In Vitro Metabolic Characterization, Phenotyping, and Kinetic Studies of 9cUAB30, a Retinoid X Receptor-Specific Retinoid
Department of Chemistry, University of Alabama at Birmingham, Birmingham, Alabama, United States Drug Metabolism and Disposition
(Impact Factor: 3.25).
08/2007; 35(7):1157-64. DOI: 10.1124/dmd.106.013938
The present study was conducted to compare the in vitro phase I and phase II metabolic profiles of (2E,4E,6Z,8E)-8-(3',4'-dihydro-1'(2'H)-naphthalen-1'-ylidene)-3,7-dimethyl-2,4,6-octatrienoic acid (9cUAB30) in human, rat, and dog microsomes and to characterize and identify the associated metabolic kinetics and specific isozymes from human liver microsomes (HLM) responsible for metabolism, respectively. Data from these experiments revealed that nine (M1-M9) phase I metabolites along with a single glucuronide conjugate were observed across the species investigated. With the exception of glucuronidation, no evidence of metabolism was detected for phase II enzymes (data not shown). Significant differences between species with regard to metabolic profile, stability, and gender were noted. For the eight phase I metabolites detected in HLM, the specific isozymes responsible for the biotransformations were CYP2C8, CYP2C9, and CYP2C19, with minor contributions from CYP1A2 and CYP2B6. For the glucuronide conjugate, UGT1A9 was the major catalyzing enzyme, with a minor contribution from UGT1A3. Kinetic analysis of eight of the detected metabolites indicated that four seemed to follow classical hyperbolic kinetics, whereas the remaining four showed evidence of either autoactivation or substrate inhibition.
Available from: Clinton Grubbs
- "We recently examined a series of RXR agonists employing gene expression arrays (Vedell et al. 2013). The agonists examined were Targretin (TRG) (the only clinically employed RXR agonist) (Farol and Hymes 2004; Rigas and Dragnev 2005; Gniadecki et al. 2007; Lansigan and Foss 2010) and two RXR-selective agonists, UAB30 and 4-Me-UAB30, (Muccio et al. 1998; Atigadda et al. 2003; Grubbs et al. 2006; Gorman et al. 2007; Kolesar et al. 2010). Recently the 3D structures of RXR homodimers were determined containing either TRG or UAB30 and a coactivator peptide (Boerma et al. 2014). "
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ABSTRACT: The ability of the retinoid X receptors (RXRs) specific agonists (targretin [TRG] and UAB30) to alter rat liver gene and protein expression was determined using Affymetrix Exon arrays and high-performance liquid chromatography – tandem mass spectrometry (LC-MS/MS). TRG profoundly increases triglycerides levels while UAB30 does not. The expression patterns of transcripts or proteins from rat liver treated with TRG or UAB-30 were different from controls and each other. There were six times more gene transcripts identified than proteins. Differentially expressed RNAs or proteins were mapped into known gene ontology (GO) categories and GeneGo Metacore (KEGG) pathway maps. The GO categories which were highly overrepresented with differentially expressed RNAs (P < 10−16) were also overrepresented at the protein level. This high concordance of GO Terms was achieved despite the fact that typically ≤1/3 of the elements identified by gene expression were identified by proteomics. Within these GO categories, the magnitude of alterations induced by RXR agonists at the transcript and protein levels were correlated. When GO categories with moderate overrepresentation (10−5 < P < 10−9) were examined, there was greater discordance between the transcript and protein data. Examination of KEGG pathway maps with highly significant changes at both the protein and the RNA levels showed that the individual proteins/genes altered were often the same and changes were of similar magnitude; while KEGG pathways showed limited statistical significance and exhibited minimal overlap. Finally, metabolomics analysis of liver and serum identified altered expression of metabolites related to fatty acid oxidation and bile acid metabolism that were consistent with transcript/protein changes. We observed significant concordance between genomics and proteomics implying either can identify pathways modulated and can indirectly predict resulting physiologic effects.
