19 research outputs found
Experience With Bexarotene to Treat Cutaneous T-Cell Lymphomas: A Study of the Spanish Working Group of Cutaneous Lymphomas
Background and objectives: Bexarotene has been approved to treat advanced stage cutaneous T -cell lymphomas (CTCL) since 1999. However, very few data have been published on its long-term safety and efficacy profile. The aim of this study is to determine the tolerability to bexarotene and outcomes by collecting the 2nd largest case series to date on its long-term use vs CTCL. Material and method: This was a multicenter retrospective review of 216 patients with mycosis fungoides (174), or S & eacute;zary syndrome (42) on a 10 -year course of bexarotene alone or in combination with other therapies at 19 tertiary referral teaching hospitals. Results: A total of 133 men (62%) and 83 women (38%) were included, with a mean age of 63.5 year (27 - 95). A total of 45% were on bexarotene monotherapy for the entire study period, 22% started on bexarotene but eventually received an additional therapy, 13% were on another treatment but eventually received bexarotene while the remaining 20% received a combination therapy since the beginning. The median course of treatment was 20.78 months (1 - 114); and the overall response rate, 70.3%. Complete and partial response rates were achieved in 26% and 45% of the patients, respectively. Treatment was well tolerated, being the most common toxicities hypertriglyceridemia (79%), hypercholesterolemia (71%), and hypothyroidism (52%). No treatment -related grade 5 adverse events were reported. Conclusions: Our study confirms bexarotene is a safe and effective therapy for the long-term treatment of CTCL. (c) 2024 AEDV. Published by Elsevier Espana, S.L.U. This is an open access article under the CC BY -NC -ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Intraoperative oxygen tension and redox homeostasis in Pseudomyxoma peritonei: A short case series
IntroductionPseudomyxoma peritonei (PMP) is a rare malignant disease characterized by a massive multifocal accumulation of mucin within the peritoneal cavity. The current treatment option is based on complete cytoreductive surgery combined with hyperthermic intraperitoneal chemotherapy. However, the recurrence is frequent with subsequent progression and death. To date, most of the studies published in PMP are related to histological and genomic analyses. Thus, the need for further studies unveiling the underlying PMP molecular mechanisms is urgent. In this regard, hypoxia and oxidative stress have been extensively related to tumoral pathologies, although their contribution to PMP has not been elucidated.MethodsIn this manuscript, we have evaluated, for the first time, the intratumoral real-time oxygen microtension (pO2mt) in the tumor (soft and hard mucin) and surrounding healthy tissue from five PMP patients during surgery. In addition, we measured hypoxia (Hypoxia Inducible Factor-1a; HIF-1α) and oxidative stress (catalase; CAT) markers in soft and hard mucin from the same five PMP patient samples and in five control samples.ResultsThe results showed low intratumoral oxygen levels, which were associated with increased HIF-1α protein levels, suggesting the presence of a hypoxic environment in these tumors. We also found a significant reduction in CAT activity levels in soft and hard mucin compared with healthy tissue samples.DiscussionIn conclusion, our study provides the first evidence of low intratumoral oxygen levels in PMP patients associated with hypoxia and oxidative stress markers. However, further investigation is required to understand the potential role of oxidative stress in PMP in order to find new therapeutic strategies
