Computational design of Checkpoint Kinase-1 (CHK-1) inhibitors for cancer therapy

CHK-1 kinase belongs to the serine/threonine family of kinases, which play a vital role in cell cycle arrest and proved to be a promising therapeutic target to control cancer development and progression. Many potent and selective CHK-1 inhibitors have been reported, but only a few are currently in clinical trial. In this era, drug re-profiling has proved to be a major step in drug discovery and development that is cost and time beneficial. In this study, we have incorporated a combined in silico computational approach to widen the chemical range of CHK-1 inhibitors from the existing FDA approved drugs. An e-pharmacophore model was created from 3D crystal coordinates of CHK-1 protein complex with the clinical trial inhibitor (CCT245737). The hypothesis with seven molecular features was screened with FDA drugs and the obtained drugs were subjected into Glide XP molecular docking. The top 10% scored ligands were visualized and Procaterol was best identified which showed similar interaction patterns with enzyme active sites as the clinical trial inhibitor. Furthermore, total binding free energy, pharmacokinetic properties and molecular dynamics were also evaluated. The results consolidated showed better binding affinity, acceptable kinetic profile and significant stability of Procaterol binding with CHK-1 kinase. In conclusion, we highlight that Procaterol is a re-provable potent CHK-1 inhibitor and appears as a new structural scaffold for further optimisation.