Virtual Screening to Identify Calcium Channel Blockers
The first step in the search for a new TCCBs is to collect all pertinent chemical and biological information related to TCCBs, in particular the Cav3.2 isoform (Figure 3). Searching by target name, initially under ion channels and then by narrowing the search down to voltage-dependent calcium channels, followed by selection of the specific isoform enables easy retrieval of all the relevant chemical and biological data from Reaxys Medicinal Chemistry.
To facilitate comparisons of bioactivity data from different publications and assay types, all the data points in Reaxys Medicinal Chemistry have pX values. pX values are calculated by transforming parameters such as EC50, IC50 and Ki into the –Log equivalent (pEC50, pIC50, pKi). These are normalized values assigned to the data that enable easily quantification of compound–target affinity and compare information from all around the world.
The target search for the Cav3.2 isoform of TCCBs in Reaxys Medicinal Chemistry retrieved the chemical structure of 1,854 ligands active against that specific target. These 1,854 ligands have 2,285 bioactivities associated with them and were extracted from 77 citations (Figure 4A).
An affinity profile for the most potent ligands active against the Cav3.2 isoform of TCCBs, with pX values greater than 6.0 (affinity < 1 μM), can be generated and viewed as a Heatmap (Figure 4B). The Heatmap visualizes the relationships between ligands and their targets in terms of key parameters, allowing rapid identification of relevant ligand–target interactions. The map displays all ligands with a pX above 6.0 and the associated target TCCB protein for which in vitro biological data has been mined from the literature. In the Heatmap, biological affinities or activities are quantified as a pX value and displayed from 1 (low activity) in blue to 15 (high activity) in red.
At the time of this analysis, this query resulted in 471 ligands with a pX value of above 6.0 (affinity < 1 μM) against the TCCB target. Some of these molecules are depicted in Figure 5. These molecules were then clustered using variable-length Jarvis-Patrick clustering. The query set was narrowed down and the central molecules of resulting clusters and singletons was used for building the 2D pharmacophore in the virtual screening analysis.
A multitude of key parameters can be explored to aid understanding of the ligand−target interactions, such as drug-like grading, in vitro efficacy, in vivo animal models, metabolism, pharmacokinetic and pharmacodynamic data, and clinical use/application, as well as extensive information on the chemical structure itself. For example, Reaxys Medicinal Chemistry can be used to get detailed metabolic data for each compound/ligand (Figure 6). For each data point, the parameter measured, the value, target, target species, tissue/organ, dose and reference are all shown and both quantitative and qualitative results available in the database. This aspect would be particularly beneficial considering that Mibefradil was withdrawn from the market due to the inhibition of cytochrome P450 enzymes 3A4 and 2D6, which had the potential to lead to serious drug−drug interactions.