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

Contralateral Second Dose Improves Antibody Responses to a 2-Dose Mrna Vaccination Regimen

BACKGROUND. Vaccination is typically administered without regard to site of prior vaccination, but this factor may substantially affect downstream immune responses. METHODS. We assessed serological responses to initial COVID-19 vaccination in baseline seronegative adults who received second-dose boosters in the ipsilateral or contralateral arm relative to initial vaccination. We measured serum SARSCoV-2 spike–specific Ig, receptor-binding domain–specific (RBD-specific) IgG, SARS-CoV-2 nucleocapsid–specific IgG, and neutralizing antibody titers against SARS-CoV-2.D614G (early strain) and SARS-CoV-2.B.1.1.529 (Omicron) at approximately 0.6, 8, and 14 months after boosting. RESULTS. In 947 individuals, contralateral boosting was associated with higher spike-specific serum Ig, and this effect increased over time, from a 1.1-fold to a 1.4-fold increase by 14 months (P \u3c 0.001). A similar pattern was seen for RBDspecific IgG. Among 54 pairs matched for age, sex, and relevant time intervals, arm groups had similar antibody levels at study visit 2 (W2), but contralateral boosting resulted in significantly higher binding and neutralizing antibody titers at W3 and W4, with progressive increase over time, ranging from 1.3-fold (total Ig, P = 0.007) to 4.0-fold (pseudovirus neutralization to B.1.1.529, P \u3c 0.001). CONCLUSIONS. In previously unexposed adults receiving an initial vaccine series with the BNT162b2 mRNA COVID-19 vaccine, contralateral boosting substantially increases antibody magnitude and breadth at times beyond 3 weeks after vaccination. This effect should be considered during arm selection in the context of multidose vaccine regimens

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

HIV Therapy Simulator: a graphical user interface for comparing the effectiveness of novel therapy regimens

Abstract Abstract: Computer simulation models can be useful in exploring the efficacy of HIV therapy regimens in preventing the evolution of drug-resistant viruses. Current modeling programs, however, were designed by researchers with expertise in computational biology, limiting their accessibility to those who might lack such a background. We have developed a user-friendly graphical program, HIV Therapy Simulator (HIVSIM), that is accessible to non-technical users. The program allows clinicians and researchers to explore the effectiveness of various therapeutic strategies, such as structured treatment interruptions, booster therapies and induction-maintenance therapies. We anticipate that HIVSIM will be useful for evaluating novel drug-based treatment concepts in clinical research, and as an educational tool. Availability: HIV Therapy Simulator is freely available for Mac OS and Windows at http://sites.google.com/site/hivsimulator/. Contact:  [email protected] Supplementary Information:  Supplementary data are available at Bioinformatics online.</jats:p

Schematic Illustrating Treatment Strategies Investigated in This Study

<div><p>(A) Effect of progressively longer induction regimens (circles A–C) on the likelihood of successfully eradicating viruses resistant to maintenance therapy under our canonical parameter set.</p><p>(B) Effect of altering the timing of induction therapy (circles A–C) relative to maintenance therapy on the likelihood of successful therapy.</p><p><i>x</i>-Axis indicates duration of induction therapy in days (A), or interval between start of the induction and maintenance therapies, 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.</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