Predicting Isoform-specific Binding Selectivities of Benzensulfonamides Using QSAR and 3D-QSAR

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Drug Design

Background:

• Designing isoform-specific inhibitors presents a significant hurdle in developing novel therapeutic agents.
• Carbonic anhydrase (CA) isoforms are important drug targets, but achieving selectivity is challenging.

Purpose of the Study:

• To develop robust quantitative structure-activity relationship (QSAR) and 3D-QSAR models for benzenesulfonamide derivatives.
• To enhance the isoform selectivity of potential therapeutic agents targeting carbonic anhydrase (CA).

Main Methods:

• Utilized Schrödinger's PHASE module for 3D-QSAR and E-DRAGON/R software for 2D-QSAR on 40 benzenesulfonamide derivatives.
• Developed 25 QSAR models (2D and 3D) using affinity and selectivity-based protocols for six CA isoforms.
• Created a novel descriptor T(OH..Cl) to target CA XII affinity.

Main Results:

• Achieved satisfactory model quality with high statistical validation (F, R2, R2ADJ) and cross-validation (LOO).
• Determined Applicability Domains for 2D-QSAR models and rationalized 3D-QSAR models via molecular docking.
• Compared affinity and selectivity-based QSAR protocols, demonstrating their effectiveness.

Conclusions:

• The developed QSAR models offer valuable insights for designing sulfonamide compounds with improved CA isoform selectivity.
• This research facilitates the development of more targeted and effective therapeutic agents.