12/2014; 2(6). DOI:10.1002/prp2.74
Available from: Jiaur R Gayen
- "Enzyme kinetic parameters for S002-333 (or enantiomers) in this study were predicted in terms of K m (mM) and relative V max as a peak area ratio of metabolite versus internal standard due to unavailability of metabolite standards (Gorman et al., 2007). The kinetic studies were performed using HLM and cDNA-expressed CYP2C9, 2C19 and 3A4. "
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ABSTRACT: Abstract 1. S002-333, (2-(4'-methoxy-benzenesulfonyl)-2,3,4,9-tetrahydro-1H-pyrido (3,4-b) indole-3-carboxylic acid amide) is a novel potent antithrombotic molecule currently under development phase. It is the racemic mixture of two enantiomers, namely S004-1032 (R-form) and S007-1558 (S-form). 2. The contribution of five major isoenzymes, namely CYP2B6, 2C9, 2C19, 2D6 and 3A4 was quantified using recombinant P450s in the phase-I metabolism through relative activity factor approach. CYP2C19 was found to be the major contributor for S002-333 and S007-1558, while CYP3A4 showed greater involvement in S004-1032 metabolism. Chemical inhibition and immunoinhibition studies reconfirmed the results in human liver microsomes (HLM). 3. Four major phase-I metabolites of S002-333; M-1 and M-3 (oxidative), M-2 (O-demethylated) and M-4 (dehydrogenated) were characterized in HLM. These metabolites constituted 11.2, 11.3 and 21.5% of the parent in comparison with the net phase-I metabolism of 29.9, 31.4 and 38.3% of S002-333, S004-1032 and S007-1558, respectively. 4. Among CYP2C9, 2C19 and 3A4, the relative contribution of CYP2C9 was found to be maximum during M-1 through M-4 formation. Enzyme kinetic analysis for detected metabolites indicated that M-1 to M-3 followed classical hyperbolic kinetics, whereas M-4 showed evidence of autoactivation. In conclusion, the results suggest prominent role of CYP2C9, 2C19 and 3A4 isoforms for enantioselective disposition of S002-333 in vitro.
Xenobiotica 08/2013; 44(4). DOI:10.3109/00498254.2013.831958 · 2.20 Impact Factor
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ABSTRACT: Macrophages are central to the initiation and progression of atherosclerosis and thus can be very appropriate targets for therapy. Cell adhesion molecules mediating monocytes recruitment to the endothelium are attractive therapy targets and their inhibitors are in clinical trials. Macrophage scavenger receptors like SR-A and CD-36 mediate foam cell formation by facilitating the uptake of modified lipids. Peroxisome proliferator-activated receptors (PPAR), liver X receptor (LXR)-mediated signaling, mitogen-activated protein kinase (MAPK) induced phosphorylation events seem to play an important role in this phenomenon. Proteins affecting macrophage cholesterol metabolism and transport, including ATP-binding cassette (ABC) A1, ABCG1, acyl-CoA:cholesterol acyltransferase (ACAT), apolipoprotein A-1 (ApoA-1), neutral cholesteryl ester hydrolase (NCEH) also regulate foam cell formation and are being developed as therapeutic targets by many pharmaceutical companies. Macrophage proliferation and apoptosis are important events controlling inflammatory response, plaque vulnerability, and destabilization. Free cholesterol (FC) activates the macrophage endoplasmic reticulum (ER) stress pathway and apoptosis. Free radicals and nitric oxide also modulate macrophage foam cell formation and apoptosis. Various anti-oxidants like AGI-1067 and BO-653 are in clinical trials for atherosclerosis treatment. Macrophage matrix metalloproteinase's (MMP's) play a significant role in weakening and rupture of plaques. Efforts are on to develop isoform specific MMP inhibitor. CD-14, MMP-3, ABCA1, Toll-like receptor-4 (TLR-4), lectin-like oxidized low-density lipoprotein receptor-1 (LOX-1), arachidonate lipoxygenase-15 (ALOX-15), and Connexin37 polymorphisms and macrophage dysfunction signify their importance in atherosclerosis. Deciphering the role of macrophages in regulating dyslipidemia and inflammation during atherosclerosis is important for developing them as therapeutic targets. ß
Medicinal Research Reviews 01/2007; 28:483-544. · 8.43 Impact Factor
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