Jian-Zhong Chen

Beta-caryophyllene is a dietary cannabinoid.

The psychoactive cannabinoids from Cannabis sativa L. and the arachidonic acid-derived endocannabinoids are nonselective natural ligands for cannabinoid receptor type 1 (CB1) and CB2 receptors. Although the CB1 receptor is responsible for the psychomodulatory effects, activation of the CB2 receptor is a potential therapeutic strategy for the treatment of inflammation, pain, atherosclerosis, and osteoporosis. Here, we report that the widespread plant volatile (E)-β-caryophyllene [(E)-BCP] selectively binds to the CB2 receptor (Ki = 155 ± 4 nM) and that it is a functional CB2 agonist. Intriguingly, (E)-BCP is a common constituent of the essential oils of numerous spice and food plants and a major component in Cannabis. Molecular docking simulations have identified a putative binding site of (E)-BCP in the CB2 receptor, showing ligand π–π stacking interactions with residues F117 and W258. Upon binding to the CB2 receptor, (E)-BCP inhibits adenylate cylcase, leads to intracellular calcium transients and weakly activates the mitogen-activated kinases Erk1/2 and p38 in primary human monocytes. (E)-BCP (500 nM) inhibits lipopolysaccharide (LPS)-induced proinflammatory cytokine expression in peripheral blood and attenuates LPS-stimulated Erk1/2 and JNK1/2 phosphorylation in monocytes. Furthermore, peroral (E)-BCP at 5 mg/kg strongly reduces the carrageenan-induced inflammatory response in wild-type mice but not in mice lacking CB2 receptors, providing evidence that this natural product exerts cannabimimetic effects in vivo. These results identify (E)-BCP as a functional nonpsychoactive CB2 receptor ligand in foodstuff and as a macrocyclic antiinflammatory cannabinoid in Cannabis.

Alkylamides from Echinacea Are a New Class of Cannabinomimetics CANNABINOID TYPE 2 RECEPTOR-DEPENDENT AND -INDEPENDENT IMMUNOMODULATORY EFFECTS

Alkylamides (alkamides) from Echinacea modulate tumor necrosis factor α mRNA expression in human monocytes/macrophages via the cannabinoid type 2 (CB2) receptor (Gertsch, J., Schoop, R., Kuenzle, U., and Suter, A. (2004) FEBS Lett. 577, 563–569). Here we show that the alkylamides dodeca-2E,4E,8Z,10Z-tetraenoic acid isobutylamide (A1) and dodeca-2E,4E-dienoic acid isobutylamide (A2) bind to the CB2 receptor more strongly than the endogenous cannabinoids. The Ki values of A1 and A2 (CB2 ∼60 nm;CB1 >1500 nm) were determined by displacement of the synthetic high affinity cannabinoid ligand [3H]CP-55,940. Molecular modeling suggests that alkylamides bind in the solvent-accessible cavity in CB2, directed by H-bonding and π -π interactions. In a screen with 49 other pharmacologically relevant receptors, it could be shown that A1 and A2 specifically bind to CB2 and CB1. A1 and A2 elevated total intracellular Ca2+ in CB2-positive but not in CB2-negative promyelocytic HL60 cells, an effect that was inhibited by the CB2 antagonist SR144528. At 50 nm, A1, A2, and the endogenous cannabinoid anandamide (CB2 Ki >200 nm) up-regulated constitutive interleukin (IL)-6 expression in human whole blood in a seemingly CB2-dependent manner. A1, A2, anandamide, the CB2 antagonist SR144528 (Ki <10 nm), and also the non-CB2-binding alkylamide undeca-2E-ene,8,10-diynoic acid isobutylamide all significantly inhibited lipopolysaccharide-induced tumor necrosis factor α, IL-1β, and IL-12p70 expression (5–500 nm) in a CB2-independent manner. Alkylamides and anandamide also showed weak differential effects on anti-CD3-versus anti-CD28-stimulated cytokine expression in human whole blood. Overall, alkylamides, anandamide, and SR144528 potently inhibited lipopolysaccharide-induced inflammation in human whole blood and exerted modulatory effects on cytokine expression, but these effects are not exclusively related to CB2 binding.

