Computational study to determine when to initiate and alternate therapy in HIV infection

Abstract

HIV is a widespread viral infection without cure. Drug treatment has transformed HIV disease into a treatable long-term infection. However, the appearance of mutations within the viral genome reduces the susceptibility of HIV to drugs. Therefore, a key goal is to extend the time until patients exhibit resistance to all existing drugs. Current HIV treatment guidelines seem poorly supported as practitioners have not achieved a consensus on the optimal time to initiate and to switch antiretroviral treatments. We contribute to this discussion with predictions derived from a mathematical model of HIV dynamics. Our results indicate that early therapy initiation (within 2 years postinfection) is critical to delay AIDS progression. For patients who have not received any therapy during the first 3 years postinfection, switch in response to virological failure may outperform proactive switching strategies. In case that proactive switching is opted, the switching time between therapies should not be larger than 100 days. Further clinical trials are needed to either confirm or falsify these predictions.

Figure 1

HIV infection cycle affected by the four distinct drug classes. The drug classes are shown in red boxes.

Figure 2

Nonlinear HIV model. T represents the uninfected CD4+ T cells, T _i* represents the infected CD4+ T cells with the ith strain, M represents uninfected macrophages, M _i* represents infected macrophages with the ith strain, V _i represents the ith strain, and V _T is the sum of all strains.

Figure 3

Four variant mutation trees. The wild type (WT) is susceptible to both therapies. Genotype 1 (G1) is susceptible to therapy 2 and genotype 2 (G2) is susceptible to therapy 1. The highly resistant genotype (HRG) is not affected by any therapy. Parameter values were taken from [10].

Figure 4

Monte Carlo simulation algorithm. The Simulation box includes the simulation of the four-genotype mutation model under a certain treatment. For each of the six times of treatment initiation, the simulation will be repeated 1000 times and the data can be analysed.

Figure 5

SVF and SWATCH treatment strategies. CD4+ T cells and viral load are displayed for different initiation times (t _i). The top two panels show simulation results for the SVF strategy and the bottom panels show the results for the SWATCH strategy. Deterministic simulations are based on nominal parameter values from Table 2.

Figure 6

Monte Carlo simulation outcome for SVF (+) and SWATCH (□) strategies. Averaged time of immunological failure (${\stackrel{-}{t}}_f$) based on 1000 random samples is plotted against different initiation times (t _i). A parabolic function was fitted to the SWATCH data (solid black line) and a linear function to the SVF data (solid blue line).

Figure 7

Optimal switching between regimens. Time of immunological failure (t _f) with respect to switching time for different initiation times (t _i).