HIV latency reversal

HIV cure research using latency reversing agents

The existence of viral reservoirs, such as latently infected cells, i.e. cells infected by HIV but that do not actively produce virus, is a major barrier to clear HIV infection. Research efforts have been focused on developing latency reserving agents (LRAs) to activate HIV production in latently infected cells and to ultimately cure HIV infection. The idea, termed ‘shock and kill’, is to first ‘shock’ the cells using LRAs, and then ‘kill’ the cells through virus- or immune-mediated cell death, and ultimately to eradicate the latent reservoir.

Some of the questions we are interested in are:

  1. What drives the population dynamics of the HIV latent reservoir?
  2. What determines the efficacy of LRAs?
  3. How do LRAs and some immuno-therapies  act to ‘shock’ and ‘kill’ latently infected cells, i.e. how do therapeutics interact with the latently infected cells and the immune system?
  4. How do we design/combine therapeutic strategies to effectively reduce the size of the reservoir?

Currently, we are collaborating with David Margolis at UNC Chapel Hill to develop mathematical models to understand the potential impact of combination therapies using LRAs and immuno-therapies.

Previously, we’ve worked on the following topics:

  • In collaborations with clinicians, we (with Alan Perelson at LANL) have developed viral dynamic models incorporating the impact of LRA treatments on the latent reservoir. By fitting these models to clinical data derived from patients treated with a LRA, vorinostat, using non-linear optimization, we have shown that HIV transcription is activated transiently at the initiation of the treatment and that sustained activation of HIV genes may depend on the long term impact of vorinostat on host gene expression. The results further suggest that vorinostat treatment does not reduce the latent reservoir in patients. More potent drugs or drugs that induce killing of activated cells are needed.
  • We (with Jessica Conway at Penn State) have constructed stochastic models to understand extinction dynamics of latently infected cells under latency reversing agent treatment.
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