Knowledge of the Target
Knowledge of the target is the most important starting point for designing a focused screening library, and it’s important to assess how much information is available about the target before deciding how to proceed. With an uncharacterized target, random screening is the first approach. But the more users know about the target, the better chance they have of finding new compounds to hit the target, and the more focused the library can be.
For example, when designing a library for H3 antagonists the target is well characterized. There are a number of ligands for it and there are also drugs on the market that hit the receptors. With this knowledge it is possible to build a field template that will lead to a focused screening library with a high chance of success.
Field templates, or pharmacophores, are used in library design to predict the activity of compounds at therapeutic targets. They can be compared to the biological fingerprint for a protein binding site.
The first step in building a field template is to analyze active ligands that interact with the target to find a common shape for binding. Where the 3D shape of the protein active site is not known, Cresset’s forgeV10 computational suite is used to compare the conformations of the ligands to find their optimum alignment in the binding site of the protein. This alignment, or an alignment generated from protein-ligand crystal data, together with structure activity data is used to find the field points that are likely to correspond to important features in the active site.
To illustrate this point, forgeV10 was used to build a library of potential H3 antagonists. A series of seven highly active H3 antagonists were identified from the literature and aligned in their bioactive conformations to generate a consensus field template (Figure 2).
As confirmation of the predictive capability of this template, the field match score was compared against the known activity (Ki) scores of 68 further H3 antagonists described in the scientific literature and outside the original training set. A good match of fields to activity was confirmed.
The H3 template was then used to screen Cresset’s compound collection to identify potential H3 antagonists. A large number of matches were identified, with 68 distinct chemical scaffolds.
This example demonstrates how forgeV10 can be used to search new areas of chemical space for new candidates. The field analyses take users beyond the limitations of chemical structure, to find compounds with similar activity but varying chemotypes, leading to new starting points for research.
Field templates can also be built for toxicity targets as well as for therapeutic targets. A range of such templates can be derived and used as filters to counterscreen a library of compounds.
In the H3 example, the compounds were screened against field templates for CYP 2D6 and hERG. Approximately 4% of the compounds were rejected due to potential 2D6 toxicity and a further 8% due to potential hERG toxicity.