D QSAR

In an independent study, Yoshida and Niwa [20] analyzed a larger and more diverse set of molecules (104 compounds) and developed a 2D QSAR model, which gave results similar to that of Cronin [19] but added some more details with regard to the physicochemical properties involved in the hERG blockade by drugs. Equation 5.2 represents the best model  

The relevance of size-related properties of hERG-blocking molecules was also detected in a 2D QSAR model developed by Coi et al. [22] after the analysis of 82 compounds through the CODESSA method. These authors developed two multiparameter models with strong predictive properties, from which, besides the involvement of hydrophobic features, the importance of linearity as opposed to globularity of the hERG blockers emerged. 

In Table 5.1, we present a list of the main physicochemical and structural properties associated with the descriptors included in the 2D QSAR models discussed above. Of course, we did some generalizations in an attempt to refer different parameters and descriptors to the same property, but the effort was devoted at identifying the smallest number of significant features positively or negatively correlated to the hERG blockade by small molecules. Examining the properties 

Property of hERG Blockers Sign of Correlation with hERG Blockade References 

Different from 2D QSAR, classification models reported in the papers cited above do not always allow the identification of descriptors related to the hERG activity however, in some cases, descriptors or molecular features crucial for the assignment to either of the classes were explicitly indicated. This allowed us to tentatively collect them in Table 5.2, which when compared with Table 5.1 provides (as expected) a very similar picture of the molecular properties involved in the blockade of hERG by drugs. Even though the properties listed in Table 5.2 are not associated with a positive or a negative sign (they can only be indicated as relevant for the classification), they 

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A widely used 3-D QSAR method that makes use of PLS is comparative molecular field analysis (CoMFA), in which a probe atom is used to calculate the steric and electronic fields at numerous points in a 3D lattice within which the molecules have been aligned. Poso et al. [56] used the technique to model the binding of coumarins to cytochrome P450 2A5, with similar results to those obtained by Bravi and Wikel [55]. Shi et al. [57] used it to model the estrogen receptor binding of a large diverse set of compounds, and Cavalli et al. [58] used it to develop a pharmacophore for hERG potassium... 

Compound 330 (Figure 31) is a potent and very selective cyclooxygenase-2 (COX-2) inhibitor <1997BMCL57>. Three-dimensional quantity-structure activity relationship (3-D QSAR) analysis of this compound and other members of a series of 5,6-diarylthiazolo[3,2-A][l,2,4]triazoles has been carried out <2004MI5>. 

Structure-based design is focused on understanding protein-ligand interactions but does not always lead to predictive models for ligand series. In contrast, 3-D-QSAR with... 

This approach was first applied toward an understanding of discriminating interactions in the serine proteases factor Xa, thrombin and trypsin [108] and provided selectivity information for all important serine protease subpockets, which are in agreement to experimental selectivities of typical protease inhibitors. This approach was complemented by a 3-D-QSAR selectivity analysis on a series of 3-amidinobenzyl-lH-indole-2-carboxamides [107], which points, from the viewpoint of the ligands, to similar main interactions driving selectivity between key enzymes in the blood... 

In the search for bUe-add resorption inhibitors (BARI), a predictive 3-D-QSAR pharmacophore model for the deal Na+/bile acid cotransporter was derived, which enhanced the understanding of binding and transport properties [205]. This model was then also successfully explored to search for potential substitution sites, which are not relevant for the SAR of this series, while they allow the addition of additional substituents to minimize the oral uptake of inhibitors. 

Ragno, R., Simeoni, S., Valente, S., Massa, S. and Mai, A. (2006) 3-D QSAR studies on histone deacetylase inhibitors. A GOLPE/GRID approach on different series of compounds. Journal of Chemical Information and Modeling 46,1420-1430. 

Caroli, A., Botta, G., Brosch, G., Massa, S. and Mai, A. (2008) Class 11-selective histone deacetylase inhibitors. Part 2 alignment-independent GRIND 3-D QSAR, homology and docking studies. European Journal of Medicinal Chemistry, 43, 621-632. 

M. Charton, in Classical and 3-D QSAR in Agrochemistry and Toxicology (Eds. C. Hansch and T. Eujita) American Chemical Society, Washington D.C., 1995, p. 75. 

Sivakumar, P.M., Babu, S.K.G., Mukesh, D. QSAR studies on chalcones and flavonoids as antituberculosis agents using genetic function approximation (GFA) method. Chem. Pharm. Bull. 2007, 55, 44-9. 

Gomplex field-based 3-D QSAR models have also been applied to the problem of predicting hERG activity. Gavalli ef al. [85] used a CoMFA model, as previously discussed. Pearlstein ef al. [89] modeled a set of sertindole analogs using compara-... 

The Avery group has produced a large number of artemisinin analogues by semisyntheses and elegant total synthesis . This has enabled Avery to develop predictive 3-D QSAR (CoMFA) analyses for the artemisinin class of antimalarial. This information coupled with the ADME approach described above should permit highly potent and orally bioavailable semi-synthetic analogues to be designed by a truly rational approach. 

R. S. Pearlman, in H. Kubinyi, ed., 3-D QSAR in Drug Design Theory, Methods and Applications, ESCOM Science Publishers, Leiden, the Netherlands,... 

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