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Determinants of the efficacy of HIV latency-reversing agents and implications for drug and treatment design
Ruian Ke, Jessica M. Conway, David M. Margolis, Alan S. Perelson
Ruian Ke, Jessica M. Conway, David M. Margolis, Alan S. Perelson
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Research Article AIDS/HIV Therapeutics

Determinants of the efficacy of HIV latency-reversing agents and implications for drug and treatment design

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Abstract

HIV eradication studies have focused on developing latency-reversing agents (LRAs). However, it is not understood how the rate of latent reservoir reduction is affected by different steps in the process of latency reversal. Furthermore, as current LRAs are host-directed, LRA treatment is likely to be intermittent to avoid host toxicities. Few careful studies of the serial effects of pulsatile LRA treatment have yet been done. This lack of clarity makes it difficult to evaluate the efficacy of candidate LRAs or predict long-term treatment outcomes. We constructed a mathematical model that describes the dynamics of latently infected cells under LRA treatment. Model analysis showed that, in addition to increasing the immune recognition and clearance of infected cells, the duration of HIV antigen expression (i.e., the period of vulnerability) plays an important role in determining the efficacy of LRAs, especially if effective clearance is achieved. Patients may benefit from pulsatile LRA exposures compared with continuous LRA exposures if the period of vulnerability is long and the clearance rate is high, both in the presence and absence of an LRA. Overall, the model framework serves as a useful tool to evaluate the efficacy and the rational design of LRAs and combination strategies.

Authors

Ruian Ke, Jessica M. Conway, David M. Margolis, Alan S. Perelson

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Figure 2

The dynamics of the HIV latent reservoir under single or multiple LRA dosing cycles and the dependence of the LRA efficacy on the induced cell clearance rate.

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The dynamics of the HIV latent reservoir under single or multiple LRA do...
(A) Time course simulations of the mathematical model showing the reservoir dynamics during one LRA dosing cycle, where a patient is treated with LRA for 1 day (shaded in light red) followed by a 2-day resting period. The only difference between the 2 simulations is that δ = 0.05/day in the simulation on the left and δ = 0.5/day in the simulation on the right. The uninduced cells (L), the induced cells (A), the refractory cells (R), and the size of latent reservoir (L + A + R) are shown in blue, red, green, and black, respectively. (B) Time course simulations of the reservoir dynamics during and after 10 LRA dosing cycles. In both panel A and B, increasing the clearance rate from 0.05/day to 0.5/day significantly increases the LRA efficacy. Baseline parameter values used are α = 1.8/day on LRA, α = 0/day off LRA, γ = 0.5/day, ω = 1/day, ρ = 0.0072/day, d = 0.006/day, η = 0.0017/day (unless varied as indicated on the plots).

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