Targeting Hypoglycemic Natural Products from the Cloud Forest Plants Using Chemotaxonomic Computer-Assisted...

Area of Science:

• Pharmacology
• Ethnobotany
• Natural Product Chemistry

Background:

• Cloud forests are biodiverse ecosystems rich in unexplored plants with potential medicinal compounds.
• Lack of ethnopharmacological data necessitates rational plant selection strategies for discovering pharmacologically active compounds.

Purpose of the Study:

• To develop a strategy for identifying plants with hypoglycemic activity from cloud forest ecosystems.
• To screen a cloud forest metabolite library for compounds targeting enzymes involved in glucose regulation.

Main Methods:

• Creation of a cloud forest (CF) metabolite library and ligand-based virtual screening for hypoglycemic activity.
• In vitro enzyme inhibition assays (α-amylase, α-glucosidase, dipeptidyl peptidase IV [DPP-IV]) on plant extracts.
• Metabolomic analysis and ensemble molecular docking to identify and evaluate bioactive metabolites.

Main Results:

• Identification of twelve plants across four botanical families exhibiting hypoglycemic activity.
• Sida rhombifolia demonstrated selective DPP-IV inhibition, with acanthoside D and cis-tiliroside identified as key compounds.
• Annotation of twenty-seven metabolites over-accumulated in S. rhombifolia compared to S. glabra.

Conclusions:

• The study presents a novel strategy for discovering bioactive natural products, complementing traditional methods.
• Sida rhombifolia is a promising source for developing novel hypoglycemic agents, particularly targeting DPP-IV.
• The workflow facilitates rational selection of plants for experimental studies in natural product chemistry.