Many G protein-coupled receptors (GPCRs), including the adenosine A(1) receptor (A(1)AR), have been shown to be allosterically modulated by small molecule ligands. So far, in the absence of structural information, the exact location of the allosteric site on the A(1)AR is not known. We synthesized a series of bivalent ligands (4) with an increasing linker length between the orthosteric and allosteric pharmacophores and used these as tools to search for the allosteric site on the A(1)AR. The compounds were tested in both equilibrium radioligand displacement and functional assays in the absence and presence of a reference allosteric enhancer, (2-amino-4,5-dimethyl-3-thienyl)-[3-(trifluoromethyl)phenyl]methanone, PD81,723 (1). Bivalent ligand N(6)-[2-amino-3-(3,4-dichlorobenzoyl)-4,5,6,7-tetrahydrothieno[2,3-c]pyridin-6-yl-9-nonyloxy-4-phenyl]-adenosine 4h (LUF6258) with a 9 carbon atom spacer did not show significant changes in affinity or potency in the presence of 1, indicating that this ligand bridged both sites on the receptor. Furthermore, 4h displayed an increase in efficacy, but not potency, compared to the parent, monovalent agonist 2. From molecular modeling studies, we speculate that the allosteric site of the A(1)AR is located in the proximity of the orthosteric site, possibly within the boundaries of the second extracellular loop of the receptor.
Ligand-based in silico hERG models were generated for 2 644 compounds using linear discriminant analysis (LDA) and support vector machines (SVM). As a result, the dataset used for the model generation is the largest publicly available (see Supporting Information). Extended connectivity fingerprints (ECFPs) and functional class fingerprints (FCFPs) were used to describe chemical space. All models showed area under curve (AUC) values ranging from 0.89 to 0.94 in a fivefold cross-validation, indicating high model consistency. Models correctly predicted 80 % of an additional, external test set; Y-scrambling was also performed to rule out chance correlation. Additionally models based on patch clamp data and radioligand binding data were generated separately to analyze their predictive ability when compared to combined models. To experimentally validate the models, 50 of the predicted hERG blockers from the Chembridge database and ten of the predicted non-hERG blockers from an in-house compound library were selected for biological evaluation. Out of those 50 predicted hERG blockers, tested at a concentration of 10 microM, 18 compounds showed more than 50 % displacement of [(3)H]astemizole binding to cell membranes expressing the hERG channel. K(i) values of four of the selected binders were determined to be in the micromolar and high nanomolar range (K(i) (VH01)=2.0 microM, K(i) (VH06)=0.15 microM, K(i) (VH19)=1.1 microM and K(i) (VH47)=18 microM). Of these four compounds, VH01 and VH47 showed also a second, even higher affinity binding site with K(i) values of 7.4 nM and 36 nM, respectively. In the case of non-hERG blockers, all ten compounds tested were found to be inactive, showing less than 50 % displacement of [(3)H]astemizole binding at 10 microM. These experimentally validated models were then used to virtually screen commercial compound databases to evaluate whether they contain hERG blockers. 109 784 (23 %) of Chembridge, 133 175 (38 %) of Chemdiv, 111 737 (31 %) of Asinex and 11 116 (18 %) of the Maybridge database were predicted to be hERG blockers by at least two of the models, a prediction which could, for example, be used as a pre-filtering tool for compounds with potential hERG liabilities.
Cyclooxygenase-2 (COX-2) is overexpressed in many human cancers and converts the n-6 polyunsaturated fatty acid (PUFA) arachidonic acid to prostaglandin E(2) (PGE(2)), which drives tumorigenesis; in contrast, n-3 PUFA inhibit tumorigenesis. We tested the hypothesis that these antitumor actions of n-3 PUFA may involve the n-3 olefinic bond. n-3 Monounsaturated fatty acids (MUFAs) of chain length C16-C22 were synthesized and evaluated in MDA-MB-468 breast cancer cells that stably overexpressed COX-2 (MDA-COX-2 cells). Longer chain (C19-C22) n-3 MUFAs inhibited proliferation, activated apoptosis, decreased PGE(2) formation, and decreased cell invasion; C16-C18 analogues were less active. Molecular modeling showed that interactions of Arg120, Tyr355, and several hydrophobic amino acid residues in the COX-2 active site with C19-C22 MUFA analogues were favored. Thus, longer-chain n-3 MUFAs may be prototypes of novel anticancer agents that decrease the formation of PGE(2) in tumor cells that contain high levels of COX-2.
Di-2-pyridylketone 4,4-dimethyl-3-thiosemicarbazone (Dp44mT) demonstrates potent anti-cancer activity. We previously demonstrated that 14C-Dp44mT enters and targets cells through a carrier/receptor-mediated uptake process. Despite structural similarity, 2-benzoylpyridine 4-ethyl-3-thiosemicarbazone (Bp4eT) and pyridoxal isonicotinoyl hydrazone (PIH) enter cells via passive diffusion. Considering albumin alters the uptake of many drugs, we examined the effect of human serum albumin (HSA) on the cellular uptake of Dp44mT, Bp4eT and PIH. Chelator-HSA binding studies demonstrated the following order of relative affinity: Bp4eT≈PIH>Dp44mT. Interestingly, HSA decreased Bp4eT and PIH uptake, potentially due to its high affinity for the ligands. In contrast, HSA markedly stimulated Dp44mT uptake by cells, with two saturable uptake mechanisms identified. The first mechanism saturated at 5-10 μM (Bmax:1.20±0.04 × 107 molecules/cell; Kd:33±3 μM) and was consistent with a previously identified Dp44mT receptor/carrier. The second mechanism was of lower affinity, but higher capacity (Bmax:2.90±0.12 × 107 molecules/cell; Kd:65±6 μM), becoming saturated at 100 μM and was only evident in the presence of HSA. This second saturable Dp44mT uptake process was inhibited by excess HSA and had characteristics suggesting it was mediated by a specific binding site. Significantly, the HSA-mediated increase in the targeting of Dp44mT to cancer cells potentiated apoptosis and could be important for enhancing efficacy.
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