Journal of Analytical & Bioanalytical Techniques
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A drug discovery cycle, to identify, to optimize and eventually take a compound
to the market is generally along process (approx 12�15 years) and is very
expensive (approx $500million R&D expense). The enormous pressure that
pharmaceutical and biotech companies are facing, has created the need to apply
all available techniques to decrease attrition rates, costs and the time to market.
Pharmacophore identification is one such technique. As case study example of
drug design against Cycloxygenase (COX) enzyme is being discussed here using
Catalyst Software. COX enzyme catalyze the biosynthesis of prostaglandins and
thromboxane from arachidonic acid (AA). In the present paper we summarize, the
development of hypotheses of a dataset comprising six chemically diverse series
of known inhibitors for COX-2, by using the Catalyst/ Hypogen module. The most
predictive pharmacophore model, consisting of four features, namely, one hydrogen
bond donors, one hydrogen bond acceptor, one hydrophobic aliphatic and one ring
aromatic feature, had a correlation (r) of 0.954 and a root mean square deviation
of 0.894. The model was validated on a test set consisting of six different series
of structurally diverse 27 compounds and performed well in correctly classifying
active and inactive molecules correctly. The resultant best hypothesis was used
to screen databases viz. NCI and maybridge to produce hit compounds. 264 hits
were obtained which were arranged according to their fit value in 8 categories
and subjected to secondary screening using Lipnski�s rule of five. The resultant
compounds were then docked into the COX-2 binding site to study the ligand �
protein interaction and binding energies were evaluated in terms of LUDI scores.
Several new structural scaffolds have been obtained as a result of the virtual
screening. The Compounds were screened in in-vitro assay and novel scaffolds
were identified as lead compounds for development of COX-2 inhibitors.
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