Department of Electrical and Computer Engineering,University of Delaware, Newark, DE 19716, USA.
IEEE Trans Biomed Eng. 2012 Aug;59(8):2227-34. doi: 10.1109/TBME.2012.2201479. Epub 2012 May 25.
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.
在抗逆转录病毒治疗期间，人类免疫缺陷病毒耐药株的出现是治疗失败和疾病进展的主要原因。耐药株的产生需要切换到由新型药物组成的新的抗逆转录病毒方案。最近的研究表明，目前改变抗病毒治疗的方法会带来显著的后续失败的不必要风险，并且已经提出了最佳的转换方案以最小化这种风险。这些转换方案需要在诱导性短暂病毒载量降低期间频繁采样病毒载量，目的是在诱导性病毒载量最低时切换到新的抗病毒方案。所提议的频繁采样在测量费用和对患者的不便方面都带来了不可接受的成本水平。在本文中，我们提出了一种闭环采样算法，以减少实现所需风险降低所需的样本数量。我们通过蒙特卡罗分析证明，该方法能够在保持后续失败风险降低至 3%以下的情况下，稳健地将所需样本数量平均减少 50%，尽管存在经过实验验证的模型和测量不确定性。
