设计减轻不依从性的药物方案
Designing drug regimens that mitigate nonadherence
摘要：
药物依从性是慢性病药物治疗的一个众所周知的问题。理解不依从性如何影响治疗效果是困难的，因为临床试验的伦理学迫使患者跳过正在测试的药物剂量，实际患者错过剂量的不可预测的时间，以及许多竞争性变量可以减轻或放大不依从性的有害影响，如药代动力学吸收和消除率，给药间隔，剂量大小，依从率，本文建立并分析了一个不完全粘附病人体内药物浓度的数学模型。我们的模型采用具有一级吸收和消除的标准单室药代动力学模型的形式，不同之处在于患者仅在规定的给药时间的给定比例下服药。剂量是随机遗漏的，我们使用随机分析来研究体内产生的随机药物水平。然后，我们使用我们的数学结果，提出设计药物方案，是强大的不遵守原则。特别是，我们量化了缓释药物对非依从性的弹性，这在某些情况下是非常重要的，并且我们显示了如果药物吸收或消除速率与给药间隔相比缓慢，则在错过剂量后服用双倍剂量的益处。我们进一步使用我们的结果来比较一些抗癫痫和抗精神病药物治疗方案。
英文摘要：
Medication adherence is a well-known problem for pharmaceutical treatment of chronic diseases. Understanding how nonadherence affects treatment efficacy is made difficult by the ethics of clinical trials that force patients to skip doses of the medication being tested, the unpredictable timing of missed doses by actual patients, and the many competing variables that can either mitigate or magnify the deleterious effects of nonadherence, such as pharmacokinetic absorption and elimination rates, dosing intervals, dose sizes, adherence rates, etc. In this paper, we formulate and analyze a mathematical model of the drug concentration in an imperfectly adherent patient. Our model takes the form of the standard single compartment pharmacokinetic model with first order absorption and elimination, except that the patient takes medication only at a given proportion of the prescribed dosing times. Doses are missed randomly, and we use stochastic analysis to study the resulting random drug level in the body. We then use our mathematical results to propose principles for designing drug regimens that are robust to nonadherence. In particular, we quantify the resilience of extended release drugs to nonadherence, which is quite significant in some circumstances, and we show the benefit of taking a double dose following a missed dose if the drug absorption or elimination rate is slow compared to the dosing interval. We further use our results to compare some antiepileptic and antipsychotic drug regimens.
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