Differential Scanning Fluorometry Signatures as Indicators of Enzyme Inhibitor Mode of Action: Case Study of Glutathione S-Transferase
Differential scanning fluorometry (DSF), also referred to as fluorescence thermal shift, is emerging as a convenient method to evaluate the stabilizing effect of small molecules on proteins of interest. However, its use in the mechanism of action studies has received far less attention. Herein, the ability of DSF to report on inhibitor mode of action was evaluated using glutathione S-transferase (GST) as a model enzyme that utilizes two distinct substrates and is known to be subject to a range of inhibition modes. Detailed investigation of the propensity of small molecule inhibitors to protect GST from thermal denaturation revealed that compounds with different inhibition modes displayed distinct thermal shift signatures when tested in the presence or absence of the enzyme's native co-substrate glutathione (GSH). Glutathione-competitive inhibitors produced dose-dependent thermal shift trendlines that converged at high compound concentrations. Inhibitors acting via the formation of glutathione conjugates induced a very pronounced stabilizing effect toward the protein only when GSH was present. Lastly, compounds known to act as noncompetitive inhibitors exhibited parallel concentration-dependent trends. Similar effects were observed with human GST isozymes A1-1 and M1-1. The results illustrate the potential of DSF as a tool to differentiate diverse classes of inhibitors based on simple analysis of co-substrate dependency of protein stabilization
Electrostatic Effects in the Folding of the SH3 Domain of the c-Src Tyrosine Kinase: pH-Dependence in 3D-Domain Swapping and Amyloid Formation
The SH3 domain of the c-Src tyrosine kinase (c-Src-SH3) aggregates to form intertwined dimers and amyloid fibrils at mild acid pHs. In this work, we show that a single mutation of residue Gln128 of this SH3 domain has a significant effect on: (i) its thermal stability; and (ii) its propensity to form amyloid fibrils. The Gln128Glu mutant forms amyloid fibrils at neutral pH but not at mild acid pH, while Gln128Lys and Gln128Arg mutants do not form these aggregates under any of the conditions assayed. We have also solved the crystallographic structures of the wild-type (WT) and Gln128Glu, Gln128Lys and Gln128Arg mutants from crystals obtained at different pHs. At pH 5.0, crystals belong to the hexagonal space group P6522 and the asymmetric unit is formed by one chain of the protomer of the c-Src-SH3 domain in an open conformation. At pH 7.0, crystals belong to the orthorhombic space group P212121, with two molecules at the asymmetric unit showing the characteristic fold of the SH3 domain. Analysis of these crystallographic structures shows that the residue at position 128 is connected to Glu106 at the diverging β-turn through a cluster of water molecules. Changes in this hydrogen-bond network lead to the displacement of the c-Src-SH3 distal loop, resulting also in conformational changes of Leu100 that might be related to the binding of proline rich motifs. Our findings show that electrostatic interactions and solvation of residues close to the folding nucleation site of the c-Src-SH3 domain might play an important role during the folding reaction and the amyloid fibril formation.This research was funded by the Spanish Ministry of Science and Innovation and Ministry of Economy and Competitiveness and FEDER (EU): BIO2009-13261-C02-01/02 (ACA); BIO2012-39922-C02-01/02 (ACA); CTQ2013-4493 (JLN) and CSD2008-00005 (JLN); Andalusian Regional Government (Spain) and FEDER (EU): P09-CVI-5063 (ACA); and Valentian Regional Government (Spain) and FEDER (EU): Prometeo 2013/018 (JLN). Data collection was supported by European Synchrotron Radiation Facility (ESRF), Grenoble, France: BAG proposals MX-1406 (ACA) and MX-1541 (ACA); and ALBA (Barcelona, Spain) proposals 2012010072 (ACA) and 2012100378 (ACA)
