Costs versus benefits: best possible and best practical treatment regimens for HIV

HIV-1 resistance to neutralizing antibodies: Determination of antibody concentrations leading to escape mutant evolution

Broadly neutralizing antibodies against human immunodeficiency virus type 1 (HIV-1) are considered vital components of novel therapeutics and blueprints for vaccine research. Yet escape to even the most potent of these antibodies is imminent in natural infection. Measures to define antibody efficacy and prevent mutant selection are thus urgently needed. Here, we derive a mathematical framework to predict the concentration ranges for which antibody escape variants can outcompete their viral ancestors, referred to as mutant selection window (MSW). When determining the MSW, we focus on the differential efficacy of neutralizing antibodies against HIV-1 in two canonical infection routes, free-virus infection and cell-cell transmission. The latter has proven highly effective in vitro suggesting its importance for both in vivo spread as well as for escaping targeted intervention strategies. We observed a range of MSW patterns that highlight the potential of mutants to arise in both transmission pathways and over wide concentration ranges. Most importantly, we found that only when the arising mutant has both, residual sensitivity to the neutralizing antibody and reduced infectivity compared to the parental virus, antibody dosing outside of the MSW to restrict mutant selection is possible. Emergence of mutants that provide complete escape and have no considerable fitness loss cannot be prevented by adjusting antibody doses. The latter may in part explain the ubiquitous resistance to neutralizing antibodies observed in natural infection and antibody treatment. Based on our findings, combinations of antibodies targeting different epitopes should be favored for antibody-based interventions as this may render complete resistance less likely to occur and also increase chances that multiple escapes result in severe fitness loss of the virus making longer-term antibody treatment more feasible.

Optimal control of HIV dynamic using embedding method

This present study proposes an optimal control problem, with the final goal of implementing an optimal treatment protocol which could maximize the survival time of patients and minimize the cost of drug utilizing a system of ordinary differential equations which describes the interaction of the immune system with the human immunodeficiency virus (HIV). Optimal control problem transfers into a modified problem in measure space using an embedding method in which the existence of optimal solution is guaranteed by compactness of the space. Then the metamorphosed problem is approximated by a linear programming (LP) problem, and by solving this LP problem a suboptimal piecewise constant control function, which is more practical from the clinical viewpoint, is achieved. The comparison between the immune system dynamics in treated and untreated patients is introduced. Finally, the relationships between the healthy cells and virus are shown.

Population biological principles of drug-resistance evolution in infectious diseases

The emergence of resistant pathogens in response to selection pressure by drugs and their possible disappearance when drug use is discontinued are evolutionary processes common to many pathogens. Population biological models have been used to study the dynamics of resistance in viruses, bacteria, and eukaryotic microparasites both at the level of the individual treated host and of the treated host population. Despite the existence of generic features that underlie such evolutionary dynamics, different conclusions have been reached about the key factors affecting the rate of resistance evolution and how to best use drugs to minimise the risk of generating high levels of resistance. Improved understanding of generic versus specific population biological aspects will help to translate results between different studies, and allow development of a more rational basis for sustainable drug use than exists at present.

Drug-Sparing Regimens for HIV Combination Therapy: Benefits Predicted for “Drug Coasting”

Structured Treatment Interruptions (STI) during HIV drug therapy were thought to potentially reduce side effects and toxicity, boost immune involvement, and possibly lower the viral set-point. Clinical trials of STI regimens, however, have had mixed results. We use an established mathematical model of HAART to estimate possible therapeutic outcomes for STI and for other, similar patterns in HIV combination therapy. We perform an exhaustive search of patterns of up to 60 days, for triple-drug combinations involving accurate pharmacokinetics for 12 specific antiviral drugs. The results of this analysis are consistent with recent clinical trials which have demonstrated that STI-type patterns, involving therapy interruption of weeks or months, are rarely optimal. Our analysis predicts, however, that the benefit of treatment can often be improved by including very short drug-free periods, during which the patient effectively "coasts" for a day or two on adequate drug concentrations due to the long half-life of some pharmaceuticals. Our analysis predicts many cases in which this may be achieved without increasing the risk of drug-resistance. This suggests that "drug coasting" patterns, significantly shorter than STI patterns, may merit further clinical investigation in efforts to find drug-sparing regimens for HIV.

Optimal drug treatment regimens for HIV depend on adherence

Drug therapies aimed at suppressing the human immunodeficiency virus (HIV) are highly effective, often reducing the viral load to below the limits of detection for years. Adherence to such antiviral regimens, however, is typically far from ideal. We have previously developed a model that predicts optimal treatment regimens by weighing drug toxicity against CD4+ T-cell counts, including the probability that drug resistance will emerge. We use this model to investigate the influence of adherence on therapy benefit. For a drug with a given half-life, we compare the effects of varying the dose amount and dose interval for different rates of adherence, and compute the optimal dose regimen for adherence between 65% and 95%. Our results suggest that for optimal treatment benefit, drug regimens should be adjusted for poor adherence, usually by increasing the dose amount and leaving the dose interval fixed. We also find that the benefit of therapy can be surprisingly robust to poor adherence, as long as the dose interval and dose amount are chosen accordingly.