Integrating the Network Pharmacology and Molecular Dynamic Simulation to Reveal Pharmacological Mechanism of Arcangelisi Flava (L.) Merr in Treating Inflammation

Arcangelisi flava (L.) Merr, a medicinal plant traditionally used in Maribu-Sentani-Papua for various ailments, has shown potential in treating inflammation. However, its pharmacological mechanism of action remains unclear. This study aimed to elucidate the mechanism of pain relief compounds in A. flava using network pharmacology, molecular docking, molecular dynamics (MD) simulation, drug-likeness prediction, and ADMET profiling. Network pharmacology analysis identified COX-1 and COX-2 as core targets of A. flava in inflammation treatment. Among the compounds studied, 1-3-hydroxy-berberine demonstrated the highest potential as a COX-1 and COX-2 inhibitor, with free binding energies of -42.08 and -37.58 kcal/mol, respectively. Drug-likeness prediction based on Lipinski's rule supported its potential as a drug candidate. Pharmacokinetic predictions using pkCSM revealed favorable properties, including high Caco2 permeability (1.073) and human intestinal absorption (97.87%). The compound showed low central nervous system distribution and blood-brain barrier penetration. Metabolic predictions indicated CYP3A4 metabolism and substrate affinity for CYP2C19, CYP2C9, and OCT2. Toxicity assessments suggested that 1-3-Hydroxy-berberine is non-mutagenic, has low acute toxicity, and poses no significant environmental or skin allergy risks. These findings highlight the potential of 1-3-Hydroxy-berberine as a promising COX-1 and COX-2 inhibitor derived from A. flava, warranting further investigation for its anti-inflammatory properties.

Publication Details

Journal
Biointerface Research in Applied Chemistry
Published
2025-06-09
DOI
https://doi.org/10.33263/briac153.044
Citations
2
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
3.01

Funders

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article

Integrating the Network Pharmacology and Molecular Dynamic Simulation to Reveal Pharmacological Mechanism of Arcangelisi Flava (L.) Merr in Treating Inflammation

2 citations
Biointerface Research in Applied Chemistry
Computational Drug Discovery Methods
3.01
article

Integrating the Network Pharmacology and Molecular Dynamic Simulation to Reveal Pharmacological Mechanism of Arcangelisi Flava (L.) Merr in Treating Inflammation

article en
2 citations

Abstract

Arcangelisi flava (L.) Merr, a medicinal plant traditionally used in Maribu-Sentani-Papua for various ailments, has shown potential in treating inflammation. However, its pharmacological mechanism of action remains unclear. This study aimed to elucidate the mechanism of pain relief compounds in A. flava using network pharmacology, molecular docking, molecular dynamics (MD) simulation, drug-likeness prediction, and ADMET profiling. Network pharmacology analysis identified COX-1 and COX-2 as core targets of A. flava in inflammation treatment. Among the compounds studied, 1-3-hydroxy-berberine demonstrated the highest potential as a COX-1 and COX-2 inhibitor, with free binding energies of -42.08 and -37.58 kcal/mol, respectively. Drug-likeness prediction based on Lipinski's rule supported its potential as a drug candidate. Pharmacokinetic predictions using pkCSM revealed favorable properties, including high Caco2 permeability (1.073) and human intestinal absorption (97.87%). The compound showed low central nervous system distribution and blood-brain barrier penetration. Metabolic predictions indicated CYP3A4 metabolism and substrate affinity for CYP2C19, CYP2C9, and OCT2. Toxicity assessments suggested that 1-3-Hydroxy-berberine is non-mutagenic, has low acute toxicity, and poses no significant environmental or skin allergy risks. These findings highlight the potential of 1-3-Hydroxy-berberine as a promising COX-1 and COX-2 inhibitor derived from A. flava, warranting further investigation for its anti-inflammatory properties.

Biointerface Research in Applied ChemistryVol. 15(3)
Badan Riset dan Inovasi Nasional
Openalex Percentile: Top 8%
Computational Drug Discovery Methods
3.01
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