Modeling HCV dynamics in clinical practice to personalize antiviral therapy

Abstract

Modeling by mathematical equations the dynamics of hepatitis C virus (HCV) infection has been helpful to uncover the mechanisms of viral persistence and to study the mode of action of different antiviral therapies. Standard biphasic models well describe the viral load decline in the first 2-4 weeks of therapy, however the hypothesis of a constant clearance rate of the infected cells during longer treatment periods provides unrealistic estimates of the chance to reach a sustained virological response (SVR). For this reason, we have developed a multiphasic model in which the analysis of alanine aminotransferase (ALT) kinetics during the first 4 weeks of therapy allows for better assessment of the infected-cell clearance rate, which is feedback regulated to the reduction of the infected cell number during the following treatment. By this approach, we found a very high correlation between the residual number of infected cells at the end of therapy and its outcome, regardless to HCV genotype, IL28B polymorphism and characteristics of the liver disease. This modeling approach was successfully applied in a pilot study to tailor the duration of peg-interferon and ribavirin therapy, as well as to make accurate predictions of SVR at the individual level in HCV genotype 1 patients receiving P/R lead-in before adding first generation protease inhibitors. The unique experience generated by the application of our bio-mathematical model in clinical practice, suggests that deterministic models of viral dynamics can be used as simulators of treatment response to help physicians in personalizing treatment management. This is especially valuable when we have to take into account the costs of the different schedules and their actual efficacy in patients with clinical features more complex than those of the drug registration trials.