Tetrahydroisoquinolines functionalized with carbamates as selective ligands of D2 dopamine receptor
[EN] A series of tetrahydroisoquinolines functionalized with carbamates is reported here as highly selective ligands on the dopamine D2 receptor. These compounds were selected by means of a molecular modeling study. The studies were carried out in three stages: first an exploratory study was carried out using combined docking techniques and molecular dynamics simulations. According to these results, the bioassays were performed; these experimental studies corroborated the results obtained by molecular modeling. In the last stage of our study, a QTAIM analysis was performed in order to determine the main molecular interactions that stabilize the different ligand-receptor complexes. Our results show that the adequate use of combined simple techniques is a very useful tool to predict the potential affinity of new ligands at dopamine D1 and D2 receptors. In turn the QTAIM studies show that they are very useful to evaluate in detail the molecular interactions that stabilize the different ligand-receptor complexes; such information is crucial for the design of new ligands.This work was supported by Universidad Nacional de San Luis (UNSL) and CONICET grants 2-1214 and PIP444, respectively. E.L.A, L.J.G, S.A.A and R.D.E are staff members of the National Scientific and Technical Research Council - Argentina ( CONICET, Argentina).Parravicini, O.; Bogado, ML.; Rojas, S.; Angelina, EL.; Andujar, SA.; Gutierrez, LJ.; Cabedo Escrig, N.... (2017). Tetrahydroisoquinolines functionalized with carbamates as selective ligands of D2 dopamine receptor. Journal of Molecular Modeling. 23(9):1-14. https://doi.org/10.1007/s00894-017-3441-6S114239Beaulieu JM, Gainetdinov RR (2011) The physiology, signaling, and pharmacology of dopamine receptors. Pharmacol Rev 63(1):182–217. https://doi.org/10.1124/pr.110.002642Luthra PM, Kumar JBS (2012) Plausible improvements for selective targeting of dopamine receptors in therapy of Parkinson’s disease. Mini-Rev Med Chem 12(14):1556–1564. https://doi.org/10.2174/138955712803832645Poewe W (2009) Treatments for Parkinson disease-past achievements and current clinical needs. Neurology 72 (7 SUPPL. 2):S65-S73. https://doi.org/10.1212/WNL.0b013e31819908ceSeeman P, Watanabe M, Grigoriadis D (1985) Dopamine D2 receptor binding sites for agonists: a tetrahedral model. Mol Pharmacol 28(5):391–399McDonald WM, Sibley DR, Kilpatrick BF, Caron MG (1984) Dopaminergic inhibition of adenylate cyclase correlates with high affinity agonist binding to anterior pituitary D2 dopamine receptors. Mol Cell Endocrinol 36(3):201–209. https://doi.org/10.1016/0303-7207(84)90037-6Mottola DM, Laiter S, Watts VJ, Tropsha A, Wyrick SD, Nichols DE, Mailman RB (1996) Conformational analysis of D1 dopamine receptor agonists: pharmacophore assessment and receptor mapping. J Med Chem 39(1):285–296. https://doi.org/10.1021/jm9502100Alkorta I, Villar HO (1993) Considerations on the recognition of the D1 receptor by agonists. J Comput Aided Mol Des 7(6):659–670. https://doi.org/10.1007/BF00125324Cueva JP, Giorgioni G, Grubbs RA, Chemel BR, Watts VJ, Nichols DE (2006) Trans-2,3-dihydroxy-6a,7,8,12b-tetrahydro-6H-chromeno[3,4-c]isoquinoline: synthesis, resolution, and preliminary pharmacological characterization of a new dopamine D1 receptor full agonist. J Med Chem 