QSAR METHODS DEVELOPMENT, VIRTUAL AND EXPERIMENTAL SCREENING FOR CANNABINOID LIGAND DISCOVERY

G protein coupled receptors (GPCRs) are the largest receptor family in mammalian genomes and are known to regulate wide variety of signals such as ions, hormones and neurotransmitters. It has been estimated that GPCRs represent more than 30% of current drug targets and have attracted many pharmaceutical industries as well as academic groups for potential drug discovery. Cannabinoid (CB) receptors, members of GPCR superfamily, are also involved in the activation of multiple intracellular signal transductions and their endogenous ligands or cannabinoids have attracted pharmacological research because of their potential therapeutic effects. In particular, the cannabinoid subtype-2 (CB2) receptor is known to be involved in immune system signal transductions and its ligands have the potential to be developed as drugs to treat many immune system disorders without potential psychotic side-effects. Therefore, this work was focused on discovering novel CB2 ligands by developing novel quantitative structure-activity relationship (QSAR) methods and performing virtual and experimental screenings. Three novel QSAR methods were developed to predict biological activities and binding affinities of ligands. In the first method, a traditional fragment-based approach was improved by introducing a fragment similarity concept that enhanced the prediction accuracy remarkably. In the second method, pharmacophoric and morphological descriptors were incorporated to derive a novel QSAR regression model with good prediction accuracy. In the third method, a novel fingerprint-based artificial neural network QSAR model was developed to overcome the similar scaffold requirement of many fragment-based and other 3D-QSAR methods. These methods provide a foundation for virtual screening and hit ranking of chemical ligands from large chemical space. In addition, several novel CB2 selective ligands within nM binding affinities were discovered. These ligands were proven to be inverse agonists as validated by functional assays and could be useful probes to study CB2 signaling as well as potential drug candidates for autoimmune disesases

Blocking the ZZ domain of sequestosome1/p62 suppresses myeloma growth and osteoclast formation in vitro and induces dramatic bone formation in myeloma-bearing bones in vivo

We reported that p62 (sequestosome 1) serves as a signaling hub in bone marrow stromal cells (BMSCs) for the formation of signaling complexes, including NFκB, p38MAPK and JNK, that are involved in the increased osteoclastogenesis and multiple myeloma (MM) cell growth induced by BMSCs that are key contributors to multiple myeloma bone disease (MMBD), and demonstrated that the ZZ domain of p62 (p62-ZZ) is required for BMSC enhancement of MMBD. We recently identified a novel p62-ZZ inhibitor, XRK3F2, which inhibits MM cell growth and BMSC growth enhancement of human MM cells. In the current study, we evaluate the relative specificity of XRK3F2 for p62-ZZ, characterize XRK3F2's capacity to inhibit growth of primary MM cells and human MM cell lines, and test the in vivo effects of XRK3F2 in the immunocompetent 5TGM1 MM model. We found that XRK3F2 induces dramatic cortical bone formation that is restricted to MM containing bones and blocked the effects and upregulation of tumor necrosis factor alpha (TNFα), an osteoblast (OB) differentiation inhibitor that is increased in the MM bone marrow microenvironment and utilizes signaling complexes formed on p62-ZZ, in BMSC. Interestingly, XRK3F2 had no effect on non-MM bearing bone. These results demonstrate that targeting p62 in MM models has profound effects on MMBD

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

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 CB₂ 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 CB₂ 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

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

An extensive exploration of the structure–activity relationship of a trisubstituted sulfonamide series led to the identification of <b>39</b>, which is a potent and selective CB₂ receptor inverse agonist [<i>K</i>_i(CB₂) = 5.4 nM, and <i>K</i>_i(CB₁) = 500 nM]. The functional properties measured by cAMP assays indicated that the selected compounds were CB₂ inverse agonists with high potency values (for <b>34</b>, EC₅₀ = 8.2 nM, and for <b>39</b>, 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