Common SAR Derived from Multiple QSAR Models on Vorinostat Derivatives Targeting HDACs in Tumor Treatment

Author(s): Sugathan Praseetha, Srinivas Bandaru, Mukesh Yadav, Anuraj Nayarisseri, Sivanpillai Sureshkumar

Journal Name: Current Pharmaceutical Design

Volume 22 , Issue 33 , 2016

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Background: Dysregulation of HDACs has been associated with tumour development and therefore inhibiting HDAC’s have surfaced as promising therapeutic strategy in malignancy.

Methods: Vorinostat analogues with different biological activities were investigated for underlying structure-activity relationship.

Results: Out of six activities and their multiple QSAR models, HDAC1 and HDAC8 produced statistically fit, stable and predictive linear (MLR) and non-linear (SVM) QSAR models. In case of HDAC1 activity as end point, linear (R2=0.8089, R2 CV=0.7343) and non-linear (R2=0.9801, R2 CV=0.8952) QSAR models turned reliable to investigate SAR. Similarly, HDAC8 activity based linear (R2=0.9454, R2 CV=0.9049) and non-linear (R2=0.9899, R2 CV=0.9232) QSAR models produced statistically improved and stable models.

Conclusion: Molecular descriptors derived from 3-D Morse and Radial Distribution Function indices were found to be selective in all the models. These molecular descriptors which encode common SAR among Vorinostat derivatives were evaluated for their potent HDAC inhibition activity.

Keywords: QSAR, Multiple Linear Regression, Support Vector Machine, Common SAR, Vorinostat Analogues, HDAC Activity.

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Article Details

Year: 2016
Page: [5072 - 5078]
Pages: 7
DOI: 10.2174/1381612822666160621094009
Price: $65

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