Modeling the effect of HIV coinfection on clearance and sustained virologic response during treatment for hepatitis C virus.
BACKGROUND
HIV/hepatitis C (HCV) coinfection is a major concern in global health today. Each pathogen can exacerbate the effects of the other and affect treatment outcomes. Understanding the within-host dynamics of these coinfecting pathogens is crucial, particularly in light of new, direct-acting antiviral agents (DAAs) for HCV treatment that are becoming available.


RESULTS
In this study, we construct a within-host mathematical model of HCV/HIV coinfection by adapting a previously published model of HCV monoinfection to include an immune system component in infection clearance. We explore the effect of HIV-coinfection on spontaneous HCV clearance and sustained virologic response (SVR) by building in decreased immune function with increased HIV viral load. Treatment is modeled by modifying HCV burst-size, and we use clinically-relevant parameter estimates. Our model replicates real-world patient outcomes; it outputs infected and uninfected target cell counts, and HCV viral load for varying treatment and coinfection scenarios. Increased HIV viral load and reduced CD4(+) count correlate with decreased spontaneous clearance and SVR chances. Treatment efficacy/duration combinations resulting in SVR are calculated for HIV-positive and negative patients, and crucially, we replicate the new findings that highly efficacious DAAs reduce treatment differences between HIV-positive and negative patients. However, we also find that if drug efficacy decays sufficiently over treatment course, SVR differences between HIV-positive and negative patients reappear.


CONCLUSIONS
Our model shows theoretical evidence of the differing outcomes of HCV infection in cases where the immune system is compromised by HIV. Understanding what controls these outcomes is especially important with the advent of efficacious but often prohibitively expensive DAAs. Using a model to predict patient response can lend insight into optimal treatment design, both in helping to identify patients who might respond well to treatment and in helping to identify treatment pathways and pitfalls.
Department of Ecology and Evolutionary Biology, Princeton University, 106a Guyot Hall, Princeton, NJ 08544, USA. Electronic address: rbirger@princeton.edu.
Ruthie
R
Birger
Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zürich, University of Zürich, Rämistr. 100, CH-8091 Zürich, Switzerland; Institute of Medical Virology, University of Zürich, Zürich, Switzerland.
Roger
R
Kouyos
Department of Biology, McMaster University, 1280 Main St. West, Hamilton, Ontario L8S 4K1, Canada.
Jonathan
J
Dushoff
Department of Ecology and Evolutionary Biology, Princeton University, 106a Guyot Hall, Princeton, NJ 08544, USA; Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA.
Bryan
B
Grenfell
D015658:HIV Infections / Q000150:complications* / Q000188:drug therapy; D006526:Hepatitis C / Q000150:complications / Q000188:drug therapy / Q000276:immunology / Q000821:virology*; D006801:Humans; D008962:Models, Theoretical
Antiviral therapy; Coinfection; HCV–HIV; Immune response; Mathematical model