Elaborate ligand-based modeling coupled with multiple linear regression and k nearest neighbor QSAR analyses unveiled...

Area of Science:

• Medicinal Chemistry
• Computational Biology
• Oncology

Background:

• Mammalian target of rapamycin (mTOR) is crucial for cell growth and survival.
• mTOR hyperactivation is common in cancer, making it a therapeutic target.
• Developing potent mTOR inhibitors is vital for effective cancer treatment.

Purpose of the Study:

• To explore structural requirements for potent mTOR inhibitors.
• To develop predictive quantitative structure-activity relationship (QSAR) models for mTOR ligands.
• To identify novel mTOR inhibitory leads for cancer therapy.

Main Methods:

• Employed pharmacophore modeling and QSAR analysis on 210 known mTOR ligands.
• Utilized genetic function algorithm (GFA), k-nearest neighbor (kNN), and multiple linear regression (MLR).
• Validated models by identifying and testing new inhibitors from the NCI database.

Main Results:

• Developed self-consistent and predictive QSAR models for mTOR inhibitors.
• Optimized pharmacophore hypotheses with exclusion spheres for improved accuracy.
• Identified several new promising mTOR inhibitory leads, with the most potent showing an IC50 of 48 nM.

Conclusions:

• The study successfully elucidated structural requirements for potent mTOR inhibition.
• Validated QSAR models and pharmacophore hypotheses led to the discovery of novel mTOR inhibitors.
• The identified leads demonstrate significant potential for further development in cancer therapy.