In Silico and Multivariate Analysis of Herbal Compounds in Asthma Inflammation: Exploring Alternatives to Corticosteroids
Corticosteroids are among the most common therapies for asthma-controlled treatment but, due to their side effects, alternative therapies are being developed. This in silico study intends to identify the distinct anti-asthma inflammatory activity of corticosteroids (budesonide and prednisolone) to herbal compounds (cannabidiol, andrographolide, and eucalyptol) toward GR, CB1R, CB2R, TNF-α, IL-1β, and TGFBR1 targets. In silico studies were carried out using PLANTS and AutoDock software. Additional software such as YASARA, MarvinSketch, BIOVIA Discovery Studio Visualizer, and PyMol were used for docking preparation and visualization. RStudio was used to perform multivariate analysis of binding affinity values. ADME characteristics were predicted using pkCSM. Both docking applications identified the TNF-α-eucalyptol interaction as the weakest binding affinity and the CB2-reference exhibited the strongest. The highest binding scores in PLANTS and AutoDock were -133.38 and -13.75 kcal/mol, respectively, while the lowest were -48.12 and -4.19 kcal/mol. Budesonide and prednisolone's binding activities were closest to cannabidiol and andrographolide (similarity>73%). In comparison to other chemicals, eucalyptol has demonstrated the most distinct affinity to the targets (similarity<50%). From all the ligands’ ADME characteristics, prednisolone potentially offers the most comprehensive benefits in asthma inflammation treatment, although with higher risk of side effects than budesonide. On the other hand, the herbal compounds demonstrate profiles suitable for systemic therapy, with differences in distribution and clearance that influence their action and side effects. In conclusion, the herbal compounds could be alternative therapies to budesonide and prednisolone in asthma. However, eucalyptol predictably has lower activity.
Institutions
- Udayana University (ID)
Publication Details
- Journal
- Tropical Journal of Natural Product Research
- Published
- 2025-02-28
- DOI
- https://doi.org/10.26538/tjnpr/v9i2.51
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00