Exploring cyclin-dependent kinase inhibitors: a comprehensive study in search of CDK-6 inhibitors using a pharmacophore modelling and dynamics approach.
Bharath Kumar ChagaletiVenkatesan SaravananChitra VellapandianMuthu Kumaradoss KathiravanPublished in: RSC advances (2023)
Cancer prevalence and resistance issues in cancer treatment are a significant public health concern globally. Among the existing strategies in cancer therapy, targeting cyclin-dependent kinases (CDKs), especially CDK-6 is found to be one of the most promising targets, as this enzyme plays a pivotal role in cell cycle stages and cell proliferation. Cell proliferation is the characteristic feature of cancer giving rise to solid tumours. Our research focuses on creating novel compounds, specifically, pyrazolopyrimidine fused azetidinones, using a groundbreaking molecular hybridization approach to target CDK-6. Through computational investigations, ligand-based pharmacophore modelling, pharmacokinetic studies (ADMET), molecular docking, and dynamics simulations, we identified 18 promising compounds. The pharmacophore model featured one aromatic hydrophobic centre (F1: Aro/Hyd) and two H-bond acceptors (F2 and F3: Acc). Molecular docking results showed favourable binding energies (-6.5 to -8.0 kcal mol -1 ) and effective hydrogen bonds and hydrophobic interactions. The designed compounds demonstrated good ADMET profiles. Specifically, B6 and B18 showed low energy conformation (-7.8 kcal and -7.6 kcal), providing insights into target inhibition compared to the standard drug Palbociclib. Extensive molecular dynamics simulations confirmed the stability of these derivatives. Throughout the 100 ns simulation, the ligand-protein complexes maintained structural stability, with acceptable RMSD values. These compounds hold promise as potential leads in cancer therapy.
Keyphrases
- molecular docking
- cell cycle
- molecular dynamics simulations
- cancer therapy
- cell proliferation
- public health
- papillary thyroid
- drug delivery
- squamous cell
- ionic liquid
- machine learning
- risk factors
- pi k akt
- childhood cancer
- deep learning
- emergency department
- zika virus
- single molecule
- lymph node metastasis
- binding protein
- density functional theory
- squamous cell carcinoma
- metastatic breast cancer
- dengue virus
- aedes aegypti
- crystal structure