Efficacy of Antibody and T cell Therapies for Highly Mutable Viruses like Human Immunodeficiency
Author(s)
Murugan, Pranav M.
DownloadThesis PDF (2.452Mb)
Advisor
Chakraborty, Arup K.
Terms of use
Metadata
Show full item recordAbstract
The isolation of broadly neutralizing antibodies (bnAbs) that can neutralize diverse strains of highly mutable viruses like human immunodeficiency virus (HIV) as well as identification of mutationally-constrained regions of the proteome that could be targeted by T cells has led to interest in passive immunotherapies and therapeutic vaccines as promising methods for treating chronic infection. However, the feasibility of creating a sufficiently powerful therapy remains uncertain. In this work, we develop a stochastic computational model of viral dynamics to help characterize the regimes where viral control or cure may be possible. We study the efficacy of either bnAb therapy or therapeutic vaccination that elicits T cell responses that target mutationally-constrained regions, as well as treatments that combine these two therapeutic modalities. Our results show that combination therapy has the best chance of maintaining viral control or achieving a cure. this is because administering combinations of bnAbs with broad coverage of viral strains for a sufficiently long time can potentially clear rare strains from the latent reservoir which are likely to escape T cell responses resulting in viral rebound. We also describe a strong relation between the outcome of treatment and the diversity of the reservoir of latently infected cells, which suggest that the best candidates for immunotherapy are those who started antiretroviral therapy shortly after infection. Importantly, we find that cure is likely to be a rare outcome, and that the average time to cure is long and independent of therapeutic modality as it depends on the rate of activation of the latent reservoir. Our results will help guide the design of new therapeutics, and provide a platform for future computational screening of of the efficacy of new treatment regimes.
Date issued
2023-06Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer SciencePublisher
Massachusetts Institute of Technology