Research Summary

Computational design of drugs, vaccines and optimal intervention strategies against rapidly evolving pathogens

Pathogens like HIV evolve rapidly and escape pressure from drugs and host immune responses, causing persistent infection and associated morbidity and mortality. Such pathogens present moving targets, which current drugs and vaccines, designed based on static structures of target proteins, tend to miss. Specific mutations on the viral genome confer resistance to specific drugs. Resistance mutations for all the available anti-HIV drugs have been identified. Which drug combination is least likely to fail due to resistance? Answering this question experimentally is difficult given the large number of drug combinations possible and the associated costs and risks of failure. Mathematical modelling presents a powerful way out by mimicking disease progression and treatment outcome on the computer. Our goal is to develop such a mathematical model and use it to identify the drug combination that is least likely to fail. We will then extend the model to hepatitis C virus infection, for which several drugs are in the pipeline. From these studies, we hope to gain insights into the nature of drugs that are likely to be effective against rapidly evolving pathogens. Using these insights, we plan to design new drug and vaccine candidates that will elicit potent, lasting activity against rapidly evolving pathogens and improve our ability to combat infectious diseases.

Figure Legend: Schematic of the interplay between the hepatitis C virus and the drug interferon within an infected cell, with each suppressing the other and leading to a double negative feedback (Inset). Reproduced from Padmanabhan et al., Nature Communications 5: 3872 (2014)