Review
Copyright ©2009 The WJG Press and Baishideng. All rights reserved.
World J Gastroenterol. Feb 7, 2009; 15(5): 531-537
Published online Feb 7, 2009. doi: 10.3748/wjg.15.531
Bio-mathematical models of viral dynamics to tailor antiviral therapy in chronic viral hepatitis
Maurizia Rossana Brunetto, Piero Colombatto, Ferruccio Bonino
Maurizia Rossana Brunetto, Piero Colombatto, UO Epatologia, Azienda Ospedaliero-Universitaria Pisana, 56100 Pisa, Italy
Ferruccio Bonino, Direzione Scientifica, Fondazione IRCCS Ospedale Maggiore Policlinico Mangiagalli and Regina Elena di Milano, 20122 Milano, Italy
Author contributions: Brunetto MR, Colombatto P and Bonino F contributed equally to the work.
Correspondence to: Maurizia Rossana Brunetto, UO Epatologia, Azienda Ospedaliero-Universitaria Pisana, 56100 Pisa, Italy. brunetto@med-club.com
Telephone: +39-335-7115067
Fax: +39-050-995457
Received: May 5, 2008
Revised: August 3, 2008
Accepted: August 10, 2008
Published online: February 7, 2009
Abstract

The simulation of the dynamics of viral infections by mathematical equations has been applied successfully to the study of viral infections during antiviral therapy. Standard models applied to viral hepatitis describe the viral load decline in the first 2-4 wk of antiviral therapy, but do not adequately simulate the dynamics of viral infection for the following period. The hypothesis of a constant clearance rate of the infected cells provides an unrealistic estimation of the time necessary to reach the control or the clearance of hepatitis B virus (HBV)/hepatitis C virus (HCV) infection. To overcome the problem, we have developed a new multiphasic model in which the immune system activity is modulated by a negative feedback caused by the infected cells reduction, and alanine aminotransferase kinetics serve as a surrogate marker of infected-cell clearance. By this approach, we can compute the dynamics of infected cells during the whole treatment course, and find a good correlation between the number of infected cells at the end of therapy and the long-term virological response in patients with chronic hepatitis C. The new model successfully describes the HBV infection dynamics far beyond the third month of antiviral therapy under the assumption that the sum of infected and non-infected cells remains roughly constant during therapy, and both target and infected cells concur in the hepatocyte turnover. In clinical practice, these new models will allow the development of simulators of treatment response that will be used as an “automatic pilot” for tailoring antiviral therapy in chronic hepatitis B as well as chronic hepatitis C patients.

Keywords: Viral hepatitis, Bio-mathematical models, Hepatitis B virus, Hepatitis C virus, Viral dynamics