Identification of Hydroxamic Acid Based Selective HDAC1 Inhibitors: Computer Aided Drug Design Studies.

Dr. Mehnaz Kamal, PhD
Dr. Mehnaz Kamal, PhD
College of Pharmacy, Prince Sattam Bin Abdulaziz University
Assistant Professor
Medicinal chemistry
Al Kharj, Riyadh | Saudi Arabia

Curr Comput Aided Drug Des 2019 ;15(2):145-166

Medicinal Chemistry Research Laboratory, Institute of Pharmaceutical Sciences, Guru Ghasidas University, Bilaspur- 495 009, (C.G.), India.

Background: Overexpression of Histone deacetylase 1 (HDAC1) is responsible for carcinogenesis by promoting epigenetic silence of tumour suppressor genes. Thus, HDAC1 inhibitors have emerged as the potential therapeutic leads against multiple human cancers, as they can block the activity of particular HDACs, renovate the expression of several tumour suppressor genes and bring about cell differentiation, cell cycle arrest and apoptosis.

Methods: The present research work comprises atom-based 3D-QSAR, docking, molecular dynamic simulations and DFT (density functional theory) studies on a diverse series of hydroxamic acid derivatives as selective HDAC1 inhibitors. Two pharmacophoric models were generated and validated by calculating the enrichment factors with the help of the decoy set. The Four different 3D-QSAR models i.e., PLS (partial least square) model, MLR (multiple linear regression) model, Field-based model and GFA (Genetic function approximation) model were developed using 'PHASE' v3.4 (Schrödinger) and Discovery Studio (DS) 4.1 software and validated using different statistical parameters like internal and external validation.

Results And Discussion: The results showed that the best PLS model has R2=0.991 and Q2=0.787, the best MLR model has R2= 0.993 and Q2= 0.893, the best Field-based model has R2= 0.974 and Q2= 0.782 and the best GFA model has R2= 0.868 and Q2= 0.782. Cross-validated coefficients, (rcv 2) of 0.967, 0.926, 0.966 and 0.829 was found for PLS model, MLR, Field based and GFA model, respectively, indicated the satisfactory correlativity and prediction. The docking studies were accomplished to find out the conformations of the molecules and their essential binding interactions with the target protein. The trustworthiness of the docking results was further confirmed by molecular dynamics (MD) simulations studies. Density Functional Theory (DFT) study was performed which promptly optimizes the geometry, stability and reactivity of the molecule during receptor-ligand interaction.

Conclusion: Thus, the present research work provides spatial fingerprints which would be beneficial for the development of potent HDAC1 inhibitors.

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July 2019
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