Optimal Timing and Duration of Induction Therapy for HIV-1 Infection

The tradeoff between the need to suppress drug-resistant viruses and the problem of treatment toxicity has led to the development of various drug-sparing HIV-1 treatment strategies. Here we use a stochastic simulation model for viral dynamics to investigate how the timing and duration of the induction phase of induction–maintenance therapies might be optimized. Our model suggests that under a variety of biologically plausible conditions, 6–10 mo of induction therapy are needed to achieve durable suppression and maximize the probability of eradicating viruses resistant to the maintenance regimen. For induction regimens of more limited duration, a delayed-induction or -intensification period initiated sometime after the start of maintenance therapy appears to be optimal. The optimal delay length depends on the fitness of resistant viruses and the rate at which target-cell populations recover after therapy is initiated. These observations have implications for both the timing and the kinds of drugs selected for induction–maintenance and therapy-intensification strategies

Daily and Nondaily Oral Preexposure Prophylaxis in Men and Transgender Women Who Have Sex With Men: The Human Immunodeficiency Virus Prevention Trials Network 067/ADAPT Study

Background: Nondaily dosing of oral preexposure prophylaxis (PrEP) may provide equivalent coverage of sex events compared with daily dosing. Methods: At-risk men and transgender women who have sex with men were randomly assigned to 1 of 3 dosing regimens: 1 tablet daily, 1 tablet twice weekly with a postsex dose (time-driven), or 1 tablet before and after sex (event-driven), and were followed for coverage of sex events with pre- and postsex dosing measured by weekly self-report, drug concentrations, and electronic drug monitoring. Results: From July 2012 to May 2014, 357 participants were randomized. In Bangkok, the coverage of sex events was 85% for the daily arm compared with 84% for the time-driven arm (P = .79) and 74% for the event-driven arm (P = .02). In Harlem, coverage was 66%, 47% (P = .01), and 52% (P = .01) for these groups. In Bangkok, PrEP medication concentrations in blood were consistent with use of ≥2 tablets per week in >95% of visits when sex was reported in the prior week, while in Harlem, such medication concentrations occurred in 48.5% in the daily arm, 30.9% in the time-driven arm, and 16.7% in the event-driven arm (P < .0001). Creatinine elevations were more common in the daily arm (P = .050), although they were not dose limiting. Conclusions: Daily dosing recommendations increased coverage and protective drug concentrations in the Harlem cohort, while daily and nondaily regimens led to comparably favorable outcomes in Bangkok, where participants had higher levels of education and employment

HIV-1 Envelope Subregion Length Variation during Disease Progression

The V3 loop of the HIV-1 Env protein is the primary determinant of viral coreceptor usage, whereas the V1V2 loop region is thought to influence coreceptor binding and participate in shielding of neutralization-sensitive regions of the Env glycoprotein gp120 from antibody responses. The functional properties and antigenicity of V1V2 are influenced by changes in amino acid sequence, sequence length and patterns of N-linked glycosylation. However, how these polymorphisms relate to HIV pathogenesis is not fully understood. We examined 5185 HIV-1 gp120 nucleotide sequence fragments and clinical data from 154 individuals (152 were infected with HIV-1 Subtype B). Sequences were aligned, translated, manually edited and separated into V1V2, C2, V3, C3, V4, C4 and V5 subregions. V1-V5 and subregion lengths were calculated, and potential N-linked glycosylation sites (PNLGS) counted. Loop lengths and PNLGS were examined as a function of time since infection, CD4 count, viral load, and calendar year in cross-sectional and longitudinal analyses. V1V2 length and PNLGS increased significantly through chronic infection before declining in late-stage infection. In cross-sectional analyses, V1V2 length also increased by calendar year between 1984 and 2004 in subjects with early and mid-stage illness. Our observations suggest that there is little selection for loop length at the time of transmission; following infection, HIV-1 adapts to host immune responses through increased V1V2 length and/or addition of carbohydrate moieties at N-linked glycosylation sites. V1V2 shortening during early and late-stage infection may reflect ineffective host immunity. Transmission from donors with chronic illness may have caused the modest increase in V1V2 length observed during the course of the pandemic

Simulations Demonstrating the Effects of Varying the Degree of Resistance on Treatment Success Rates

<p>As in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0030133#pcbi-0030133-g005" target="_blank">Figures 5</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0030133#pcbi-0030133-g006" target="_blank">6</a>, (A) and (C) demonstrate success rates as the duration of induction therapy is increased, and (B) and (D) demonstrate success rates over a range of induction therapy/therapy intensification start times. IC_50INT quantifies the degree of resistance that either mutation 1 or mutation 2 confers to drug I. IC_50MUT quantifies both the degree of resistance that mutation 3 confers to drug II and the degree of resistance that mutation 4 confers to drug III. <i>x</i>-Axis indicates duration of induction therapy in days (A,C), or interval between start of a 30-d induction therapy and maintenance therapy, in days (B,D). Maintenance therapy is assumed to start on day 0. <i>y</i>-Axis indicates percentage of simulations in which viral load remained undetectable for at least 3 y after ending induction therapy. Data in each panel were based on 400 simulations. Interpretation: IM therapy success rates decrease with the degree of resistance conferred by these mutations.</p

