Nonlinear mixed effects modelling viral load in untreated patients with chronic hepatitis C

It is well known that viral load of the hepatitis C virus (HCV) is related to the efficacy of interferon therapy. We have previously observed that viral load can fluctuate within an untreated patient population. The complex biological parameters that impact on viral load are essentially unknown. No mathematical model exists to describe HCV viral load dynamics in untreated patients. We carried out an empirical modelling to investigate whether different fluctuation patterns exist and how these patterns (if exist) are related to host-specific factors. Data was collected from 147 untreated patients chronically infected with hepatitis C, each contributing between 2 to 10 years of measurements. We propose to use a three parameter logistic model to describe the overall pattern of viral load fluctuation based on an exploratory analysis of the data. To incorporate the correlation feature of longitudinal data and patient to patient variation we introduced random effects components into the model. On the base of this nonlinear mixed effects modelling, we investigated effects of host-specific factors on viral load fluctuation by incorporating covariates into the model. The proposed model provided a good fit for describing fluctuations of viral load measured with varying frequency over different time intervals. The average viral load growth time was significantly different between infection sources. There was a large patient to patient variation in viral load asymptote