Topomer-CoMFA proposed as a tool to construct dual EGFR/HER-2 models

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Pharmacology

Background:

• Protein kinases like HER-2 and EGFR are crucial in cancer development.
• Classical Comparative Molecular Field Analysis (CoMFA) faces challenges in multitarget drug design due to alignment issues.
• Topomer-Comparative Molecular Field Analysis (CoMFA) offers an alignment-independent alternative for 3D model generation.

Purpose of the Study:

• To develop a unique dual 3D Quantitative Structure-Activity Relationship (QSAR) model for predicting inhibitory activities against both EGFR and HER-2.
• To combine the sum of activities with Topomer-CoMFA for enhanced multitarget drug discovery.
• To propose structural modifications for improved dual-target compound efficacy.

Main Methods:

• Utilized 88 compounds from literature, divided into training (71) and test (17) sets.
• Converted IC50 values to pIC50 and summed activities for EGFR and HER-2.
• Applied Topomer-CoMFA to generate a dual 3D model using summed pIC50 as the dependent variable.

Main Results:

• A statistically robust Topomer-CoMFA model was successfully developed.
• The model demonstrated strong predictive capability for the dual inhibitory activity of new compounds.
• Proposed structural modifications led to suggestions for novel derivatives with enhanced dual-target activity.

Conclusions:

• The combination of summed activities and Topomer-CoMFA is an effective strategy for multitarget drug discovery.
• This approach provides a robust method for predicting biological activities in complex systems.
• The developed model aids in designing improved therapeutic agents targeting both EGFR and HER-2.