Proteases, comparative QSAR

Initially Kurup et al. [15] analyzed some series of aminoindazoles [79,80]. Later, a comprehensive comparative QSAR study on P2/P2 and Pi/Pj substituted symmetrical and non-symmetrical 3-aminoindazole cyclic urea HIV protease inhibitors (21) was performed [81]. The SAR data were taken from different papers [79,80,82-85]. Several QSAR models were developed for individual datasets. QSAR 16-18 were derived for the combined set [81]. 

Except for a very few studies most of the work so far has been done using SAR data of wild-type protease. Further QSAR and molecular modeling studies on prodrug derivatives, wild-type vs. mutant SAR data and lateral validation of these models via comparative analysis can provide useful insight. Such studies can highlight the differences and similarity, if any, in their mechanism of interaction with wild-type and mutant protease receptor. 

Summary of the 3D-QSAR Analysis Results for the 49 HIV-1 Protease Inhibitors Using the 3D-LogP Descriptor and Statistical Comparison with the Comparative Binding Energy Analysis (50) (COMBINE)... 

Nair A, Jayatilleke P, Wang X, Miertus S, Welsh WJ. Computational studies on tetrahydropyrimidine-2-one (THP) HIV-1 protease inhibitors Improving 3D-QSAR comparative molecular field analysis (CoMFA) models by inclusion of calculated inhibitor- and receptor-based properties. J Med Chem 2000 45 973-83. 

The seven-member cyclic urea scaffold was found to be a potent inhibitor of HIVPR and has been studied extensively [70-74]. This scaffold creates an effective hydrogen bond network with the enzyme while incorporating the structural water molecule (Fig. 4) [71]. Initial SAR studies optimized the benzyl group as an ideal Pi/P -substituent. Variation of nitrogen substituents at P2/P2 lead to clinical trials of several cyclic urea s (12-15, Scheme 4), which could not be pursued further due to poor bio availability, metabolic instability, moderate potency and inadequate resistance profile as compared to other protease inhibitors. Further studies led to the identification of several other classes of cyclic urea derivatives. Subsequently QSAR and molecular modeling studies were also reported to aid in the design of an effective inhibitor belonging to this class. A discussion on these studies is presented in this section. 

D-QSAR studies demonstrated that there could be more than one way to fit structure-activity data within a QSAR methodology. A receptor-independent 4D-QSAR study identified the hydrophobic nature of a HIV protease receptor site and helped in structural modification to improve the potency of the AHPBA inhibitors [241], A 4D-fingerprint-based QSAR model developed for AHPBA inhibitors of HIV was generated independent of any receptor structure or alignment information [126]. These models exhibited comparable statistical data with CoMFA, CoMSIA and H-QSAR approaches. This study proved that genuine representation of 3D and conformational properties of compounds is possible using this approach. 

Hansch recommended a lateral validation of QSAR results, i.e., the comparison of models of closely related series of compounds in one biological test system (cf. equations 16 and 17) or trie comparison of the QSAR models derived for one series of compounds in several related biological test models (e.g., serine and cysteine proteases). If all models are of comparable quality, and if they show similar regression coefficients of the physicochemical terms, the results can be accepted. However, in most cases the required effort will be too large to do this routinely. In addition, even closely related enzymes or receptors may have significantly different binding sites. 

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