Examining the critical roles of human CB2 receptor residues Valine 3.32 (113) and Leucine 5.41 (192) in ligand recognition and downstream signaling activities.

We performed molecular modeling and docking to predict a putative binding pocket and associated ligand-receptor interactions for human cannabinoid receptor 2 (CB2). Our data showed that two hydrophobic residues came in close contact with three structurally distinct CB2 ligands: CP-55,940, SR144528 and XIE95-26. Site-directed mutagenesis experiments and subsequent functional assays implicated the roles of Valine residue at position 3.32 (V113) and Leucine residue at position 5.41 (L192) in the ligand binding function and downstream signaling activities of the CB2 receptor. Four different point mutations were introduced to the wild type CB2 receptor: V113E, V113L, L192S and L192A. Our results showed that mutation of Val113 with a Glutamic acid and Leu192 with a Serine led to the complete loss of CB2 ligand binding as well as downstream signaling activities. Substitution of these residues with those that have similar hydrophobic side chains such as Leucine (V113L) and Alanine (L192A), however, allowed CB2 to retain both its ligand binding and signaling functions. Our modeling results validated by competition binding and site-directed mutagenesis experiments suggest that residues V113 and L192 play important roles in ligand binding and downstream signaling transduction of the CB2 receptor.

Novel triaryl sulfonamide derivatives as selective cannabinoid receptor 2 inverse agonists and osteoclast inhibitors: discovery, optimization, and biological evaluation.

Cannabinoid receptors have gained increasing attention as drug targets for developing potential therapeutic ligands. Here, we report the discovery and optimization of triaryl sulfonamides as a novel series possessing significant CB2 receptor affinity and selectivity. Four sets of triaryl ligands were designed and synthesized for further structural modifications and led to the identification of eight compounds as potent and selective CB2 inverse agonists with high binding affinity (CB2K(i) < 10 nM). Especially, compound 57 exhibited the strongest binding affinity on the CB2 receptor (CB2K(i) of 0.5 nM) and the best selectivity over the CB1 receptor (selectivity index of 2594). Importantly, 57 also showed potent inhibitory activity on osteoclast formation, and it was confirmed by a cell viability assay that the inhibition effects were not derived from the cytotoxicity. Finally, 3D QSAR studies confirmed our SAR findings that three bulky groups play an important role for CB2 receptor binding affinity.

Trisubstituted Sulfonamides: a New Chemotype for Development of Potent and Selective CB2 Receptor Inverse Agonists.

An extensive exploration of the SAR of a trisubstituted sulfonamides series led to the identification of 39, which is a potent and selective CB2 receptor inverse agonist (CB2Ki = 5.4 nM, and CB1Ki = 500 nM). The functional properties measured by cAMP assays indicated that the selected compounds were CB2 inverse agonists with high potency values (34, EC50 = 8.2 nM, and 39, EC50 = 2.5 nM). Furthermore, an osteoclastogenesis bioassay indicated that trisubstituted sulfonamide compounds showed great inhibition of osteoclast formation.

Lead discovery, chemistry optimization, and biological evaluation studies of novel biamide derivatives as CB2 receptor inverse agonists and osteoclast inhibitors.

N,N'-((4-(Dimethylamino)phenyl)methylene)bis(2-phenylacetamide) was discovered by using 3D pharmacophore database searches and was biologically confirmed as a new class of CB(2) inverse agonists. Subsequently, 52 derivatives were designed and synthesized through lead chemistry optimization by modifying the rings A-C and the core structure in further SAR studies. Five compounds were developed and also confirmed as CB(2) inverse agonists with the highest CB(2) binding affinity (CB(2)K(i) of 22-85 nM, EC(50) of 4-28 nM) and best selectivity (CB(1)/CB(2) of 235- to 909-fold). Furthermore, osteoclastogenesis bioassay indicated that PAM compounds showed great inhibition of osteoclast formation. Especially, compound 26 showed 72% inhibition activity even at the low concentration of 0.1 µM. The cytotoxicity assay suggested that the inhibition of PAM compounds on osteoclastogenesis did not result from its cytotoxicity. Therefore, these PAM derivatives could be used as potential leads for the development of a new type of antiosteoporosis agent.

Molecular fingerprint-based artificial neural networks QSAR for ligand biological activity predictions.

In this manuscript, we have reported a novel 2D fingerprint-based artificial neural network QSAR (FANN-QSAR) method in order to effectively predict biological activities of structurally diverse chemical ligands. Three different types of fingerprints, namely, ECFP6, FP2 and MACCS, were used in FANN-QSAR algorithm development, and FANN-QSAR models were compared to known 3D and 2D QSAR methods using five data sets previously reported. In addition, the derived models were used to predict GPCR cannabinoid ligand binding affinities using our manually curated cannabinoid ligand database containing 1699 structurally diverse compounds with reported cannabinoid receptor subtype CB(2) activities. To demonstrate its useful applications, the established FANN-QSAR algorithm was used as a virtual screening tool to search a large NCI compound database for lead cannabinoid compounds, and we have discovered several compounds with good CB(2) binding affinities ranging from 6.70 nM to 3.75 µM. To the best of our knowledge, this is the first report for a fingerprint-based neural network approach validated with a successful virtual screening application in identifying lead compounds. The studies proved that the FANN-QSAR method is a useful approach to predict bioactivities or properties of ligands and to find novel lead compounds for drug discovery research.

Recent advances in fragment-based QSAR and multi-dimensional QSAR methods.

This paper provides an overview of recently developed two dimensional (2D) fragment-based QSAR methods as well as other multi-dimensional approaches. In particular, we present recent fragment-based QSAR methods such as fragment-similarity-based QSAR (FS-QSAR), fragment-based QSAR (FB-QSAR), Hologram QSAR (HQSAR), and top priority fragment QSAR in addition to 3D- and nD-QSAR methods such as comparative molecular field analysis (CoMFA), comparative molecular similarity analysis (CoMSIA), Topomer CoMFA, self-organizing molecular field analysis (SOMFA), comparative molecular moment analysis (COMMA), autocorrelation of molecular surfaces properties (AMSP), weighted holistic invariant molecular (WHIM) descriptor-based QSAR (WHIM), grid-independent descriptors (GRIND)-based QSAR, 4D-QSAR, 5D-QSAR and 6D-QSAR methods.