3D structural model of the G-protein-coupled cannabinoid CB2 receptor

The potential for therapeutic specificity in regulating diseases and for reduced side effects has made cannabinoid (CB) receptors one of the most important G‐protein‐coupled receptor (GPCR) targets for drug discovery. The cannabinoid (CB) receptor subtype CB2 is of particular interest due to its involvement in signal transduction in the immune system and its increased characterization by mutational and other studies. However, our understanding of their mode of action has been limited by the absence of an experimental receptor structure. In this study, we have developed a 3D model of the CB2 receptor based on the recent crystal structure of a related GPCR, bovine rhodopsin. The model was developed using multiple sequence alignment of homologous receptor sub‐types in humans and mammals, and compared with other GPCRs. Alignments were analyzed with mutation scores, pairwise hydrophobicity profiles and Kyte‐Doolittle plots. The 3D model of the transmembrane segment was generated by mapping the CB2 sequence onto the homologous residues of the rhodopsin structure. The extra‐ and intracellular loop regions of the CB2 were generated by searching for homologous Cα backbone sequences in published structures in the Brookhaven Protein Databank (PDB). Residue side chains were positioned through a combination of rotamer library searches, simulated annealing and minimization. Intermediate models of the 7TM helix bundles were analyzed in terms of helix tilt angles, hydrogen‐bond networks, conserved residues and motifs, possible disulfide bonds. The amphipathic cytoplasmic helix domain was also correlated with biological and site‐directed mutagenesis data. Finally, the model receptor‐binding cavity was characterized using solvent‐accessible surface approach. Proteins 2003.

GPCR structure-based virtual screening approach for CB2 antagonist search.

The potential for therapeutic specificity in regulating diseases has made cannabinoid (CB) receptors one of the most important G-protein-coupled receptor (GPCR) targets in search for new drugs. Considering the lack of related 3D experimental structures, we have established a structure-based virtual screening protocol to search for CB2 bioactive antagonists based on the 3D CB2 homology structure model. However, the existing homology-predicted 3D models often deviate from the native structure and therefore may incorrectly bias the in silico design. To overcome this problem, we have developed a 3D testing database query algorithm to examine the constructed 3D CB2 receptor structure model as well as the predicted binding pocket. In the present study, an antagonist-bound CB2 receptor complex model was initially generated using flexible docking simulation and then further optimized by molecular dynamic and mechanical (MD/MM) calculations. The refined 3D structural model of the CB2-ligand complex was then inspected by exploring the interactions between the receptor and ligands in order to predict the potential CB2 binding pocket for its antagonist. The ligand-receptor complex model and the predicted antagonist binding pockets were further processed and validated by FlexX-Pharm docking against a testing compound database that contains known antagonists. Furthermore, a consensus scoring (CScore) function algorithm was established to rank the binding interaction modes of a ligand on the CB2 receptor. Our results indicated that the known antagonists seeded in the testing database can be distinguished from a significant amount of randomly chosen molecules. Our studies demonstrated that the established GPCR structure-based virtual screening approach provided a new strategy with a high potential for in silico identifying novel CB2 antagonist leads based on the homology-generated 3D CB2 structure model.

Data Mining a Small Molecule Drug Screening Representative Subset from NIH PubChem

PubChem is a scientific showcase of the NIH Roadmap Initiatives. It is a compound repository created to facilitate information exchange and data sharing among the NIH Roadmap-funded Molecular Library Screening Center Network (MLSCN) and the scientific community. However, PubChem has more than 10 million records of compound information. It will be challenging to conduct a drug screening of the whole database of millions of compounds. Thus, the purpose of the present study was to develop a data mining cheminformatics approach in order to construct a representative and structure-diverse sublibrary from the large PubChem database. In this study, a new chemical diverse representative subset, rePubChem, was selected by whole-molecule chemistry-space matrix calculation using the cell-based partition algorithm. The representative subset was generated and was then subjected to evaluations by compound property analyses based on 1D and 2D molecular descriptors. The new subset was also examined and assessed for self-similarity analysis based on 2D molecular fingerprints in comparing with the source compound library. The new subset has a much smaller library size (540K compounds) with minimum similarity and redundancy without loss of the structural diversity and basic molecular properties of its parent library (5.3 million compounds). The new representative subset library generated could be a valuable structure-diverse compound resource for in silico virtual screening and in vitro HTS drug screening. In addition, the established subset generation method of using the combined cell-based chemistry-space partition metrics with pairwised 2D fingerprint-based similarity search approaches will also be important to a broad scientific community interested in acquiring structurally diverse compounds for efficient drug screening, building representative virtual combinatorial chemistry libraries for syntheses, and data mining large compound databases like the PubChem library in general.

