Quantitative structure-activity relationship study on BTK inhibitors by modified multivariate adaptive regression...

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Pharmacology

Background:

• Bruton's tyrosine kinase (BTK) is a critical target for B-cell malignancies and autoimmune diseases.
• Developing potent and selective BTK inhibitors is a key area of pharmaceutical research.

Purpose of the Study:

• To design and identify novel pyridine and pyrimidine-based Bruton's tyrosine kinase (BTK) inhibitors.
• To establish robust quantitative structure-activity relationship (QSAR) models for predicting BTK inhibitory activity.
• To explore structure-activity relationships (SAR) for optimizing inhibitor design.

Main Methods:

• Utilized two- and three-dimensional quantitative structure-activity relationship (2D and 3D-QSAR) analyses.
• Employed genetic algorithm optimized multivariate adaptive regression spline (GA-MARS) and comparative molecular similarity index analysis (CoMSIA) methods.
• Generated a new molecular database using molecular fragment replacement (MFR) for enhanced prediction.

Main Results:

• Developed statistically significant 2D-QSAR (Q(2)=0.884, r(2)=0.929, r(2)pred=0.878) and 3D-QSAR (q(2)=0.616, r(2)=0.987, r(2)pred=0.905) models.
• Identified key molecular descriptors and visualized SAR using 3D-CoMSIA contour maps.
• Successfully predicted and selected 25 novel pyridine and pyrimidine derivatives as potential BTK inhibitors.

Conclusions:

• The developed QSAR models are efficient tools for discovering novel potent BTK inhibitors.
• The identified compounds represent promising candidates for further preclinical investigation.
• This computational approach accelerates the drug discovery process for BTK-targeted therapies.