Summary of model parameters.

<p>Parameters in the model were optimally chosen to match the monotherapy characteristics from Murray <i>et al.</i><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002971#pcbi.1002971-Murray2" target="_blank">[6]</a>, and , , , and were chosen to be consistent with previous literature values.</p

Plasma HIV RNA curves for the four drug classes INI, RTI, EI, and PI.

<p>The dotted, short dashed, long dashed, and solid lines are model simulations where the effects of an INI, RTI, EI, and PI respectively were modeled. The key features of the model have been labeled. The time delays are given by . An efficacy of 0.95 has been used for each drug.</p

Model for HIV infection in CD4+ T cells.

<p>Production and loss includes CD4+ T cell production, CD4+ T cell death of infected and uninfected cells, and viral clearance. The major HIV infection stages in the model are: entry, reverse transcription, integration, and viral production. The model describes five types of CD4+ T cells: uninfected cells , cells where HIV has passed entry , cells where HIV has passed RT , cells where HIV has passed IN , and productively infected CD4+ T cells . Virus comprises infectious virus , and non-infectious virus (when PIs are administered).</p

Influence of the progression rate to IN,

<p><b>, on the pVL curves.</b> (A) A relatively low rate of (black curves) giving a variance of 0.5 days around the mean time to IN , leads to a large slope difference between the INI and RTI, compared to a fast rate (gray curves) with standard deviation of 0.1 days around the mean for virus progressing from the middle of RT to IN. (B) In this panel, we show how the CD4+ T cells move from RT to IN for all drug classes. For , note the relatively slow rate at which the progress from the RT stage and move to IN, is reflected in the slow decrease in . An efficacy of 0.95 is used for each drug.</p

Monotherapy data.

<p>Initial delay and phase IA monotherapy slopes when fitting biphasic decay curves separately to each drug group <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002971#pcbi.1002971-Murray2" target="_blank">[6]</a>. Standard deviations (Std Dev.) were obtained from the nonlinear mixed effects calculations for each drug. The number of patients used in the analysis is also shown. The entries that are empty under the slope category did not admit nonlinear mixed effect fits due to insufficient data. The pharmacological delay has been subtracted from the delays.</p

Illustration of the different phase IA slope possibilities in the model.

<p>(A) When the productively infected death rate is low, or the rate to viral production is low, a difference in phase IA slopes is not seen between the drug classes. (B) When the progression rate to IN complete is low compared to the productively infected death rate or the rate to viral production, a phase IA slope difference will be observed. In this case the INI will have a steeper decay than the RTI, EI, or PI.</p

Qualitative comparison with the raw plasma viral load data.

<p>The raw plasma viral load data from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002971#pcbi.1002971-Murray2" target="_blank">[6]</a> and the model with parameters given in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002971#pcbi-1002971-t001" target="_blank">Table 1</a> have been overlayed. The complete monotherapy data sets are shown. (A) For the INI, we show viral load data during raltegravir treatment. (B) For the RTI, rilpivirine, abacavir, and tenofovir are shown. (C) For the EI, enfuvirtide and maraviroc are shown. (D) For the PI, ritonavir and nelfinavir are shown. Good agreement is seen across the four drug classes in the model. We note the model is fitted to the longitudinal analysis of the assay data from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002971#pcbi.1002971-Murray2" target="_blank">[6]</a>. Efficacy is set to 0.90 for each drug class.</p

Absolute change over 96 weeks in spine BMD by HLA-DQ3 supertype and randomisation in participants naïve to both ABC and TDF at study entry.

<p>Absolute change over 96 weeks in spine BMD by HLA-DQ3 supertype and randomisation in participants naïve to both ABC and TDF at study entry.</p

Moving Away from Ritonavir, Abacavir, Tenofovir, and Efavirenz (RATE) - Agents That Concern Prescribers and Patients: A Feasibility Study and Call for a Trial

<div><p>Objectives</p><p>Regimens sparing RATE (ritonavir, abacavir, tenofovir, efavirienz) agents might have better long-term safety. We conducted a feasibility exercise to assess the potential for a randomised trial evaluating RATE-sparing regimens.</p><p>Design</p><p>Observational.</p><p>Methods</p><p>We first calculated RATE-sparing options available to an average patient receiving RATE agents. We reviewed treatment history and all resistance assays from patients attending the St. Vincent’s Hospital (Sydney) clinic and receiving ≥2 RATE agents (n = 120). A viable RATE-sparing regimen with 2 or 3 fully-active agents was constructed from the following six ‘safer’ agents: rilpivirine or etravirine; atazanavir; raltegravir; maraviroc; and lamivudine. Activity for each drug was predicted as 1 (full-activity), 0.5 or 0 (no activity) using the Stanford mutation database. The utility of maraviroc was calculated assuming both maraviroc activity and inactivity where unknown. The analysis was restricted to regimens for which supporting evidence was identified in the literature or conference proceedings. Finally, we calculated the proportion of patients in the nationally representative Australian HIV Observational Database (AHOD) cohort receiving ≥2 RATE agents (n = 1473) to measure the potential population-level uptake of RATE-sparing agents.</p><p>Results</p><p>Assuming full maraviroc activity, 117(97.5%) and 107(89.2%) individuals had at least one option with 2 or 3 active RATE-sparing agents, respectively. Assuming no maraviroc activity this decreased to 113(94.2%) and 104(86.7%), respectively. In AHOD, 837(56.8%) patients were receiving ≥2 RATE agents.</p><p>Conclusion</p><p>Feasible treatment switch options sparing RATE agents exist for the majority of patients. Understanding the pros and cons of switching stable patients onto new RATE-sparing regimens requires evidence derived from randomised controlled trials.</p></div

Number of treatment options under various scenarios in the Hospital database.

<p>Number of treatment options under various scenarios in the Hospital database.</p