Cyclin‐dependent kinase 11 (CDK11) plays a critical role in cell cycle regulation and transcriptional control, making it a promising target for therapeutic intervention in cancer and other proliferative disorders. This study employs computationally driven approaches encompassing homology modelling, molecular dynamic simulations, ultralarge‐scale virtual screening and medicinal chemistry optimisation to develop a series of novel inhibitors of CDK11. Our virtual screening pipeline led to the identification of two initial hits (compounds 3 and 4 ), which were further evaluated through structure–activity relationship (SAR) studies. Together with structure‐guided molecular docking and design, these SAR analyses revealed key structural motifs and functional groups that are crucial for inhibitory activity and selectivity, providing insights into the efficient hit‐to‐lead optimisation. Compound 37 emerged as an optimised potent and selective CDK11 inhibitor (IC 50 4 nM, kinome panel clean). In a lung tumour model, mice dosed twice daily with 100 mg/kg of compound 37 showed ∼30% tumour growth inhibition. Both in vitro absorption, distribution, metabolism and excretion and in vivo mouse pharmacokinetics (PK) profiling indicated that compound 37 possesses excellent PK/pharmacodynamic properties, positioning the compound for further development and evaluation as a lead candidate for CDK11‐targeted therapy. Meanwhile, the series of compounds developed throughout this study represent novel tools for studying CDK11‐mediated pathophysiology. The integration of in silico modelling, screening and structure‐based drug design provides a robust strategy for accelerating the identification of potent and selective inhibitors for other CDK families.
Mak et al. (Thu,) studied this question.