In the present study we have investigated whether pharmacophore models may account for the activity and selectivity of the known cyclooxygenase-2 (COX-2) selective inhibitors of the phenylsulfonyl tricyclic series, i.e., Celecoxib (1) and Rofecoxib (3), and whether transferring this structural information onto the frame of a nonsteroidal antiinflammatory drug (NSAID), known to tightly bind the enzyme active site, may be useful for designing novel COX-2 selective inhibitors. With this aim we have developed a pharmacophore based on the geometric disposition of chemical features in the most favorable conformation of the COX-2 selective inhibitors SC-558 (2; analogue of Celecoxib (1)) and Rofecoxib (3) and the more restrained compounds 4 (DFU) and 5. The pharmacophore model contains a sulfonyl S atom, an aromatic ring (ring plane A) with a fixed position of the normal to the plane, and an additional aromatic ring (ring plane B), both rings forming a dihedral angle of 290 degrees +/- 10 degrees. The final disposition of the pharmacophoric groups parallels the geometry of the ligand SC-558 (2) in the known crystal structure of the COX-2 complex. Moreover, the nonconserved residue 523 is known to be important for COX-2 selective inhibition; thus, the crystallographic information was used to position an excluded volume in the pharmacophore, accounting for the space limits imposed by this nonconserved residue. The geometry of the final five-feature pharmacophore was found to be consistent with the crystal structure of the nonselective NSAID indomethacin (6) in the COX-2 complex. This result was used to design indomethacin analogues 8 and 9 that exhibited consistent structure-activity relationships leading to the potent and selective COX-2 inhibitor 8a. Compound 8a (LM-1685) was selected as a promising candidate for further pharmacological evaluation.

Molecular modeling studies performed on the two cyclooxygenase isozymes (COXs) suggest that active site hydration is crucial for understanding inhibitor selectivity. In this work, models have been constructed considering some implicit water molecules, placed in the position suggested by GRID, that participate in the dynamic hydrogen-bonding network at the polar active site entrance together with protein residues 355, 524, 120, and 513. The selectivity observed for ketoprofen (1) and the structural analogues 2 and 3 may be rationalized in terms of such implicit hydration.

The present work focuses on the study of the three-dimensional (3D) structural requirements for the leukotriene D(4) (LTD(4)) antagonistic activity of compounds having the basic quinolinyl(bridged)aryl framework. An approach combining pharmacophore mapping, molecule alignment, and CoMFA models was used to derive a hypothesis for a series of LTD(4) antagonists having the basic diaryl-bridged framework. In this compound series, the produced pharmacophore hypotheses have shown to yield molecule alignments suitable to derive valuable CoMFA models. Model selection focused on (1) obtention of coherent modeling results, (2) consistency with the available SAR data, and (3) ability to predict the activity of an independent set of congeneric molecules. This approach resulted in a combined pharmacophore and CoMFA model that can generally represent the antagonistic activity within a log unit of the measured value for compounds of the series. The resulting pharmacophore (model C) consists of an acidic or negative ionizable function (AC), a hydrogen-bond acceptor (HBA), and three hydrophobic regions (HY) and produces chemically meaningful alignments with the most active compounds of the series mapping the pharmacophore in a extended energetically favorable conformation.

Smart cities and smart tourism destinations integrate technological infrastructures and end-user devices with the aim of providing more satisfying experiences. They generate expectations of superior innovation, greater interactivity, participation and a better image. Therefore, they need to communicate their smartness and include it in their branding. The study analyses the smart content present in the tourist and non-tourist official Twitter accounts of a selection of smart cities and smart destinations, through semantic analysis, in order to find out which smart concepts are strategically branded to create a smart tourism destination image. The results show that the best cities in the ranking for each smart topic are not the ones with highest percentages of tweets on that topic. The study also shows that the number and percentage of tweets that communicate smart aspects on the platforms analyzed are low, showing the smartness is not included in their branding. It has also been observed that, almost all cities communicate or do not communicate the same concepts, evincing an undifferentiated branding strategy. The results of this study are interesting for urban policy-makers and tourism destination marketers to improve their branding.

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