Abstract 49P
Background
Cyclin-dependent kinase (CDK), a major cell cycle regulator; associated with several oncogenes and over-expressed in almost all types of major cancers, serves as an important anti-cancer therapeutic target. Due to high toxicity issues of pan inhibitors, subtype-selective inhibitors are needed. Unlike CDK4/6 inhibitors (like Palbociclib, Ribociclib), there has not been much success for CDK1 selective inhibitors. Inhibiting CDK1 is also reported as one of the promising strategies to target cancers over-expressing MYC oncoprotein due to its undruggable nature. In our study, we are working to develop subtype-selective CDK1 inhibitors using computational approaches.
Methods
A 3D QSAR pharmacophore model for CDK1 was built and used to screen different databases like DrugBank, Selleckchem and Otava Libraries to generate hits as potential CDK1 inhibitors. The hits were filtered based on their ADMET profiles. Filtered hits were docked in the binding site of CDK1 and final hits were selected based on the interaction analysis. The final ligand-protein complexes were evaluated for their stability through MD simulations for 100ns.
Results
A highly correlating (r2=0.958) model was generated and validated. Above mentioned libraries were screened and hits generated were filtered based on fit value, ADMET and toxicity parameters. The selected hits when docked in the binding site of CDK1 yielded 49 hits with better scores than the control. These hits were then analyzed for their interactions with site residues of both CDK1 and CDK4/6 to maximize their selectivity. Finally, MD simulation studies were performed for the top 10 hits and their binding energies were calculated. As a result, 8 lead molecules were selected based on these studies.
Conclusions
In the current study, we have identified 8 hits as potential CDK1 inhibitors. To accelerate the drug development, we have used multiple databases and validated our screened hits by evaluating their binding affinity to CDK1 using different in-silico approaches. These results need to be validated in-vitro and in-vivo for confirmation of their anti-cancer therapeutic potential. Leads generated from this study may be useful in the development of target-specific anti-cancer therapies in the future.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
Molecular Modelling and Anti-Cancer Drug Development Lab supervised by Professor Madhu Chopra.
Funding
University of Delhi, Department of Biotechnology - Bioinformatics Facility, Council of Scientific and Industrial Research, India.
Disclosure
All authors have declared no conflicts of interest.