Deecke Lucas, Dobrovolny Hana M
Institut für Theoretische Physik, Universität zu Köln, Cologne, Germany.
Department of Physics & Astronomy, Texas Christian University, Fort Worth, TX, USA.
J Theor Biol. 2018 Apr 7;442:129-138. doi: 10.1016/j.jtbi.2018.01.012.
Severe, long-lasting influenza infections are often caused by new strains of the virus. The long duration of these infections leads to an increased opportunity for the emergence of drug resistant mutants. This is particularly problematic since for new strains there is often no vaccine, so drug treatment is the first line of defense. One strategy for trying to minimize drug resistance is to apply drugs periodically. During treatment phases the wild-type virus decreases, but resistant virus might increase; when there is no treatment, wild-type virus will hopefully out-compete the resistant virus, driving down the number of resistant virus. A stochastic model of severe influenza is combined with a model of drug resistance to simulate long-lasting infections and intermittent treatment with two types of antivirals: neuraminidase inhibitors, which block release of virions; and adamantanes, which block replication of virions. Each drug's ability to reduce emergence of drug resistant mutants is investigated. We find that cell regeneration is required for successful implementation of intermittent treatment and that the optimal cycling parameters change with regeneration rate.
严重的、持续时间长的流感感染通常由病毒的新毒株引起。这些感染的持续时间长会增加耐药突变体出现的机会。这尤其成问题，因为对于新毒株通常没有疫苗，所以药物治疗是第一道防线。试图尽量减少耐药性的一种策略是定期用药。在治疗阶段，野生型病毒数量减少，但耐药病毒数量可能增加；在没有治疗时，野生型病毒有望胜过耐药病毒，从而降低耐药病毒的数量。将严重流感的随机模型与耐药性模型相结合，以模拟持续时间长的感染以及用两种抗病毒药物进行间歇性治疗：神经氨酸酶抑制剂，其可阻断病毒粒子的释放；金刚烷类药物，其可阻断病毒粒子的复制。研究了每种药物减少耐药突变体出现的能力。我们发现，间歇性治疗的成功实施需要细胞再生，并且最佳循环参数会随再生率而变化。
