Abstract
In Indonesia, herbs are used to treat various diseases, including hypertension. A network pharmacology approach can be a breakthrough in research strategy by discovering a new drug using the multicomponent-multitarget concept. The protein-compound mechanism in plants is a potential target for creating new combination formulas that will be developed into new drug discoveries in hypertension therapy. This study aims to determine a new formula from Indonesian medicinal plants using a network pharmacological approach based on molecular mechanisms and protein-compound-plant interactions that have antihypertensive effects. We use graph mining and machine learning to explore compounds and plant-related hypertension target proteins and determine plant formulations based on the molecular mechanisms of hypertension-related target proteins. A search found 78 Indonesian medicinal plants that contain trans zeatin, seselin, sesamin, and other compounds effective as antihypertensive agents. The study obtained a formula with a combination of 2 plants, including 31 formulas bound to 5 target proteins, and a combination of 3 plants, with 85 formulas bound to 6 hypertension target proteins. The plant formula illustrates the involvement of several mechanisms in lowering blood pressure using protein-plant compounds. Some of the plant formula’s constituents, such as antioxidants and anti-inflammatories, have blood pressure-lowering properties. Proteins mitogen-activated protein kinase 3, peroxisome proliferator-activated receptor alpha, estrogen receptor 1, endothelin 1, vascular endothelial growth factor A, and fos proto-oncogene are possible biomarkers that could be used to target hypertension medication in the coming years.
| Original language | English |
|---|---|
| Pages (from-to) | 252-262 |
| Number of pages | 11 |
| Journal | Journal of Applied Pharmaceutical Science |
| Volume | 14 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - Mar 2024 |
Keywords
- Formulas
- hypertension
- molecular mechanism
- network pharmacology
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