49(23):6848–6857. https://doi.org/10.1021/jm0604979Negash K, Nichols DE, Watts VJ, Mailman RB (1997) Further definition of the D1 dopamine receptor pharmacophore: synthesis of trans-6,6a,7,8,9,13b-hexahydro-5h-benzo[d]naphth[2,1-b]azepines as rigid analogues of β-phenyldopamine. J Med Chem 40(14):2140–2147. https://doi.org/10.1021/jm970157aPettersson I, Liljefors T (1987) Structure-activity relationships for apomorphine congeners. Conformational energies vs. biological activities. J Comput Aided Mol Des 1(2):143–152. https://doi.org/10.1007/BF01676958Tonani R, Dunbar Jr J, Edmonston B, Marshall GR (1987) Computer-aided molecular modeling of a D2-agonist dopamine pharmacophore. J Comput Aided Mol Des 1(2):121–132. https://doi.org/10.1007/BF01676956Mewshaw RE, Kavanagh J, Stack G, Marquis KL, Shi X, Kagan MZ, Webb MB, Katz AH, Park A, Kang YH, Abou-Gharbia M, Scerni R, Wasik T, Cortes-Burgos L, Spangler T, Brennan JA, Piesla M, Mazandargmi H, Cockett MI, Ochalski R, Coupet J, Andree TH (1997) New generation dopaminergic agents. 1. Discovery of a novel scaffold which embraces the D2 agonist pharmacophore. Structure-activity relationships of a series of 2-(aminomethyl)chromans. J Med Chem 40(26):4235–4256. https://doi.org/10.1021/jm9703653Chidester CG, Lin CH, Lahti RA, Haadsma-Svensson SR, Smith MW (1993) Comparison of 5-HT1A and dopamine D2 pharmacophores. X-ray structures and affinities of conformationally constrained ligands. J Med Chem 36(10):1301–1315Alkorta I, Villar HO (1994) Molecular electrostatic potential of d1 and d2 dopamine agonists. J Med Chem 37(1):210–213Wilcox RE, Tseng T, Brusniak MYK, Ginsburg B, Pearlman RS, Teeter M, Durand C, Starr S, Neve KA (1998) CoMFA-based prediction of agonist affinities at recombinant D1 vs D2 dopamine receptors. J Med Chem 41(22):4385–4399. https://doi.org/10.1021/jm9800292El Aouad N, Berenguer I, Romero V, Marín P, Serrano A, Andujar S, Suvire F, Bermejo A, Ivorra MD, Enriz RD, Cabedo N, Cortes D (2009) Structure-activity relationship of dopaminergic halogenated 1-benzyl-tetrahydroisoquinoline derivatives. Eur J Med Chem 44(11):4616–4621. https://doi.org/10.1016/j.ejmech.2009.06.033Berenguer I, Aouad NE, Andujar S, Romero V, Suvire F, Freret T, Bermejo A, Ivorra MD, Enriz RD, Boulouard M, Cabedo N, Cortes D (2009) Tetrahydroisoquinolines as dopaminergic ligands: 1-butyl-7-chloro-6-hydroxy-tetrahydroisoquinoline, a new compound with antidepressant-like activity in mice. Bioorg Med Chem 17(14):4968–4980. https://doi.org/10.1016/j.bmc.2009.05.079Andujar S, Suvire F, Berenguer I, Cabedo N, Marin P, Moreno L, Dolores Ivorra M, Cortes D, Enriz RD (2012) Tetrahydroisoquinolines acting as dopaminergic ligands. A molecular modeling study using MD simulations and QM calculations. J Mol Model 18(2):419–431. https://doi.org/10.1007/s00894-011-1061-0Angelina E, Andujar S, Tosso RD, Enriz RD, Peruchena N (2014) Non-covalent interactions in receptor–ligand complexes. A study based on the electron charge density. J Phys Org Chem 27:128–134Parraga J, Cabedo N, Andujar S, Piqueras L, Moreno L, Galan A, Angelina E, Enriz RD, Ivorra MD, Sanz MJ, Cortes D (2013) 2,3,9- and 2,3,11-trisubstituted tetrahydroprotoberberines as D2 dopaminergic ligands. Eur J Med Chem 68:150–166Andujar SA, de Angel BM, Charris JE, Israel A, Suarez-Roca H, Lopez SE, Garrido MR, Cabrera EV, Visbal G, Rosales C, Suvire FD, Enriz RD, Angel-Guio JE (2008) Synthesis, dopaminergic profile, and molecular dynamics calculations of N-aralkyl substituted 2-aminoindans. Bioorg Med Chem 16(6):3233–3244Párraga J, Andujar SA, Rojas S, Gutierrez LJ, El Aouad N, Sanz MJ, Enriz RD, Cabedo N, Cortes D (2016) Dopaminergic isoquinolines with hexahydrocyclopenta[ij]-isoquinolines