Robust Closed-Loop Minimal Sampling Method for HIV Therapy Switching Strategies

The emergence of drug-resistant strains of human immunodeficiency virus during antiretroviral therapy is a major cause of treatment failure and disease progression. Development of a resistant strain necessitates switching to a new antiretroviral regimen composed of novel drugs. Recent work has shown that current methods of switching antiviral therapies carry significant unnecessary risk of subsequent failures, and optimal switching schedules to minimize this risk have been proposed. These switching schedules require frequent sampling of viral load during an induced phase of transient viral load reduction, with the goal of switching to the new antiviral regimen at an induced viral load minimum. The proposed frequent sampling carries an unacceptable level of cost both in terms of measurement expense and inconvenience to the patient. In this paper, we propose a closed-loop sampling algorithm to reduce the number of samples required to achieve the desired reduction in risk. We demonstrate through the Monte-Carlo analysis that the proposed method is able to robustly achieve an average 50% reduction in the number of required samples while maintaining a reduction in the risk of subsequent failure to under 3%, despite experimentally verified levels of model and measurement uncertainty.