Activity and Selectivity Maps

The miCTokinetic models in this section are built upon scaling relations of the type described in Chapter 6. It will be shown that an underlying scaling relation in general leads to the existence of what we call an activity map, which is a map of the catalytic activity as a function of a few descriptors, hi many cases, such a map shows a single maximum for a certain set of descriptor values, and it is often also called a volcano relation. In a similar way, we will introduce selectivity maps showing selectivity as a function of the descriptors. 

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Figure 5.7 shows the major contributions of the local descriptors to activity and selectivity. As can be seen, the activity and selectivity maps are quite similar but also differ. Such fine distinctions are difficult to take into account to design new promising structures intuitively (especially if selectivity with respect to other receptor subtypes is also required). Thus, the automated virtual screening approach is preferred. 

Kunkel, E. J., Plavec, L, Nguyen, D., Melrose, J., Rosier, E. S., Kao, L. T., Wang, Y., Hytopoulos, E., Bishop, A. C., Bateman, R., et al. (2004). Rapid structure-activity and selectivity analysis of kinase inhibitors by Bio Map analysis in complex human primary cell-based models. ASSAY Drug Dev. Technol. 2 431-441. 

The results of the study also indicate how contribution estimates can be used to map each individual amino acid (and each amino acid physicochemical property) in the receptors with respect to its contribution to the activity and selectivity for every amine ligand [14a],... 

Most often the results of a CoMFA study are presented in graphical form, with contours for favorable and unfavorable regions of the different fields e.g. Figure 60). Difference maps were proposed as tools to analyze and identify areas of interest with respect to activity and selectivity, if two different types of biological activities are compared [1011]. 

One may think of an iterative model for the preclinical discovery screening cycle. A large number of compounds are to be mined for compounds that are active for example, that bind to a particular target. The compounds may come from different sources such as vendor catalogues, corporate collections, or combinatorial chemistry projects. In fact, the compounds need only to exist in a virtual sense, because in silico predictions in the form of a model can be made in a virtual screen (Section 8) which can then be used to decide which compounds should be physically made and tested. A mapping from the structure space of compounds to the descriptor space or property space provides covariates or explanatory variables that can be used to build predictive models. These models can help in the selection process, where a subset of available molecules is chosen for the biological screen. The experimental results of the biological screen (actives and inactives, or numeric potency values) are then used to learn more about the structure-activity relationship (SAR) which leads to new models and a new selection of compounds as the cycle renews. 

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