as D2−like selective ligands. Eur J Med Chem 122:27-42. https://doi.org/10.1016/j.ejmech.2016.06.009Galán A, Moreno L, Párraga J, Serrano Á, Sanz MJ, Cortes D, Cabedo N (2013) Novel isoquinoline derivatives as antimicrobial agents. Bioorg Med Chem 21(11):3221–3230. https://doi.org/10.1016/j.bmc.2013.03.042Malo M, Brive L, Luthman K, Svensson P (2010) Selective pharmacophore models of dopamine D1 and D2 full agonists based on extended pharmacophore features. ChemMedChem 5(2):232–246. https://doi.org/10.1002/cmdc.200900398Lan H, DuRand CJ, Teeter MM, Neve KA (2006) Structural determinants of pharmacological specificity between D 1 and D2 dopamine receptors. Mol Pharmacol 69(1):185–194. https://doi.org/10.1124/mol.105.017244Neve KA, Cumbay MG, Thompson KR, Yang R, Buck DC, Watts VJ, Durand CJ, Teeter MM (2001) Modeling and mutational analysis of a putative sodium-binding pocket on the dopamine D2 receptor. Mol Pharmacol 60(2):373–381Kalani MYS, Vaidehi N, Hall SE, Trabanino RJ, Freddolino PL, Kalani MA, Floriano WB, Wai Tak Kam V, Goddard Iii WA (2012) The predicted 3D structure of the human D2 dopamine receptor and the binding site and binding affinities for agonists and antagonists. Proc Natl Acad Sci USA 101:3815–3820Becker OM, Marantz Y, Shacham S, Inbal B, Heifetz A, Kalid O, Bar-Haim S, Warshaviak D, Fichman M, Noiman S (2004) G protein-coupled receptors: in silico, drug discovery in 3D. Proc Natl Acad Sci USA 101(31):11304–11309. https://doi.org/10.1073/pnas.0401862101Micheli F, Bonanomi G, Blaney FE, Braggio S, Capelli AM, Checchia A, Curcuruto O, Damiani F, Di Fabio R, Donati D, Gentile G, Gribble A, Hamprecht D, Tedesco G, Terreni S, Tarsi L, Lightfoot A, Stemp G, MacDonald G, Smith A, Pecoraro M, Petrone M, Perini O, Piner J, Rossi T, Worby A, Pilla M, Valerio E, Griffante C, Mugnaini M, Wood M, Scott C, Andreoli M, Lacroix L, Schwarz A, Gozzi A, Bifone A, Ashby Jr CR, Hagan JJ, Heidbreder C (2007) 1,2,4-Triazol-3-yl-thiopropyl-tetrahydrobenzazepines: a series of potent and selective dopamine D3 receptor antagonists. J Med Chem 50(21):5076–5089. https://doi.org/10.1021/jm0705612Párraga J, Cabedo N, Andujar S, Piqueras L, Moreno L, Galán A, Angelina E, Enriz RD, Ivorra MD, Sanz MJ, Cortes D (2013) 2,3,9- and 2,3,11-Trisubstituted tetrahydroprotoberberines as D2 dopaminergic ligands. Eur J Med Chem. 68:150-166. https://doi.org/10.1016/j.ejmech.2013.07.036Angelina E, Andujar S, Moreno L, Garibotto F, Párraga J, Peruchena N, Cabedo N, Villecco M, Cortes D, Enriz RD (2015) 3-chlorotyramine acting as ligand of the D2 dopamine receptor. Molecular modeling, synthesis and D2 receptor affinity. Molec Inform 34 (1):28-43. https://doi.org/10.1002/minf.201400093Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30(16):2785–2791Lindorff-Larsen K, Piana S, Palmo K, Maragakis P, Klepeis JL, Dror RO, Shaw DE (2010) Improved side-chain torsion potentials for the amber ff99SB protein force field. Proteins 78(8):1950–1958. https://doi.org/10.1002/prot.22711Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) Development and testing of a general amber force field. J Comput Chem 25(9):1157–1174Case DA, Darden TA, Cheatham III TE, Simmerling CL, Wang J, Duke RE, Luo R, Walker RC, Zhang W, Merz KM, Roberts B, Hayik S, Roitberg A, Seabra G, Swails J, Goetz AW, Kolossváry I, Wong KF, Paesani F, Vanicek J, Wolf RM, Liu J, Wu X, Brozell SR, Steinbrecher T, Gohlke H, Cai Q, Ye X, Wang J, Hsieh M-J, Cui G, Roe DR, Mathews DH, Seetin MG, Salomon-Ferrer R, Sagui C, Babin V, Luchko T, Gusarov S, Kovalenko A, Kollman PA (2012) AMBER12. University of California, San FranciscoRyckaert JP, Ciccotti G, Berendsen HJC (1977) Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J Comput Phys 23(3):327–341. https://doi.org/10.1016/0021-9991(77)90098-5Izaguirre JA, Catarello DP, Wozniak JM, Skeel RD (2001) Langevin stabilization of molecular dynamics. J Chem Phys 114(5):2090–2098. https://doi.org/10.1063/1.1332996Essmann U, Perera L, Berkowitz M, Darden T, Lee H, Pedersen L (1995) A smooth particle mesh Ewald method. J Chem Phys 103:8577–8593Hou T, Li N, Li Y, Wang W (2012) Characterization of domain-peptide interaction interface: prediction of SH3 domain-mediated protein-protein interaction network in yeast by generic structure-based models. J Proteome Res 11(5):2982–2995Gohlke H, Kiel C, Case DA (2003) Insights into protein-protein binding by binding free energy calculation and free energy decomposition for the Ras-Raf and Ras-RalGDS complexes. J Mol Biol 330(4):891–913Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Scalmani G, Barone V, Mennucci B, Petersson GA, Nakatsuji H, Caricato M, Li X, Hratchian HP, Izmaylov AF, Bloino J, Zheng G, Sonnenberg JL, Hada M, Ehara M, Toyota K, Fukuda R, Hasegawa J, Ishida M, Nakajima T, Honda Y, Kitao O, Nakai H, Vreven T, Montgomery JA, Jr., Peralta JE, Ogliaro F, Bearpark M, Heyd JJ, Brothers E, Kudin KN, Staroverov VN, Kobayashi R, Normand J, Raghavachari K, Rendell A, Burant JC, Iyengar SS, Tomasi J, Cossi M, Rega N, Millam JM, Klene M, Knox JE, Cross JB, Bakken V, Adamo C, Jaramillo J, Gomperts R, Stratmann RE, Yazyev O, Austin AJ, Cammi R, Pomelli C, Ochterski JW, Martin RL, Morokuma K, Zakrzewski VG, Voth GA, Salvador P, Dannenberg JJ, Dapprich S, Daniels AD, Farkas Ö, Foresman JB, Ortiz JV, Cioslowski J, Fox DJ (2009) Gaussian 09 revision D.01. Gaussian Inc, WallingfordBader RFW (1994) Atoms in molecules: a quantum theory. Clarendon, OxfordLu T, Chen F (2012) Multiwfn: a multifunctional wavefunction analyzer. J Comput Chem 33(5):580–592Case DA, Cheatham Iii TE, Darden T, Gohlke H, Luo R, Merz Jr KM, Onufriev A, Simmerling C, Wang B, Woods RJ (2005) The amber biomolecular simulation programs. J Comput Chem 26(16):1668–1688. https://doi.org/10.1002/jcc.20290Angel Guio JE, Santiago A, Rossi R, Migliore de Angel B, Barolo S, Andujar S, Hernandez V, Rosales C, Charris JE, Suarez-Roca H, Israel A, Ramirez MM, Ortega J, Cano NH, Enriz RD (2011) Synthesis and preliminary pharmacological evaluation of methoxilated indoles with possible dopaminergic central action. Lat Am J Pharm 30(10):1934Andujar SA, Tosso RD, Suvire FD, Angelina E, Peruchena N, Cabedo N, Cortes D, Enriz RD (2012) Searching the “biologically relevant” conformation of dopamine: a computational approach. J Chem Inf Model 52(1):99–112. https://doi.org/10.1021/ci2004225Sealfon SC, Chi L, Ebersole BJ, Rodic V, Zhang D, Ballesteros JA, Weinstein H (1995) Related contribution of specific helix 2 and 7 residues to conformational activation of the serotonin 5-HT2A receptor. J Biol Chem 270(28):16683–16688Trzaskowski B, Latek D, Yuan S, Ghoshdastider U, Debinski A, Filipek S (2012) Action of molecular switches in GPCRs - theoretical and experimental studies. Curr Med Chem 19(8):1090–1109. https://doi.org/10.2174/092986712799320556Tosso RD, Andujar SA, Gutierrez L, Angelina E, Rodriguez R, Nogueras M, Baldoni H, Suvire FD, Cobo J, Enriz RD (2013) Molecular modeling study of dihydrofolate reductase inhibitors. Molecular dynamics simulations, quantum mechanical calculations, and experimental corroboration. J Chem Inf Model 53(8):2018–2032Ortiz JE, Pigni NB, Andujar SA, Roitman G, Suvire FD, Enriz RD, Tapia A, Bastida J, Feresin GE (2016) Alkaloids from Hippeastrum Argentinum and their cholinesterase-inhibitory activities: an in vitro and in Silico study. J Nat Prod 79(5):1241–1248. https://doi.org/10.1021/acs.jnatprod.5b00785Luchi AM, Angelina EL, Andujar SA, Enriz RD, Peruchena NM (2016) Halogen bonding in biological context: a computational study of D2 dopamine receptor. J Phys Org Chem 29 (11):645-655. https://doi.org/10.1002/poc.358
