Neuraminidase inhibitor resistance in influenza: assessing the danger of its generation and spread.
Neuraminidase inhibitor resistance in influenza: assessing the danger of its generation and spread.
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DOI：
10.1371/journal.pcbi.0030240
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发表时间：
2007-12
影响因子：
4.3
通讯作者：
Antia R
中科院分区：
文献类型：
--
作者：
Handel A;Longini IM Jr;Antia R
神经氨酸酶抑制剂（NI）是目前最有效的抗流感药物。最近发生的耐药性病例令人关切。为了评估NI耐药性的危险，许多研究报告了可以分离耐药菌株的治疗患者的比例。不幸的是，这些结果在很大程度上取决于实验方案的细节。此外，了解耐药患者的比例本身并不太有用。相反，我们想知道的是，感染患者在第二宿主中产生耐药感染的可能性有多大，以及耐药菌株随后传播的可能性有多大。虽然这些参数的估计值通常可以从流行病学数据中获得，但缺乏流感NI耐药性的此类数据。在这里，我们使用一种不依赖流行病学数据的方法。相反，我们将来自人类志愿者流感感染的联合收割机数据与数学框架相结合，该数学框架允许估计控制初始生成和随后的耐药性传播的参数。我们展示了这些参数是如何受到药物疗效、治疗时机、耐药菌株适应性以及病毒和免疫系统动态细节变化的影响。我们的研究提供了可直接用于数学和计算模型的参数估计，以研究NI的使用如何导致耐药性在人群中的出现和传播。我们发现，耐药病例的初始生成很可能低于报告的耐药病例的比例。然而，我们也表明，结果强烈依赖于流感感染的宿主内动态的细节，最重要的是，免疫系统发挥的作用。更好地了解流感感染期间免疫反应的定量动力学对于进一步改善结果至关重要。神经氨酸酶抑制剂（NI）是目前最有效的抗流感药物。最近发生的耐药性病例令人关切。许多研究报告了可以分离出耐药病毒的治疗患者比例。虽然这些结果提供了一些评估的危险NI电阻，更定量的理解是可取的。我们特别想知道，一个被感染的、接受治疗的病人感染另一个人的耐药菌株的可能性有多大，以及耐药菌株随后传播的可能性有多大。了解这些数量对于研究整个人群的耐药性非常重要。虽然这些参数通常可以从流行病学数据中估计，但缺乏流感NI耐药性的此类数据。在这里，我们使用一种替代方法，将人类志愿者流感感染的数据与数学框架相结合。我们发现，耐药病例的初始生成很可能低于报告的耐药病例的比例。然而，我们的研究也清楚地表明，结果在很大程度上取决于免疫反应所起的作用，这是一个需要在未来研究中解决的问题。
Neuraminidase Inhibitors (NI) are currently the most effective drugs against influenza. Recent cases of NI resistance are a cause for concern. To assess the danger of NI resistance, a number of studies have reported the fraction of treated patients from which resistant strains could be isolated. Unfortunately, those results strongly depend on the details of the experimental protocol. Additionally, knowing the fraction of patients harboring resistance is not too useful by itself. Instead, we want to know how likely it is that an infected patient can generate a resistant infection in a secondary host, and how likely it is that the resistant strain subsequently spreads. While estimates for these parameters can often be obtained from epidemiological data, such data is lacking for NI resistance in influenza. Here, we use an approach that does not rely on epidemiological data. Instead, we combine data from influenza infections of human volunteers with a mathematical framework that allows estimation of the parameters that govern the initial generation and subsequent spread of resistance. We show how these parameters are influenced by changes in drug efficacy, timing of treatment, fitness of the resistant strain, and details of virus and immune system dynamics. Our study provides estimates for parameters that can be directly used in mathematical and computational models to study how NI usage might lead to the emergence and spread of resistance in the population. We find that the initial generation of resistant cases is most likely lower than the fraction of resistant cases reported. However, we also show that the results depend strongly on the details of the within-host dynamics of influenza infections, and most importantly, the role the immune system plays. Better knowledge of the quantitative dynamics of the immune response during influenza infections will be crucial to further improve the results. Neuraminidase Inhibitors (NI) are currently the most effective drugs against influenza. Recent cases of NI resistance are a cause for concern. A number of studies have reported the fraction of treated patients from which resistant virus could be isolated. While these results provide some assessment of the danger of NI resistance, a more quantitative understanding is preferable. We specifically want to know how likely it is that an infected, treated patient infects another person with the resistant strain, and how likely it is that the resistant strain subsequently spreads. Knowing these quantities is important for studies of the population-wide emergence of resistance. While these parameters can often be estimated from epidemiological data, such data is lacking for NI resistance in influenza. Here, we use an alternative approach that combines data from influenza infections of human volunteers with a mathematical framework. We find that the initial generation of resistant cases is most likely lower than the fraction of resistant cases reported. However, our study also clearly shows that the results depend strongly on the role the immune response plays, an issue that needs to be addressed in future studies.
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