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Research Journal of Pharmacy and Technology
Year : 2017, Volume : 10, Issue : 3
First page : ( 778) Last page : ( 784)
Print ISSN : 0974-3618. Online ISSN : 0974-360X.
Article DOI : 10.5958/0974-360X.2017.00146.9

Computational Approach for the Design of Novel Tankyrase Inhibitors: A Rational Study based on Pharmacophore and Atom based 3D QSAR

Pai Vasudev1, Muddukrishna B.S.2, Pai Aravinda3,*

1Department of Pharmacognosy, Manipal College of Pharmaceutical Sciences (MCOPS), Manipal University, Manipal, Karnataka, India

2Department of Pharmaceutical Quality Assurance, Manipal College of Pharmaceutical Sciences (MCOPS), Manipal University, Manipal, Karnataka, India

3Department of Pharmaceutical Chemistry, Manipal College of Pharmaceutical Sciences (MCOPS), Manipal University, Manipal, Karnataka, India

*Corresponding Author E-mail: aravind.pai@manipal.edu

Online published on 29 April, 2017.

Abstract

Pharmacophore based screening was undertaken for a set of 30 flavones exhibiting Tankyrase I inhibitory activity. The two isoenzymes Tankyrase I and Tankyrase II share 80% sequence homology. This gives rise to the fine tuning required in the designing of small molecules wchich can selectively inhibit one or the other. In a recent study it was reported that, small molecule inhibitors of Tankyrases will be benificial in certain types of cancers like colorectal cancers. In future it may be a slective target for cancers like colorectal cancer. Four feature pharmacophores with one H bond acceptor and three aromatic rings were developped. The hypothesis ARRR2 was considered as best hypothesis, which gave a statistically significant three dimensional QSAR model with corresponding statistical parameters (0.9028 as r2 value and 0.8548 as q2 value). The generated model was applied sucessfully on a set of both training and test set molecules. The squared corelation coefficient of 0.85 was observed between actual and predicted values in test set. The squared corelation coefficient of 0.96 was observed between actual and predicted value in training set. The built model will be useful in predicting the structural requirements needed for the selective inhibition of Tankyrase I

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Keywords

Tankyrase, Pharmacophore, QSAR, flavones.

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