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Not AvailableThe current study evaluated the key characters of aroma composition in diversified red wines (Cinsaut,
Grenache, Cabernet Franc, Petit Verdot, Cabernet Sauvignon, Nielluccio, Tempranillo, Syrah, Merlot and Caladoc). Out of hundreds of volatile compounds 64 compounds were considered for study. Different groups
consisting of fatty acids, volatile alcohols, aldehydes, esters, volatile phenols and terpenes were analysed using gas chromatography mass spectrometry coupled with thermal desorption (TD–GC–MS). Among all these diversified classes, alcohols were found as the most dominant group followed by esters and acids whereas aldehydes, phenols and terpenes were found to be minor compounds. Among the varieties, Nielluccio wine recorded highest concentration of total volatile compounds (191.53 mg/L) while, it was least in Caladoc wines (15.45 mg/L). The principal component analysis clearly differentiated Grenache wines based on their relationships between scores and their aroma composition followed by Nielluccio and Cinsuat wines. Out of sixty four compounds, only six aromatic compounds viz. butanediol, isoamyl actate, c-Terpene, butanol, acetic acid and furfural have satisfying aroma descriptors with floral and fruity nuances and contribute to differentiate the Grenache wines from other varieties which have similar scores in PC1 analysis. The cluster analysis also suggested that the wines in the same group (Cinsaut, Tempranillo and Syrah), (Cabernet Franc, Cabernet Sauvignon, Caladoc and Merlot) and (Nielluccio and Petit Verdot) had similar aroma characterization. Grenache wines were well differentiated from the sub group formed by other red varieties.Not Availabl
Volatile composition and sensory profile of wines obtained from partially defoliated vines: the case of Nero di Troia wine
The effects of defoliation treatments performed in the bunch-zone on volatile composition and sensory attributes of the corresponding wines were evaluated. Nero di Troia grapes were subjected to four different treatments: no leaf removal (N); leaf removal in the fruit-zone along the east side (at complete veraison) (E); leaf removal in the fruit-zone along the east and west side (at complete veraison) (E/W); almost complete leaf removal along the west side (at complete veraison) and at pre-harvest also along the east side (F). For each defoliation thesis, half of the wine was treated with oak chips in order to verify whether the treatments with oak chips can mask the effects of defoliation. Defoliation partially affected the volatile profiles. Data concerning the volatile profiles show that the highest concentrations of total acids were detected in N and E wines, while those of the total ethyl esters were detected in F wines, and the lowest terpenes concentrations were found in E wines. The oak-treated wines show the highest contents of 1-heptanol, 1-octanol, many ethyl esters, and total hydrocarbons. They were the only in which the whisky lactone was detected. From a sensory point of view, the wines from almost completely defoliated grapes exhibited the lowest scores of gustatory-olfactory intensity, persistence, and quality. The wines that were not treated with chips exhibited sensory profiles characterized by floral and fruity notes, while those treated with oak chips showed sensory profiles characterized by spicy and fruity notes