Introducing a pyrazolopyrimidine as a multi-tyrosine kinase inhibitor, using multi-QSAR and docking methods

Area of Science:

• Medicinal Chemistry
• Computational Drug Discovery
• Oncology

Background:

• Cancer is a major global health challenge, often driven by overexpressed tyrosine kinase receptors.
• Targeting these receptors is crucial for developing effective anticancer therapies.
• Computational methods like QSAR and docking accelerate drug discovery.

Purpose of the Study:

• To identify novel anticancer agents targeting tyrosine kinase receptors.
• To develop a multidimensional quantitative structure-activity relationship (QSAR) model for pyrazolopyrimidine compounds.
• To evaluate the binding affinity of potential drug candidates using molecular docking.

Main Methods:

• Utilized a backpropagation artificial neural network for multidimensional QSAR analysis.
• Screened 26 pyrazolopyrimidine compounds to identify key structural features for anticancer activity.
• Performed molecular docking simulations to assess the interaction of eight candidate compounds with EGFR, FGFR4, PDGFRA, and VEGFR2.

Main Results:

• The QSAR model demonstrated high validity and accuracy.
• Compound 1u showed favorable binding interactions with EGFR, PDGFRA, and VEGFR2.
• Compound 1u exhibited excellent binding free energy for FGFR4 (-9.22 kcal/mol), surpassing known anticancer drugs gefitinib and regorafenib.

Conclusions:

• Compound 1u is proposed as a potent anticancer agent targeting tyrosine kinase receptors.
• The study highlights the efficacy of integrated QSAR and docking approaches in drug discovery.
• Further investigation of compound 1u is warranted for its therapeutic potential against various cancers.