Abstract

It is becoming clear that one solution to the development of new treatments for complex diseases such as HIV-1, resistant bacterial strains, or cancer is to use efficient drug combinations whose properties cannot be achieved by one drug alone. However, studying all possible combinations of drugs is impractical – if not infeasible – and thus, there is a critical need for new approaches to tackle this challenge. In an effort to illuminate this currently intractable question, we developed a computational and experimental framework to predict the effects of multiple stimuli on the induction of immune responses, and evaluated the utility of this principle for the development of new vaccines. In this project, we hypothesized that the effects of higher-order combinations of stimuli (i.e., triplets or quadruplets) can be accurately predicted by using information about the effects of single and pairs of stimuli only. Specifically, we tested this idea by combining high-throughput in vitro co-culture assays followed by in vivo testing in mouse models of cancer, in an effort to develop innovative anti-tumor vaccines. This interdisciplinary work marks a departure from current approaches to investigate immunological pathways and their interactions, and is poised to make a significant impact on how to rationally select combinations of drugs or adjuvants to enable desired effects on biological processes.

References:

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