Screening of SGLT2 inhibitors based on virtual screening and cellular experiments

Area of Science:

• Medicinal Chemistry
• Computational Drug Discovery
• Pharmacology

Background:

• Type 2 diabetes mellitus (T2DM) is a global health concern requiring novel therapeutic strategies.
• Sodium-glucose cotransporter 2 (SGLT2) inhibitors represent a key class of antidiabetic drugs.
• Developing new SGLT2 inhibitors with improved efficacy and specificity is crucial.

Purpose of the Study:

• To identify novel hit compounds as SGLT2 inhibitors using computational and experimental methods.
• To evaluate the inhibitory potential of identified compounds against SGLT2.
• To explore the binding interactions of hit compounds with the SGLT2 protein.

Main Methods:

• Virtual screening and three-dimensional pharmacophore modeling were employed for initial compound selection.
• Quantitative structure-activity relationship (QSAR) modeling was utilized.
• Biological assays (glucose uptake in 293T cells) and molecular docking were performed for validation and interaction analysis.

Main Results:

• Twenty compounds were initially selected via virtual screening as potential diabetes treatments.
• Thirteen hit compounds demonstrated strong SGLT2 inhibitory properties, with compound 2 showing the lowest glucose uptake.
• Molecular docking confirmed stable, specific hydrogen-bonding interactions between the hit compounds and SGLT2.

Conclusions:

• Thirteen novel compounds with significant SGLT2 inhibitory activity were identified.
• These compounds exhibit potential as new therapeutic agents for managing diabetes.
• The study validates the combined use of computational and experimental approaches for drug discovery.