D QSAR Approaches

Basak, S. C., Balasubramanian, K., Gute, B. D., Mills, D., Gorczynska, A., Roszak, S. Prediction of cellular toxicity of halocarbons from computed chemodescriptors A hierarchical QSAR approach. J. Chem. Inf. Comput. Sci. 2003, 43, 1103-1109. 

Gute, B. D., Grunwald, G. D., Basak, S. C. Prediction of the dermal penetration of polycyclic aromatic hydrocarbons (PAHs) A hierarchical QSAR approach. SAR QSAR Environ. Res. 1999, 10, 1-15. 

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... 

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. 

Horvath D. (2001b) ComPharm—Automated comparative analysis of phar-macophoric patterns and derived QSAR approaches, novel tools in high throughput drug discovery. A proof-of-concept study applied to farnesyl protein transferase inhibitor design. In M Diudea (ed), QSPR/QSAR Studies by Molecular Descriptors, pp. 395-439, Nova Science Publishers, New York, USA. 

Amic, D., Lucic, B., Nikolic, S., and Trinajstic, N. (2001) Predicting inhibition of microsomal p-hydroxylation of aniline by aliphatic alcohols a QSAR approach based on the weighted path numbers. Croat. Chem. Acta 74, 237-250. 

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. 

Any QSAR method can be generally defined as an application of mathematical and statistical methods to the problem of finding empirical relationships (QSAR models) of the form ,- = k(D, D2,..., D ), where ,- are biological activities (or other properties of interest) of molecules, D, P>2,- ,Dn are calculated (or, sometimes, experimentally measured) structural properties (molecular descriptors) of compounds, and k is some empirically established mathematical transformation that should be applied to descriptors to calculate the property values for all molecules (Fig. 6.1). The goal of QSAR modeling is to establish a trend in the descriptor values, which parallels the trend in biological activity. In essence, all QSAR approaches imply, directly or indi-... 

Gute, B.D. and Basak, S.C., Predicting acute toxicity (LC50) of benzene derivatives using theoretical molecular descriptors a hierarchical QSAR approach, SAP QSAR Environ. Res., 1, 117-131, 1997. 

Veith, G.D. and Mekenyan, O.G., A QSAR approach for estimating the aquatic toxicity of soft electrophiles [QSAR for soft electrophiles], Quant. Struct.-Act. Relat., 12, 349-356, 1993. 

The seminal work of Corwin Hansch initiated the field of QSAR. Two-dimensional and three-dimensional QSAR methods (2-D QSAR and 3-D QSAR) have been widely applied to problems of biological interest. The latter approach has increased in popularity with the introduction of the comparative molecular field analysis (CoMFA) and commercial availability of similar methods. 

Basak, S.C., Gute, B.D. and Grunwald, G.D. (1997c). Use of Topostructural, Topochemical, and Geometric Parameters in the Prediction of Vapor Pressure A Hierarchical QSAR Approach. J.Chem.Inf.Comput.ScL, 37, 651-655. 

Gute, B.D., Grunwald, G.D. and Basak, S.C. (1999). Prediction of the Dermal Penetration of Polycyclic Aromatic Hydrocarbons (PAHs) A Hierarchical QSAR Approach. SAR QSAR Environ.Res., 10,1-15. 

It is conceivable that quantitative structure-activity (QSAR) approaches (e.g., TOPKAT see Chapter 7) could be applied to predict response levels for uncharacterized contaminants for use in the HI approach. Further, specific submodels existing (e.g., that for developmental toxicity) could be applied to estimate system-specific response levels for application in the IT D approach. To our knowledge, there are no computer-assisted programs available that can automate the prediction of toxicity for mixtures. Much of the reason may reside in the relative lack of empirical observations and characterizations of chemical interactions. Many QSAR approaches rely on training set approaches to the development of automated programs. Another impediment may be the many examples of the levels, types and biochemical bases for chemical interactions, the intricacies of which would benefit from an automated approach. This area is a useful area for exploration. 

The CoMFA approach, as implemented in SYBYL 6.1 (Tripos Ass., St. Louis, MO, USA) was thus chosen as the most promising tool with which to carry out a 3-D QSAR study on the set of I receptor ligands reported in Tables 1-4 and Chart 2. 

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