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Comparative qsar analysis of histone deacetylase 6 (hdac6) inhibitors as anti-cancer agents

Author: 
Sandhya Vijayasarathy and Jhinuk Chatterjee
Subject Area: 
Life Sciences
Abstract: 

Histone deacetylases (HDACs) remain a promising class of anti-cancer drug targets with an ability to reverse abnormal epigenetic states associated with cancer. HDAC6, a subtype of HDAC, functions at the crossroads between atleast two cell signalling pathways involving ubiquitination and lysine acetylation. Over expression of this enzyme is associated with tumorigenesis and cell survival, other than promoting metastasis in cancer cells. In this study, a comparative quantitative structure activity relationship (QSAR) analyses has been performed on HDAC6 inhibitors for predicting their inhibitory activity using two-dimensional and three-dimensional QSAR models. 2D QSAR models were built using Multiple Linear Regression (MLR), Principal Component Regression (PCR) besides Partial Least Squares regression (PLS) methods, in addition to a 3D QSAR model which was developed using k-Nearest Neighbor Molecular Field Analysis (kNN-MFA). Among all the developed models, multiple linear regression (MLR) model performed better with the correlation coefficient r2 = 0.7381 and cross-validated squared correlation coefficient q2 = 0.6449 with external predictive ability of pred_r2 = 0.5107. Thus, the information rendered by these QSAR models may lead to a better understanding of structural requirements of this class of compounds against cancer in addition to paving the way for design of new and potent histone deacetylase inhibitors.

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