Hepatoprotective effects of Met-enkephalin on acetaminophen-induced liver lesions in male CBA mice

Recent histopathological investigations in patients with hepatitis suggested possible involvement of Met-enkephalin and its receptors in the pathophysiology of hepatitis. Consequently, we evaluated the potential hepatoprotective effects of this endogenous opioid pentapeptide in the experimental model of acetaminophen induced hepatotoxicity in male CBA mice. Met-enkephalin exhibited strong hepatoprotective effects in a dose of 7.5 mg/kg, which corresponds to the protective dose reported for several different animal disease models. In this group plasma alanine aminotransferase and aspartate aminotransferase enzyme activities, as well as liver necrosis score were significantly reduced in comparison to control animals treated with physiological saline (p>0.01). The specificity of the peptide hepatoprotection was investigated from the standpoint of the receptor and peptide blockade. It was concluded that Met-enkephalin effects on the liver were mediated via δ and ζ opioid receptors. Genotoxic testing of Met-enkephalin confirmed the safety of the peptide.

A simple three-step method for design and affinity testing of new antisense peptides: an example of erythropoietin

Antisense peptide technology is a valuable tool for deriving new biologically active molecules and performing peptide-receptor modulation. It is based on the fact that peptides specified by the complementary (antisense) nucleotide sequences often bind to each other with a higher specificity and efficacy. We tested the validity of this concept on the example of human erythropoietin, a well-characterized and pharmacologically relevant hematopoietic growth factor. The purpose of the work was to present and test simple and efficient three-step procedure for the design of an antisense peptide targeting receptor-binding site of human erythropoietin. Firstly, we selected the carboxyl-terminal receptor binding region of the molecule (epitope) as a template for the antisense peptide modeling; Secondly, we designed an antisense peptide using mRNA transcription of the epitope sequence in the 3'→5' direction and computational screening of potential paratope structures with BLAST; Thirdly, we evaluated sense-antisense (epitope-paratope) peptide binding and affinity by means of fluorescence spectroscopy and microscale thermophoresis. Both methods showed similar Kd values of 850 and 816 µM, respectively. The advantages of the methods were: fast screening with a small quantity of the sample needed, and measurements done within the range of physicochemical parameters resembling physiological conditions. Antisense peptides targeting specific erythropoietin region(s) could be used for the development of new immunochemical methods. Selected antisense peptides with optimal affinity are potential lead compounds for the development of novel diagnostic substances, biopharmaceuticals and vaccines.

Hla-Cw7 allele as predictor of favorable therapeutic response to interferon-alpha in patients with chronic hepatitis C

AIM

To evaluate the association between human leukocyte antigens (HLA) class I and therapeutic response to interferon-alpha in Croatian patients with chronic hepatitis C.

METHODS

HLA-A, -B, and -C genotyping was performed in 55 patients with sustained virological response and in 57 patients without sustained virological response to interferon-alpha therapy. Patients were treated in the period from 1998-2001 with interferon-alpha at a dose of 3 million units three times a week. Patients who became negative for hepatitis C virus RNA after 12 weeks of therapy completed 48 weeks of therapy.

RESULTS

There was no association between therapeutic outcome and frequency of HLA-A, as well as of HLA-B alleles. HLA-Cw7 was significantly more frequent in patients with than those without sustained virological response (27.0% vs 6.7%; P=0.011).

CONCLUSION

In Croatian patients with chronic hepatitis C, HLA-Cw7 is the predictor of sustained virological response to interferon-alpha therapy.