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

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

Volume: 22 Issue: 33

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

Affiliation:

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

Abstract: 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.

Cite this article as:

Praseetha Sugathan, Bandaru Srinivas, Yadav Mukesh, Nayarisseri Anuraj and Sureshkumar Sivanpillai, Common SAR Derived from Multiple QSAR Models on Vorinostat Derivatives Targeting HDACs in Tumor Treatment, Current Pharmaceutical Design 2016; 22 (33) . https://dx.doi.org/10.2174/1381612822666160621094009