Systems-level modelling of hepatitis C virus infection and treatment response

Hepatitis C virus (HCV) infects about 130-170 million people worldwide. Interferon-based treatment, which works by enhancing the immune response against HCV, fails to cure a significant fraction of HCV patients treated. The cause of this treatment failure has remained poorly understood. Newer potent drugs called direct-acting antivirals (DAAs), which directly target specific HCV proteins and block viral production, cure almost all treated patients. Tremendous efforts are now ongoing to optimise DAA-based treatment dosage, duration and cost. Unexpectedly, DAAs appear to work better in treatment-naive individuals than in individuals who previously did not respond to interferon-based treatment, suggesting a potential role for individual’s immune responsiveness in optimising current DAA-based treatments. In this talk, I will describe a multiscale mathematical model which presents a conceptual understanding of Interferon-based treatment failure, identifies a mechanistic, causal link between individual’s immune responsiveness and DAA-based treatment outcome, and suggests strategies to optimise DAA-based therapies exploiting this causal relationship. I will also discuss our modelling efforts to guide drug and vaccine intervention strategies aimed at blocking HCV entry into target cells. This work is done in collaboration with Narendra M. Dixit and colleagues at the Indian Institute of Science, India.