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

Community-wide assessment of GPCR structure modelling and ligand docking: GPCR Dock 2008

Recent breakthroughs in the determination of the crystal structures of G protein-coupled receptors (GPCRs) have provided new opportunities for structure-based drug design strategies targeting this protein family. With the aim of evaluating the current status of GPCR structure prediction and ligand docking, a community-wide, blind prediction assessment - GPCR Dock 2008 - was conducted in coordination with the publication of the crystal structure of the human adenosine A2Areceptor bound to the ligand ZM241385. Twenty-nine groups submitted 206 structural models before the release of the experimental structure, which were evaluated for the accuracy of the ligand binding mode and the overall receptor model compared with the crystal structure. This analysis highlights important aspects for success and future development, such as accurate modelling of structurally divergent regions and use of additional biochemical insight such as disulphide bridges in the extracellular loops

Identifying genetic signatures of vaccine-induced immune responses in HIV-1 infected MRKAd5 STEP vaccine study subjects

Thesis (Ph.D.)--University of Washington, 2015Massively parallel sequencing technologies have been extensively applied in HIV-1 research to study the presence of minority variants. Insight gained through these technologies includes identification of minor drug resistant variants and immune escape variants. However, given the massive amounts of data generated, processing the sequences and discerning true minor variants from sequencing artifacts is important. Additionally, errors introduced during viral template amplification and incorrect quantification of templates prior to the sequencing process can further obfuscate resolving mismatch errors within the sequences. I address these issues in this dissertation. We developed a computational algorithm, CorQ, to correct specific patterns of sequencing errors and call Single Nucleotide Polymorphisms (SNPs). When coupled with additional error correction steps, we observed a 97% reduction in insertion, and deletion sequencing errors. In addition, we observed over 98% specificity in SNP detection compared to other available error correction methods. We observed reduced SNP calling specificity when error correction programs were tested on sequences with simulated PCR amplification mismatch errors, with the highest specificity of 70% observed with a combination CorQ algorithm, highlighting the difficulty in resolving errors generated during PCR amplification. We observed over 99% concordance in consensus variants observed in multiple HIV-1 infected subjects sequenced with traditional Sanger sequencing and pyrosequencing. The majority of SNPs that were specific to subjects’ pyrosequences were present at less than 2% of the subjects viral sequence population. We observed higher accuracy in variant frequencies in positions where read coverage exceeded the number of input templates. We have applied the developed error correction algorithm and observations from SNP variant comparisons to identifying major and minor variants observed within predicted T-cell epitope regions in HIV-1 infected study subjects enrolled in the MRKAd5 STEP vaccine trial. We observed genetic signatures of immune responses primed by the vaccine on breakthrough HIV-1 sequences. We observed greater genetic distances to the vaccine sequence in breakthrough sequences from vaccine recipients than placebo recipients and this difference was most significant within T-cell epitope regions. Additionally over time, the vaccine-primed immune responses resulted in reduced epitope diversity and decreased rates of epitope evolution over time. Combined, these results strongly support the hypothesis that the MRKAd5 vaccine resulted in T-cell mediated selection occurring post-infection. The results from our study are the first evidence of vaccine-induced anamnestic pressure influencing CTL epitope evolution and epitope diversity over time during HIV-1 infection

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

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

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

Deterministic Model of the Dynamics of Resistant Viruses under the One-Drug, One-Mutant, One-Cell Version of Our Target-Cell Model

<div><p>(A) Slow turnover rates for CD4⁺ target cells (<i>m</i> = 0.02, <i>k</i> = 0.0005).</p><p>(B) Rapid turnover rates for CD4⁺ target cells (<i>m</i> = 0.32, <i>k</i> = 0.008).</p><p>Here <i>m</i> and <i>k</i> were increased proportionately so as to isolate the effect of changing turnover rate without altering pre-therapy viral load. Blue lines, WT virus; red lines, drug-resistant virus; green lines, target cells. These simulations assume a high cost of resistance (<i>w</i>₁ = <i>k</i>₁/<i>k</i> = 0.6). 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> assuming a single population of short-lived infected cells. Interpretation: these simulations illustrate previous theoretical studies showing the concentration of drug-resistant viruses declines transiently following the initiation of therapy.</p></div

Computer Simulations Demonstrating Success Rates in Eradicating Viruses Resistant to Maintenance Therapy as a Function of Fitness Costs of Resistance and Turnover Rates of Target Cells

<div><p>(A,B) Effects of fitness (<i>w</i>) of resistant viruses in the absence of drug.</p><p>(C,D) Effect of target-cell death rates (<i>m</i>) (modeled here with simultaneous increases in <i>k</i> in order to keep pre-therapy viral load 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 500 replicate simulations. Interpretation: delaying induction therapy until after the start of maintenance therapy results in higher success rates. Under these conditions, starting a 30-d induction period after the start of maintenance therapy usually optimized the probably of success. Success rates decline as the fitness cost of resistance mutations decreases (<i>w</i> approaches 1) and as target-cell turnover rates (<i>m</i>) increase. The latter effect occurs because target cells necessary for the return of resistant virus rebound more rapidly after therapy at higher turnover rates.</p></div

Relationship between Duration of Induction Therapy and Start Time of Induction Therapy Relative to Start of Maintenance Therapy

<p><i>x</i>-Axis indicates interval between start of induction and maintenance therapies, in days. 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. Interpretation: the success of IM therapy increases with increasing duration of induction therapy. Delaying the start of induction therapy until ∼40 d after the start of maintenance therapy may be optimal, and the effect of timing is most pronounced with induction therapies lasting 0.5–2 mo. Longer and shorter induction periods are less sensitive to the effects of timing. There is little benefit to adding a delayed-induction therapy at times beyond 90 d after the start of maintenance therapy.</p