that Azithromycin, which is an antibiotic, has a high binding affinity compared to the other four drugs. Further research to investigate possible pharmacodynamic effects due to binding affinity is interesting to carry out.

DISCUSSION:

COPD is a common chronic condition in the community, causing death and disability. Emphysema, characterized by airway inflammation and oxidative stress, is a major molecular mechanism in COPD. Our analysis identified several key marker genes from the COPD dataset, including FN1, CDH2, and NOS3. Docking analysis revealed that certain active compounds from P. palustre exhibit high binding affinity to these proteins, potentially inhibiting their activity and providing anti-inflammatory effects.

FN1 is a prognostic biomarker involved in immune infiltration in diseases such as gastric cancer and diabetic nephropathy, where inflammation is a significant component^28,29. CDH2, which encodes N-cadherin, is essential for neuronal cell migration and intracellular adhesion, although it is not directly related to inflammation^30,31. NOS3 produces nitric oxide, which is essential for a variety of bioactivities, including immune responses.^32,33

Active compounds of P. palustre with the highest potential anti-inflammatory bioactivity include brassinolide, trehalose, leukotriene B4, linolenic acid, and diosgenin. Brassinolide, a plant steroid hormone from Brassica napus, affects the GSK3 pathway, reduces inflammation, and accelerates wound healing^34,35. Trehalose, a nonreducing disaccharide, has therapeutic effects by inhibiting the NF-kB pathway and preventing the secretion of proinflammatory cytokines, thereby protecting human corneal cells from inflammation.^36,37

Leukotriene B4 (LTB4) is a proinflammatory lipid mediator involved in low-grade inflammation, neurodegeneration, and the formation of the tumor microenvironment^38,39. Linolenic acid and diosgenin are well known for their anti-inflammatory properties^40–42. Although research on the therapeutic effects of P. palustre is limited, its potential as a carrier or enhancer of drug efficacy remains promising.

Our study predicted the pharmacokinetics of secondary metabolites of P. palustre, specifically their potential to inhibit FN1, CDH2, and NOS3. Although these genes are not the primary cause of COPD, and other factors such as bacterial infection also contribute, our findings suggest the potential for COPD treatment through inhibition of these proteins. Understanding the benefits of active secondary metabolites in herbal plants can encourage the consumption of nutritious foods and reveal their potential in treating diseases associated with these proteins.

CONCLUSION:

From this research, it was concluded that the active compounds with the highest anti-inflammatory bioactivity found in P. palustre were brassinolide, D-(+)-trehalose, leukotriene B4, linolenic acid, and diosgenin. Meanwhile, the DEGs from the COPD dataset obtained are FN1, CDH2, NOS3, DNAH9, DNAH11, CACNA1H, CACNG4, TNC, BAI1, and DNAAF1. Several active compounds have high binding affinity for FN1, CDH2, and NOS3, which are expected to induce anti-inflammatory activity through the inhibition that occurs. However, the results of this study require further research using in vitro and in vivo methods to evaluate and confirm the benefits and side effects of targeting those proteins.

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Received on 08.12.2023 Revised on 12.09.2024

Accepted on 20.03.2025 Published on 01.10.2025

Available online from October 04, 2025

Research J. Pharmacy and Technology. 2025;18(10):4773-4780.

DOI: 10.52711/0974-360X.2025.00687

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Creative Commons License.

Computational Screening of Platostoma palustre Metabolites for Anti- Inflammation Bioactivity in the Chronic Obstructive Pulmonary Disease Gene Dataset