Hepatic drug metabolizing profile of Flinders Sensitive Line rat model of depression
ABSTRACT The Flinders Sensitive Line (FSL) rat model of depression exhibits some behavioral, neurochemical, and pharmacological features that have been reported in depressed patients and has been very effective in screening antidepressants. Major factor that determines the effectiveness and toxicity of a drug is the drug metabolizing capacity of the liver. Therefore, in order to discriminate possible differentiation in the hepatic drug metabolism between FSL rats and Sprague-Dawley (SD) controls, their hepatic metabolic profile was investigated in this study. The data showed decreased glutathione (GSH) content and glutathione S-transferase (GST) activity and lower expression of certain major CYP enzymes, including the CYP2B1, CYP2C11 and CYP2D1 in FSL rats compared to SD controls. In contrast, p-nitrophenol hydroxylase (PNP), 7-ethoxyresorufin-O-dealkylase (EROD) and 16alpha-testosterone hydroxylase activities were higher in FSL rats. Interestingly, the wide spread environmental pollutant benzo(alpha)pyrene (B(alpha)P) induced CYP1A1, CYP1A2, CYP2B1/2 and ALDH3c at a lesser extend in FSL than in SD rats, whereas the antidepressant mirtazapine (MIRT) up-regulated CYP1A1/2, CYP2C11, CYP2D1, CYP2E1 and CYP3A1/2, mainly, in FSL rats. The drug also further increased ALDH3c whereas suppressed GSH content in B(alpha)P-exposed FSL rats. In conclusion, several key enzymes of the hepatic biotransformation machinery are differentially expressed in FSL than in SD rats, a condition that may influence the outcome of drug therapy. The MIRT-induced up-regulation of several drug-metabolizing enzymes indicates the critical role of antidepressant treatment that should be always taken into account in the designing of treatment and interpretation of insufficient pharmacotherapy or drug toxicity.
Conference Paper: Comparison of cooperative R&D arrangements among competitive firms[Show abstract] [Hide abstract]
ABSTRACT: There has been a lot of research in the field of cooperative R&D and a lot has asserted that `cartelization' dominates all other forms of cooperative R&D. But cartelization can harm both the competition and the consumer by facilitating collusion among the participating firms. This may be the reason why the suggestions of previous research could not be readily implemented in a real world. The authors' model is built on the R&D classification of Kamien et al. (1992); R&D competition, R&D cartelization, RJV competition, and RJV cartelization, but with the consideration of input sharing in addition to output sharing. Furthermore, to fully explore the effect of competition they use a conjectural variation model instead of a conventional Cournot or Bertrand model. The focus of the analysis is to explore which type yields the best outcome when degree of competition, spillover rate, and input/output sharing rate are simultaneously considered. They show that unlike Kamien et al. (1992), a dominant type of R&D arrangement is not uniquely obtained, rather heavily depends on the parameters (degree of competition, spillover rate, and input/output sharing rate). RJV-competition that have the least desirable outcome in Kamien et al. (1992) may have the best outcome in an appropriate range of input sharing. Also it is shown that R&D-competition dominates all the other R&D arrangements under a low rate of spillover and high competitionEngineering and Technology Management, 1996. IEMC 96. Proceedings., International Conference on; 09/1996
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ABSTRACT: The aim of the present study was to ascertain whether the noradrenergic or serotonergic systems may affect the expression of liver cytochrome P450 (CYP). Rats were injected intraperitoneally with N-(2-chloroethyl)-N-ethyl-2-bromobenzylamine (DSP-4, a noradrenergic neurotoxin) or p-chloroamphetamine (PCA, a serotonergic neurotoxin) or p-chlorophenylalanine (PCPA, an inhibitor of serotonin synthesis). One week after neurotoxin injection the levels of neurotransmitters (noradrenaline, dopamine, serotonin) and their metabolites were measured in brain structures, and the activity and protein levels of CYP isoforms were measured in the liver. In the brain, DSP-4 or PCA and PCPA selectively decreased noradrenaline or serotonin levels, respectively. In the liver, the applied neurotoxins evoked decrease in the activity of CYP2B, CYP2C11 and CYP3A (DSP-4, PCA, PCPA) and increase in the activity of CYP1A (PCA, PCPA), while the activity of CYP2A, CYP2C6 and CYP2D was not affected by the applied neurotoxins. Since the affected isoforms (CYP1A/2B/2C11/3A) are regulated by endogenous hormones (growth hormone, corticosterone, thyroid hormones), the latter being under control of the central nervous system, it is postulated that the brain noradrenergic and serotonergic systems are involved in the physiological regulation of liver CYP expression.Pharmacological Research 06/2011; 64(4):371-80. DOI:10.1016/j.phrs.2011.06.020 · 3.98 Impact Factor
Article: Modeling depression in animal models[Show abstract] [Hide abstract]
ABSTRACT: Animal models and preclinical tests have played large roles in the development of antidepressant drugs and are likely to continue to play important roles. In the present communication, the main animal models of depression have been described and reviewed. These models include the Flinders sensitive line (FSL) rat, the Wistar Kyoto (WKY) rat, the fawn-hooded (FH) rat, and the learned helpless (LH) rat. In addition, the materials used to assess the behavior of these rats, including swim tanks, drinking tubes, and an open field apparatus, have been discussed. Finally, the methods used in collecting the relevant behaviors in the animal models are described. These include the procedures used in the forced swim test and chronic mild stress protocols, including the sucrose preference test. It is concluded that the behavioral tests used to infer depressed-like behavior in rats will continue to provide useful data if the appropriate animals and proper methods are used.Methods in molecular biology (Clifton, N.J.) 01/2012; 829:125-44. DOI:10.1007/978-1-61779-458-2_7 · 1.29 Impact Factor