Host responses are critical in the control and ultimate elimination of most viruses. Some viral agents, however, are not cleared and persist for years in an asymptomatic yet immunologically dynamic state. Two such examples of chronic viral infections are those caused by the Human Immunodeficiency...
Host responses are critical in the control and ultimate elimination of most viruses. Some viral agents, however, are not cleared and persist for years in an asymptomatic yet immunologically dynamic state. Two such examples of chronic viral infections are those caused by the Human Immunodeficiency Virus (HIV) and by Hepatitis (HCV). The long-term consequences in both conditions lead to serious disease manifestations such as AIDS, cirrhosis, cancer and, ultimately, death. In both these viral conditions, we have an interest in developing novel immune-based therapies and in evaluating immune surrogate markers of disease progression and response to antiviral therapies. Furthermore, we have examined markers of both phenotypic and functional immunity at our centre in our well-characterized natural history cohorts and in antiviral and vaccine clinical trials that we have conducted to evaluate the predictive value of these surrogate markers. Current measures of disease activity in peripheral blood that are based on viral measurements are of limited value, while disease activity in tissues such as lymphoid tissue or the liver must rely on invasive and potentially dangerous procedures. Our research is focused thus on identifying specific immune response phenotypes, the ultimate aim being in applying this approach to clinical management. Decisions on starting or stopping therapies can in this way be made more appropriately and precisely. Our second area of interest and activity is in the field of clinical information. We have developed sophisticated approaches that utilize artificial intelligence and integrate computational tools in clinical practice. We believe that the application of powerful advanced analytic tools such as neural networks, fuzzy logic and genetic algorithms will lead to improved treatments and patient management.