Pharmacophore Search Successes

Extract pharmacophore from known ligand. Use this in database screens. 

---

Recent examples of successful peptide-ligand based discoveries of drug-like peptidomimetics include the discovery of SST antagonists, or the discovery of non-peptidic antagonists of the recently deorphanized urotensin II receptor at Sanofi-Aventis. ° As illustrated in Fig. 3, Flohr etal. used 3D models of the NMR solution structure of cyclic peptide derivatives of Urotensin II as a template for virtual 3D pharmacophore searches which resulted into non-peptidic candidates for lead optimization. 

To illustrate how pharmacophore searches can be applied successfully to the discovery of novel ligands, we chose two examples from our own recent work. 

Some hits also revealed sufficient selectivity of type 1 inhibition versus the type 2 isoform, which is advantageous for the side-effect profile of these compounds. Comparison of the model for llp-HSDl inhibitors with the X-ray crystal structure (which was published shortly after model generation and VS) showed good correlation of the chemical features responsible for ligand binding. In another study, a combination of common feature-based qualitative and quantitative models was used as 3D pharmacophore search query to successfully detect novel endothelin-A antagonistic lead structures. 

In this chapter, we will start by providing an overview of the evolution of the 3D pharmacophore concept and subsequently show the usefulness of 3D pharmacophore searching in modern lead discovery. The use of this approach for combinatorial library design, compound classification, and molecular diversity analysis is presented. Examples of successful applications reported in the last couple of years are reviewed. 

For all compounds in the CIDB, a number of pre-calculated properties or predicted endpoints are stored. These pre-calculated properties allow, for example, for an efficient assembly of property-filtered subsets of the different compound collections. Additionally, important parameters have to be calculated only once for each compound and can then be used multiple times. This saves computational resources and ensures that all users rely on standardized structures and descriptor values that have been calculated in a consistent way. The stractures from the CIDB are therefore also used for updates of computational chemistry tools that maintain compound sets internally (e.g., pharmacophore search software). The calculation of the properties is facilitated by a number of workflows that are automatically triggered whenever structures are added or updated in a compound collection. For some structures, not all properties can be calculated successfully—this case is captured by an error tracking mechanism. Furthermore, a version backing procedure notes which version of a property calculator was used to generate certain property value and permits recalculations of properties if necessary. 

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. 

At the beginning of this chapter we will look into the different automated alignment methods as correct alignment is the first and most important prerequisite for a successful pharmacophore identification process. Further, we will elaborate how essential issues of pharmacophore modeling such as conformational search, pharmacophore feature definitions, compounds structure storage and screening are handled by various available software packages. 

---