229
Views
49
CrossRef citations to date
0
Altmetric
Review

Cannabinoid receptors as new targets of antifibrosing strategies during chronic liver diseases

, , &
Pages 403-409 | Published online: 14 Feb 2007
 

Abstract

Chronic liver injury exposes the patient to liver fibrosis and its end stage, cirrhosis, is a major public health problem worldwide. In western countries, prevailing causes of cirrhosis include chronic alcohol consumption, hepatitis C virus infection and non-alcoholic steatohepatitis. Current treatment of hepatic fibrosis is limited to withdrawal of the noxious agent. Nevertheless, suppression of the cause of hepatic injury is not always feasible and numerous efforts are directed at the development of liver-specific antifibrotic therapies. Along these lines, the authors recently demonstrated that the endocannabinoid system shows promise as a novel target for antifibrotic therapy during chronic liver injury. Indeed, cannabinoid receptors CB1 and CB2 promote dual pro- and antifibrogenic effects, respectively. Therefore, endocannabinoid-based therapies, combining CB2 agonists and CB1 antagonists may open novel therapeutic perspectives for the treatment of chronic liver diseases.

Acknowledgements

This work was supported by the INSERM, the Université Paris-Val-de-Marne, and by grants (to S Lotersztajn) of the Agence Nationale de la Recherche, the Association pour la Recherche sur le Cancer and Sanofi-Aventis.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 99.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 1,049.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.