In Silico-Enabled Discovery and Development of Potent and Selective CDK11 Inhibitors.

Area of Science:

• Medicinal Chemistry
• Computational Biology
• Pharmacology

Background:

• Cyclin-dependent kinase 11 (CDK11) is crucial for cell cycle regulation and transcription.
• CDK11 is a promising therapeutic target for cancers and proliferative diseases.

Purpose of the Study:

• To develop novel, potent, and selective CDK11 inhibitors using a computational drug design strategy.
• To identify lead compounds for CDK11-targeted cancer therapies.

Main Methods:

• Homology modelling, molecular dynamics simulations, and virtual screening were employed.
• Structure-activity relationship (SAR) studies, molecular docking, and medicinal chemistry optimization were performed.
• In vitro and in vivo assays were used to evaluate compound efficacy and pharmacokinetic properties.

Main Results:

• Two initial hits (compounds 3 and 4) were identified via virtual screening.
• Compound 37 emerged as a potent and selective CDK11 inhibitor (IC50 = 4 nM).
• Compound 37 demonstrated approximately 30% lung tumor growth inhibition in vivo and possessed favorable pharmacokinetic properties.

Conclusions:

• The study successfully identified potent and selective CDK11 inhibitors using an integrated computational approach.
• Compound 37 is a promising lead candidate for CDK11-targeted cancer therapy.
• The developed compounds serve as valuable tools for studying CDK11's role in disease.