Factor Xa Inhibitors with Heterocyclic Moieties

The next step was made by Klebe et al. [50]. Two 3D-QSAR methods were applied to get three-dimensional quantitative structure-activity relationships using a training set of 72 inhibitors of the benzamidine type with respect to their binding affinities toward Factor Xa to yield statistically reliable models of good predictive power [51-54] the widely used CoMFA method (for steric and electrostatic properties) and the comparative molecular similarity index analysis (CoMSlA) method (for steric, electrostatic, hydrophobic, hydrogen bond donor, and hydrogen bond acceptor properties). These methods allowed the consideration of various physicochemical properties, and the resulting contribution maps could be intuitively interpreted. 

In both the cases, the molecular property fields were evaluated between a probe atom and each molecule of the data set at the intersections of a regularly spaced grid. Since the affinity data were inter-correlated between 30 and 70%, the derivation of three significant models was not obvious or trivial. In all the cases, the CoMSlA analyses revealed significantly better correlations expressed in terms of higher q values. Three different grid spacings were evaluated in both CoMFA as well as CoMSlA. 

The numbers of partial least squares (PLS) components were higher in CoMSIA than in CoMFA. This difference probably resulted from the significantly higher number of lattice points showing steadily varying field values (e.g., inside the molecules). The optimal numbers of components were selected on the basis of lowest Spress. 

A significant work was recently presented [1] for a series of achiral FXa inhibitors, which were designed by a combination of benzamidines directed toward the EXa SI pocket. Their basic or hydrophobic substituents were directed toward the flexible S4 subsite of Factor Xa. The 3-amidinobenzyl-lH-indole-2-carboxamide scaffold as an interesting motif was also identified. A combination of X-ray structure analysis of Factor Xa and trypsin, flexible moleciflar docking, and 3D-QSAR analyses was performed to understand relevant protein-hgand 

Moreover, a final 3D-QSAR model vahdation was done using a prospective study with an external test set. The 82 compounds from the data set were used in a lead optimization project. A CoMFA model gave an (cross validated) value of 0.698 for four relevant PLS components and a conventional of 0.938 were obtained for those 82 compounds. The steric descriptors contributed 54% to the total variance, whereas the electrostatic field explained 46%. The CoMSIA model led to an (cross vahdated) value of 0.660 for five PLS components and a conventional of 0.933. The contributions for steric, electrostatic, and hydrophobic fields were 25, 44, and 31%. As a result, it was proved that the basic S4-directed substituents should be replaced against more hydrophobic building blocks to improve pharmacokinetic properties. The structural and chemical interpretation of CoMFA and CoMSIA contour maps directly pointed to those regions in the Factor Xa binding site, where steric, electronic, or hydrophobic effects play a dominant role in ligand-receptor interactions. 

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