McCaw James M, Wood James G, McCaw Christopher T, McVernon Jodie
Vaccine and Immunisation Research Group, Murdoch Childrens Research Institute and Melbourne School of Population Health, The University of Melbourne, Parkville, Victoria, Australia.
PLoS One. 2008 Jun 4;3(6):e2362. doi: 10.1371/journal.pone.0002362.
Wide-scale use of antiviral agents in the event of an influenza pandemic is likely to promote the emergence of drug resistance, with potentially deleterious effects for outbreak control. We explored factors promoting resistance within a dynamic infection model, and considered ways in which one or two drugs might be distributed to delay the spread of resistant strains or mitigate their impact.
We have previously developed a novel deterministic model of influenza transmission that simulates treatment and targeted contact prophylaxis, using a limited stockpile of antiviral agents. This model was extended to incorporate subclinical infections, and the emergence of resistant virus strains under the selective pressure imposed by various uses of one or two antiviral agents. For a fixed clinical attack rate, R(0) rises with the proportion of subclinical infections thus reducing the number of infections amenable to treatment or prophylaxis. In consequence, outbreak control is more difficult, but emergence of drug resistance is relatively uncommon. Where an epidemic may be constrained by use of a single antiviral agent, strategies that combine treatment and prophylaxis are most effective at controlling transmission, at the cost of facilitating the spread of resistant viruses. If two drugs are available, using one drug for treatment and the other for prophylaxis is more effective at preventing propagation of mutant strains than either random allocation or drug cycling strategies. Our model is relatively straightforward, and of necessity makes a number of simplifying assumptions. Our results are, however, consistent with the wider body of work in this area and are able to place related research in context while extending the analysis of resistance emergence and optimal drug use within the constraints of a finite drug stockpile.
Combined treatment and prophylaxis represents optimal use of antiviral agents to control transmission, at the cost of drug resistance. Where two drugs are available, allocating different drugs to cases and contacts is likely to be most effective at constraining resistance emergence in a pandemic scenario.
在流感大流行期间广泛使用抗病毒药物可能会促使耐药性的出现，对疫情控制产生潜在的有害影响。我们在一个动态感染模型中探究了促使耐药性产生的因素，并考虑了如何分配一种或两种药物来延缓耐药菌株的传播或减轻其影响。
我们之前开发了一种新型的流感传播确定性模型，该模型使用有限的抗病毒药物储备来模拟治疗和针对性的接触预防。此模型被扩展以纳入亚临床感染，以及在一种或两种抗病毒药物的不同使用方式所施加的选择压力下耐药病毒株的出现情况。对于固定的临床发病率，R(0) 会随着亚临床感染比例的增加而上升，从而减少适合治疗或预防的感染数量。因此，疫情控制更加困难，但耐药性的出现相对少见。在疫情可能通过使用单一抗病毒药物得到控制的情况下，将治疗和预防相结合的策略在控制传播方面最为有效，但代价是会促进耐药病毒的传播。如果有两种药物可用，与随机分配或药物轮换策略相比，使用一种药物进行治疗而另一种药物进行预防在防止突变株传播方面更有效。我们的模型相对简单，并且必然做了一些简化假设。然而，我们的结果与该领域更广泛的研究工作一致，能够将相关研究置于背景之中，同时在有限药物储备的限制范围内扩展对耐药性出现和最佳药物使用的分析。
联合治疗和预防是控制传播的抗病毒药物的最佳使用方式，但会产生耐药性。在有两种药物可用的情况下，在大流行情况下将不同药物分配给病例和接触者可能最有效地抑制耐药性的出现。
