Elucidation of Chromolaena Odorata Extract’s Pharmacological Potential Through Integration of Network Pharmacology and Molecular Docking Study
Network pharmacology, an interdisciplinary field that combines principles from pharmacology, systems biology, and network science, provides a robust framework for exploring the intricate relationship between biological systems and pharmacologically active compounds. This study focuses on the herbal medicine Chromolaena odorata, known as “Daun kapal terbang” in Malaysia. This plant, renowned for its diverse medicinal properties, underwent thorough analysis, revealing its anti-inflammatory, antimicrobial, anticancer, antidiabetic, and wound-healing attributes. However, a deeper understanding of its pharmacological mechanism of action remains unclear. This study addresses this gap by conducting network pharmacology analysis and molecular docking studies on C. odorata. In this current work, three identified compounds from C. odorata, namely squalene, linolenic acid and hexadecanoic acid, were subjected to compound-target identification via SwissTargetPrediction and Cytoscape 3.10.1 visualization tools. Subsequently, Gene Ontology enrichment was performed to analyze gene clusters within the network. Finally, AutoDOCK tools were employed to elucidate the protein-ligand interaction among selected targets. PPARA was identified as the most important target among all the key proteins based on the binding affinity and GO enrichment analysis. PPARA displayed the strongest binding affinities: -9.6 kcal/mole for squalene, -7.6 kcal/mole for linolenic acid, and -7.0 kcal/mole for hexadecanoic acid, surpassing the affinities observed for PGR and RORA. This comprehensive study not only emphasizes the significance of network pharmacology in delineating herbal remedy potentials but also underscores its implications for advancing drug development, particularly in designing novel therapeutics based on targeted mechanisms.
Authors
- Norazlan Mohmad Misnan (ORCID: https://orcid.org/0000-0003-0942-9543)
- Iswaibah Mustafa (ORCID: https://orcid.org/0000-0001-7247-3542)
- Mohd Shahrul Nizam Salleh (ORCID: https://orcid.org/0000-0002-3195-8989)
- Nur ‘Ainun Mokhtar (ORCID: https://orcid.org/0000-0002-0670-5701)
- Norzila Mohd (ORCID: https://orcid.org/0000-0001-5317-1688)
- Fatahiya Mohamed Tap (ORCID: https://orcid.org/0000-0001-6961-6901)
- Nurhannani Ahmad Rozani
Publication Details
- Journal
- Pertanika journal of science & technology
- Published
- 2025-04-23
- DOI
- https://doi.org/10.47836/pjst.33.s3.09
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Universiti Teknologi MARA