Diverging Rh(I)-Catalyzed Carbocylization Strategy to Prepare a Library of Unique Cyclic Ethers

A library of 90 carboxamide-containing oxepines and pyrans was synthesized. A dual-branching strategy was used where a common intermediate, an allenyl-hydroxy ester, was either allylated or propargylated then subjected to rhodium(I)-catalyzed carbocyclization reaction conditions to afford an oxepine- or triene-containing pyran, respectively. The oxepines were selectively reduced to afford two functionally unique scaffolds using complementary hydrogenation conditions. Diversification of the oxepines and pyrans involved conversion of the methyl carboxylate group to a carboxamide via either a microwave-assisted amidation using polymer-bound carbodiimide (DCC) and 1-hydroxybenzotriazole (HOBt) or a NaCN-catalyzed aminolysis. The scope of a rarely used carbonyl-yne reaction was expanded to the preparation of 10 new allenyl-hydroxy esters using microwave irradiation. Finally, a cell-based diversity analysis using BCUT (Burden (B) CAS (C) Pearlman at the University of Texas (UT)) metrics calculations and two-dimensional fingerprint similarity approaches shows that when compared to the 100,000 Pittsburgh Molecular Library Screening Center (PMLSC) compound database and PubChem the new compound library occupies a unique chemical space.

New QSAR prediction models derived from GPCR CB2-antagonistic triaryl bis-sulfone analogues by a combined molecular morphological and pharmacophoric approach

In order to build quantitative structure–activity relationship (QSAR) models for virtual screening of novel cannabinoid CB2 ligands and hit ranking selections, a new QSAR algorithm has been developed for the cannabinoid ligands, triaryl bis-sulfones, using a combined molecular morphological and pharmacophoric search approach. Both pharmacophore features and shape complementarity were considered using a number of molecular descriptors, including Surflex–Sim similarity and Unity Query fit, in addition to other molecular properties such as molecular weight, ClogP, molecular volume, molecular area, molecular polar volume, molecular polar surface area and dipole moment. Subsequently, partial least squares regression analyses were carried out to derive QSAR models linking bioactivity and the descriptors mentioned, using a training set of 25 triaryl bis-sulfones. Good prediction capability was confirmed for the best QSAR model by evaluation against a test set of a further 20 triaryl bis-sulfones. The pharmacophore and molecular shape-based QSAR scoring function now established can be used to predict the biological properties of virtual hits or untested compounds obtained from ligand-based virtual screenings.

Design, synthesis and 3D-QSAR study of cytotoxic flavonoid derivatives

Three series of flavonoid derivatives were designed and synthesized. All synthesized compounds were evaluated for cytotoxic activities against five human cancer cell lines, including K562, PC-3, MCF-7, A549, and HO8910. Among the compounds tested, compound 9d exhibited the most potent cytotoxic activity with IC50 values of 2.76–6.98μM. Further comparative molecular field analysis was performed to conduct a 3D quantitative structure–activity relationship study. The generated 3D-QSAR model could be used for further rational design of novel flavonoid analogs as highly potent cytotoxic agents.

Parallel synthesis of novel antitumor agents: 1,2,3-triazoles bearing biologically active sulfonamide moiety and their 3D-QSAR.

A 60-member 1,2,3-triazoles bearing biologically active sulfonamide moiety library was synthesized via azide–alkyne cycloaddition and examined for cytotoxic activity against human leukemia cell line HL-60. 25 of them were evaluated further in four additional cancer cell lines (HepG2, A549, PC3, SGC7901). Most of the 25 compounds showed moderate cytotoxic activities against the tested cell lines. Furthermore, the structure–activity relationships were discussed and a reliable 3D-QSAR model with good prediction