Simulations Demonstrating the Effects of Cross-Resistance on Treatment Success Rates

<p>As in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0030133#pcbi-0030133-g005" target="_blank">Figures 5</a>–<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0030133#pcbi-0030133-g007" target="_blank">7</a>, (A) demonstrates success rates as the duration of induction therapy is increased, and (B) demonstrates success rates over a range of induction therapy/therapy intensification start times. The different lines quantify the degree of resistance that mutations 3 and 4 confer against drugs III and II, respectively. <i>x</i>-Axis indicates duration of induction therapy in days (A), or interval between start of a 30-d induction therapy and maintenance therapy, in days (B). Maintenance therapy is assumed to start on day 0. <i>y</i>-Axis indicates percentage of simulations in which viral load remained undetectable for at least 3 y after ending induction therapy. Data in each panel were based on 400 simulations. Interpretation: IM therapy success rates decrease with the degree of cross-resistance between mutations 3 and 4.</p

Simulations of Viral Dynamics

<div><p>(A) Dynamics in the absence of therapy.</p><p>(B) Decline in viral load during potent triple-drug combination therapy. Maintenance and inducer drugs are provided for 360 d starting on day 0.</p><p>Dark blue line, target cells; black line, WT virus; blue-green lines, single mutants; orange lines, double mutants; red lines, triple mutants. Viral populations that are above the threshold for stochastic effects (dark gray line) may fluctuate if the corresponding infected cell populations are below the cutoff for stochastic effects. After the initiation of therapy, WT virus declines with appropriate first-, second-, and third-order kinetics. Viruses with a single mutation decline to near steady-state levels above the extinction threshold. Viruses with two resistance mutations approach the extinction threshold, but are not entirely eliminated by day 300. Triple mutants are generally extinct by day 40.</p></div

Overview of Cell Populations (A) and Mutations Responsible for Resistance (B)

<p>Mutation accumulation was modeled as a sequential process in which each genotype can acquire a single additional mutation in any given time-step (0.002 d in our simulations). In a single time step, <i>V</i>₁, for example, could mutate to <i>V</i>₁₂, <i>V</i>₁₃, or <i>V</i>₁₄, but not to <i>V</i>₁₂₃. The model also allows for recombinational steps (see text), which are not depicted here.</p

Computer Simulations of Dynamics of Drug-Resistant Virus under Simple Immune-Control Model

<div><p>(A) Immune-control analog of the one-cell, one-drug model presented in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0030133#pcbi-0030133-g004" target="_blank">Figure 4</a>.</p><p>(B) Effect of changing turnover rate of immune effectors under the immune-control analog of the full model explored in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0030133#pcbi-0030133-g005" target="_blank">Figures 5</a>–<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0030133#pcbi-0030133-g009" target="_blank">9</a>. In this simulation, the turnover rate of the immune effectors was modeled by simultaneously increasing <i>s</i>_X, <i>m</i>_X, and <i>k</i>_X. Here, <i>k</i> = 0.00085, <i>T</i> = 1,000, <i>μ</i> = 6 × 10⁻⁴, and <i>w</i>₁ = <i>w</i>₂ = <i>w</i>₃ = <i>w</i>₄ = 0.9. Other parameters are as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0030133#pcbi-0030133-t002" target="_blank">Table 2</a>.</p><p>Interpretation: changing the factor responsible for controlling viral load did not change the conclusion that drug resistant viruses will decrease transient after drug therapy. As with the target-cell limited model, the rate at which the factor that controlled viral load changed after therapy played a major role in determining when therapy should be intensified.</p></div

Computer Simulations Showing Relationships Between Long-Lived Infected Cells and Treatment Success Rates

<div><p>(A,B) Effect of proportion of infected cells becoming latently infected quiescent memory T lymphocytes (modeled here by changing <i>f</i>_L).</p><p>(C,D) Effect of varying the death rate of moderately long-lived infected cells, <i>δ</i>_M (modeled here with simultaneous increases in <i>f</i>_M in order to keep the pre-therapy density of moderately long-lived cells the same in each simulation).</p><p>(A) and (C) demonstrate success rates as the duration of induction therapy is increased, and (B) and (D) demonstrate success rates over a range of induction therapy start times. <i>x</i>-Axis indicates duration of induction therapy in days (A,C), or the interval between the start of a 30-d induction period and maintenance therapy in days (B,D). Maintenance therapy is assumed to start on day 0. <i>y</i>-Axis indicates percentage of simulations in which viral load remained undetectable for at least 3 y after ending induction therapy. Data in each panel were based on 400 simulations. Interpretation: the death rate of moderately long-lived infected cells is a major determinant of how long induction therapy should last. At expected rates of <i>f</i>_L (rate at which infected target cells transition to quiescent memory T lymphocytes), success rates depend little on rebound from the latent reservoir. However, success rates decline as the rate of virus input into the latent reservoir exceeds ∼6.4 × 10⁻⁶ per infected cell, indicating that rebound of resistant virus from the latent reservoir becomes a significant factor.</p></div