Dual therapeutic potential of Scoparia dulcis in combating hyperglycemia and inflammation in diabetes through network pharmacology and in silico analysis

Abstract

Chronic low-grade inflammation is a key contributor to the pathogenesis and complications of diabetes, leading to issues such as joint pain, skin disorders, periodontal disease, and neuropathy. Therefore, targeting inflammatory pathways has emerged as a promising therapeutic strategy for both the prevention and management of diabetes and its associated comorbidities. Natural products with dual anti-inflammatory and antidiabetic properties have gained significant interest, with Scoparia dulcis showing notable therapeutic potential. This study aimed to evaluate the efficacy of this herbal medicine in alleviating inflammation in diabetic patients using an integrative in silico approach, incorporating network pharmacology, molecular docking, and molecular dynamics simulations. Initial screening of compounds focused on their ability to inhibit key pathological targets implicated in diabetes-related inflammation. Pathway enrichment analysis revealed significant involvement in the AGE-RAGE signaling pathway, lipid metabolism, atherosclerosis pathways, and the hypoxia-inducible factor 1 (HIF-1) pathway. Ten critical molecular targets were identified, with TNF-α being the most prominent. Molecular docking followed by 200 ns molecular dynamics simulations assessed the binding affinity of TNF-α with the top ten selected compounds, revealing strong and stable interactions with essential active site residues. Furthermore, ADMET analysis and density functional theory (DFT) evaluations highlighted the therapeutic potential of these compounds as promising lead candidates for drug development. Existing literature supports the antidiabetic effects of these bioactive compounds, reinforcing the in silico findings. Thus, Scoparia dulcis represents a potential adjunct or alternative therapy for diabetic patients with chronic inflammation, offering a multifaceted approach to disease management.

Keywords: DFT; Diabetes mellitus; Docking; Inflammation; Molecular dynamics; Network Pharmacology.

Fig. 1

Comprehensive network pharmacology investigation of diabetes and inflammation-related targets linked to SD. (A) Venn diagram illustrating overlapping target genes among SD, diabetes, and inflammation. (B) Compound–target interaction network showing the relationships between active compounds (blue diamonds) and intersecting target genes (pink rectangles). (C) PPI network of shared targets constructed using the STRING database. (D) Top 10 core target genes identified through topological analysis. The circular nodes indicate targets that are associated with compounds, with darker colors reflecting a stronger correlation.

Fig. 2

Gene Ontology (GO) enrichment analysis of target genes is summarized across three domains: Biological Process (BP), Cellular Component (CC), and Molecular Function (MF). Bubble color intensity, ranging from red (most significant) to blue (least significant), represents -log₁₀(FDR) values. Bubble size reflects the number of genes associated with each pathway.

Fig. 3

Dot plot of KEGG enrichment analysis of target genes. The bubble and bar color range from red to blue, representing the decreasing − log10(FDR) value of the pathway, and the size of the circle and the length of the bar indicate the number of target genes associated with each pathway.

Fig. 4

Computational screening via molecular docking of the core targets and bioactive compounds associated with SD..

Fig. 5

3D interactions between top 10 selected compounds and protein TNF-α. (T1). Kaempferol. (T2). Luteolin. (T3). Quercetin B. (T4). Scutellarein. (T5). Cirsimaritin. (T6). Hispidulin. (T7). Acerosin. (T8). Apigenin. (T9). Acacetin. (T10). Morin. (T11). Control ligand.

Fig. 6

Backbone atom RMSD of TNF-α in apoprotein form (T0) and in complexes with 10 ligands. (T1). Kaempferol. (T2). Luteolin. (T3). Quercetin B. (T4). Scutellarein. (T5). Cirsimaritin. (T6). Hispidulin. (T7). Acerosin. (T8). Apigenin. (T9). Acacetin. (T10). Morin. (T11). Control ligand.

Fig. 7

RMSF (A), Rg (B), SASA (C) values for the complexes involving the TNF-α protein with ligands.

Fig. 8

3D FEL plots of TNF-α in apoprotein form (T0) and in complexes with 10 ligands. (T1). Kaempferol. (T2). Luteolin. (T3). Quercetin B. (T4). Scutellarein. (T5). Cirsimaritin. (T6). Hispidulin. (T7). Acerosin. (T8). Apigenin. (T9). Acacetin. (T10). Morin. (T11). Control ligand.

Fig. 9

Electron density maps of HOMO and LUMO of ten selected compounds and control ligand.