Molecular Modeling Studies of Curcumin Analogs as Anti-Angiogenic Agents

Abstract

Angiogenesis plays a pivotal role in the metastasis of cancer: curcumin showed excellent anti-angiogenesis activity on metastatic tumors. Several curcumin analogues have been synthesized and studied, and their biological activity was reported in the literature. One class of potent analogues are aromatic enones. In Dr Bowen's laboratory sixty three compounds were synthesized and in the laboratory of Dr Jack Arbizer (Emory University, Atlanta, GA) they were tested for their anti-angiogenic activity with an SVR endothelial cell growth assay developed by Dr Arbizer. The precise mechanism or the specific biological target on which these analogs exert their inhibition potential as anti-angiogenic agents is unknown. Therefore, structure-based molecular modeling is not a possibility. However, ligand based molecular modeling methods are available for studying and predicting which compounds among the sixty three can be further optimized for selectivity and desired property. Computational studies were carried out to identify which structural features within the series of analogues are significantly important for activity. Initially, pharmacophore modeling was carried out in Molecular Operating Environment (MOE) software to identify the Interaction Pharmacophore Elements (IPE) and their relative geometry in three-dimensional space. Two different three dimensional quantitative structural Activity Relationship (3D-QSAR) studies, Comparative Molecular Field Analysis (CoMFA), and Comparative Molecular Similarity Indices Analysis (CoMSIA) were carried out with this dataset. SYBYL (versions 7.2 and 7.3) were used for the development of the models. Forty six compounds were used as the calibration or the training set. The model yielded a cross validated q2 of 0.289 for CoMFA and 0.146 for CoMSIA analyses. Eleven compounds were used as the test set (or the prediction) set to externally validate the QSAR models and their robustness. The predictions of the model are acceptable with a few outliers