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      Xyloketal B, a marine compound, acts on a network of molecular proteins and regulates the activity and expression of rat cytochrome P450 3a: a bioinformatic and animal study

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          Abstract

          Natural compounds are becoming popular for the treatment of illnesses and health promotion, but the mechanisms of action and safety profiles are often unknown. Xyloketal B (XKB) is a novel marine compound isolated from the mangrove fungus Xylaria sp., with potent antioxidative, neuroprotective, and cardioprotective effects. However, its molecular targets and effects on drug-metabolizing enzymes are unknown. This study aimed to investigate the potential molecular targets of XKB using bioinformatic approaches and to examine the effect of XKB on the expression and activity of rat cytochrome P450 3a (Cyp3a) subfamily members using midazolam as a model probe. DDI-CPI, a server that can predict drug–drug interactions via the chemical–protein interactome, was employed to predict the targets of XKB, and the Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to analyze the pathways of the predicted targets of XKB. Homology modeling was performed using the Discovery Studio program 3.1. The activity and expression of rat hepatic Cyp3a were examined after the rats were treated with XKB at 7 and 14 mg/kg for 8 consecutive days. Rat plasma concentrations of midazolam and its metabolite 1′-OH-midazolam were determined using a validated high-performance liquid chromatographic method. Bioinformatic analysis showed that there were over 324 functional proteins and 61 related signaling pathways that were potentially regulated by XKB. A molecular docking study showed that XKB bound to the active site of human cytochrome P450 3A4 and rat Cyp3a2 homology model via the formation of hydrogen bonds. The in vivo study showed that oral administration of XKB at 14 mg/kg to rats for 8 days significantly increased the area under the plasma concentration-time curve (AUC) of midazolam, with a concomitant decrease in the plasma clearance and AUC ratio of 1′-OH-midazolam over midazolam. Further, oral administration of 14 mg/kg XKB for 8 days markedly reduced the activity and expression of hepatic Cyp3a in rats. Taken together, the results show that XKB could regulate networks of molecular proteins and related signaling pathways and that XKB downregulated hepatic Cyp3a in rats. XKB might cause drug interactions through modulation of the activity and expression of Cyp3a members. More studies are warranted to confirm the mechanisms of action of XKB and to investigate the underlying mechanism for the regulating effect of XKB on Cyp3a subfamily members.

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          Most cited references 49

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          PKSolver: An add-in program for pharmacokinetic and pharmacodynamic data analysis in Microsoft Excel.

          This study presents PKSolver, a freely available menu-driven add-in program for Microsoft Excel written in Visual Basic for Applications (VBA), for solving basic problems in pharmacokinetic (PK) and pharmacodynamic (PD) data analysis. The program provides a range of modules for PK and PD analysis including noncompartmental analysis (NCA), compartmental analysis (CA), and pharmacodynamic modeling. Two special built-in modules, multiple absorption sites (MAS) and enterohepatic circulation (EHC), were developed for fitting the double-peak concentration-time profile based on the classical one-compartment model. In addition, twenty frequently used pharmacokinetic functions were encoded as a macro and can be directly accessed in an Excel spreadsheet. To evaluate the program, a detailed comparison of modeling PK data using PKSolver and professional PK/PD software package WinNonlin and Scientist was performed. The results showed that the parameters estimated with PKSolver were satisfactory. In conclusion, the PKSolver simplified the PK and PD data analysis process and its output could be generated in Microsoft Word in the form of an integrated report. The program provides pharmacokinetic researchers with a fast and easy-to-use tool for routine and basic PK and PD data analysis with a more user-friendly interface. Copyright 2010 Elsevier Ireland Ltd. All rights reserved.
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            Gene regulation by transcription factors and microRNAs.

             Oliver Hobert (2008)
            The properties of a cell are determined by the genetic information encoded in its genome. Understanding how such information is differentially and dynamically retrieved to define distinct cell types and cellular states is a major challenge facing molecular biology. Gene regulatory factors that control the expression of genomic information come in a variety of flavors, with transcription factors and microRNAs representing the most numerous gene regulatory factors in multicellular genomes. Here, I review common principles of transcription factor- and microRNA-mediated gene regulatory events and discuss conceptual differences in how these factors control gene expression.
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              Global status report on noncommunicable diseases 2010

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                Author and article information

                Journal
                Drug Des Devel Ther
                Drug Des Devel Ther
                Drug Design, Development and Therapy
                Drug Design, Development and Therapy
                Dove Medical Press
                1177-8881
                2014
                12 December 2014
                : 8
                : 2555-2602
                Affiliations
                [1 ]Institute of Biomedicine and Guangdong Provincial Key Laboratory of Bioengineering Medicine, Jinan University, Guangzhou, People’s Republic of China
                [2 ]Department of Pharmacy, Jinan University, Guangzhou, People’s Republic of China
                [3 ]The People’s Hospital of Shenzhen City, Shenzhen, People’s Republic of China
                [4 ]Department of Pharmaceutical Science, College of Pharmacy, University of South Florida, Tampa, FL, USA
                [5 ]School of Chemistry and Chemical Engineering, Sun Yat-Sen University, Guangzhou, People’s Republic of China
                [6 ]Bio-X Institutes, Key Laboratory for the Genetics of Development and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, People’s Republic of China
                [7 ]Guizhou Provincial Key Laboratory for Regenerative Medicine, Stem Cell and Tissue Engineering Research Center and Sino-US Joint Laboratory for Medical Sciences, Guiyang Medical University, Guiyang, People’s Republic of China
                Author notes
                Correspondence: Qi Xiang, Institute of Biomedicine and Guangdong Provincial Key Laboratory of Bioengineering Medicine, Jinan University, Guangzhou 510632, Guangdong, People’s Republic of China, Tel +86 20 8556 3234, Fax +86 20 8556 5109, Email txiangqi@ 123456jnu.edu.cn
                Shu-Feng Zhou, Department of Pharmaceutical Science, College of Pharmacy, University of South Florida, 12901 Bruce B. Downs Boulevard, Tampa, FL 33612, USA, Tel +1 813 974 6276, Fax +1 813 905 9885, Email szhou@ 123456health.usf.edu

                *These two authors contributed equally to this work

                Article
                dddt-8-2555
                10.2147/DDDT.S73476
                4271727
                © 2014 Su et al. This work is published by Dove Medical Press Limited, and licensed under Creative Commons Attribution – Non Commercial (unported, v3.0) License

                The full terms of the License are available at http://creativecommons.org/licenses/by-nc/3.0/. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed.

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                Original Research

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