Determination of the binding mode and interacting amino-acids for dibasic H3 receptor antagonists.

Due to its involvement in major CNS functions, the histamine H3 receptor (H3R) is the subject of intensive medicinal chemistry investigation, supported by the range of modern drug discovery tools, such as receptor modeling and ligand docking. Although the receptor models described to date share a majority of common traits, they display discrete alternatives in amino-acid conformation, rendering ligand binding modes quite different. Such variations impede structure-based drug design in the H3R field. In the present study, we used a combination of medicinal chemistry, receptor-guided and ligand-based methods to elucidate the binding mode of antagonists. The approaches converged towards a ligand orientation perpendicular to the membrane plane, bridging Glu206 of the transmembrane helix 5 to acidic amino acids of the extracellular loops. This consensus will help future structure-based drug design for H3R ligands.

Novel and highly potent histamine H3 receptor ligands. Part 3: an alcohol function to improve the pharmacokinetic profile.

Synthesis and biological evaluation of potent histamine H3 receptor antagonists incorporating a hydroxyl function are described. Compounds in this series exhibited nanomolar binding affinities for human receptor, illustrating a new possible component for the H3 pharmacophore. As demonstrated with compound BP1.4160 (cyclohexanol 19), the introduction of an alcohol function counter-intuitively allowed to reach high in vivo efficiency and favorable pharmacokinetic profile with reduced half-life.

Novel and highly potent histamine H3 receptor ligands. Part 1: withdrawing of hERG activity.

Pre-clinical investigation of some aryl-piperidinyl ether histamine H3 receptor antagonists revealed a strong hERG binding. To overcome this issue, we have developed a QSAR model specially dedicated to H3 receptor ligands. This model was designed to be directly applicable in medicinal chemistry with no need of molecular modeling. The resulting recursive partitioning trees are robust (80-85% accuracy), but also simple and comprehensible. A novel promising lead emerged from our work and the structure-activity relationships are presented.

Novel and highly potent histamine H3 receptor ligands. Part 2: exploring the cyclohexylamine-based series.

Synthesis and biological evaluation of novel and potent cyclohexylamine-based histamine H3 receptor inverse agonists are described. Compounds in this newly identified series exhibited subnanomolar binding affinities for human receptor and no significant interaction with hERG channel. One derivative (10t) demonstrated enhanced in vivo efficiency and preferential brain distribution, both properties suitable for potential clinical evaluation.

Refined docking as a valuable tool for lead optimization: application to histamine H3 receptor antagonists.

Drug-discovery projects frequently employ structure-based information through protein modeling and ligand docking, and there is a plethora of reports relating successful use of them in virtual screening. Hit/lead optimization, which represents the next step and the longest for the medicinal chemist, is very rarely considered. This is not surprising because lead optimization is a much more complex task. Here, a homology model of the histamine H(3) receptor was built and tested for its ability to discriminate ligands above a defined threshold of affinity. In addition, drug safety is also evaluated during lead optimization, and "antitargets" are studied. So, we have used the same benchmarking procedure with the HERG channel and CYP2D6 enzyme, for which a minimal affinity is strongly desired. For targets and antitargets, we report here an accuracy as high as at least 70%, for ligands being classified above or below the chosen threshold. Such a good result is beyond what could have been predicted, especially, since our test conditions were particularly stringent. First, we measured the accuracy by means of AUC of ROC plots, i. e. considering both false positive and false negatives. Second, we used as datasets extensive chemical libraries (nearly a thousand ligands for H(3)). All molecules considered were true H(3) receptor ligands with moderate to high affinity (from microM to nM range). Third, the database is issued from concrete SAR (Bioprojet H(3) BF2.649 library) and is not simply constituted by few active ligands buried in a chemical catalogue.