Docking-based 3D-QSAR study for selectivity of DPP4, DPP8, and DPP9 inhibitors

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Pharmacology

Background:

• Dipeptidyl peptidase-4 (DPP4) inhibitors are crucial for managing type 2 diabetes.
• Selectivity against related enzymes like DPP8 and DPP9 is essential to minimize side effects.
• Understanding structure-activity relationships is key for designing potent and selective inhibitors.

Purpose of the Study:

• To develop a predictive 3D-QSAR model for designing selective DPP4 inhibitors.
• To evaluate newly synthesized cyclic hydrazine derivatives for their inhibitory activity and selectivity against DPP4, DPP8, and DPP9.
• To identify key interactions responsible for DPP4 selectivity.

Main Methods:

• A 3D-Quantitative Structure-Activity Relationship (3D-QSAR) study using the Comparative Molecular Field Analysis (CoMFA) method.
• Fast-docking simulations were employed for model development.
• In vitro assays were conducted to evaluate the inhibitory activity of compounds against DPP4, DPP8, and DPP9.

Main Results:

• A highly predictive CoMFA model was successfully generated for DPP4, DPP8, and DPP9 inhibitors (q² values ranging from 0.58 to 0.68, r² values from 0.96 to 0.98).
• The model's validity was confirmed using a test set, including newly synthesized compounds KR64300 and KR64301.
• Newly synthesized compounds KR64300 and KR64301 demonstrated excellent selectivity for DPP4 over DPP8 and DPP9.

Conclusions:

• The developed 3D-QSAR model provides a reliable framework for designing selective DPP4 inhibitors.
• Interactions with the S3 and S1' sites of DPP4 are critical for achieving selectivity over isozymes.
• The synthesized cyclic hydrazine derivatives, KR64300 and KR64301, represent promising candidates for selective DPP4 inhibition.