慢性HBV感染的联合与序贯单药治疗:一种数学方法

Daniela Bertacchi;Fabio Zucca;Sergio Foresti;Davide Mangioni;Andrea Gori
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引用次数: 2

摘要

序贯单药治疗是治疗乙型肝炎病毒(HBV)慢性感染最广泛使用的治疗方法。不幸的是,在治疗过程中,一些患者的肝炎病毒会发生变异,并产生耐药性。我们想知道这些患者是否会从选择联合治疗而不是顺序单一治疗中受益。为了研究这两种治疗方法的作用并解释耐药性的出现,我们提出了一个随机模型,用于连续或同时接受两种药物治疗的患者体内的感染,以及在第一种治疗下产生对两种药物都有耐药性的病毒株的患者。我们的随机模型具有确定性近似,这是对经典三应变模型的轻微修改。我们讨论了为什么在对突变的上升进行建模时,随机模拟比确定性近似的研究更合适(这主要是由于随机波动的幅度)。我们使用合适的参数进行随机模拟,并比较在两种治疗方法下,耐药菌株首次在血清病毒载量中达到可检测性的时间。我们的研究结果表明,最好的选择是尽早开始联合治疗,这可以让人在更长的时间内保持无耐药性,在许多情况下可以根除病毒。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Combination versus sequential monotherapy in chronic HBV infection: a mathematical approach
Sequential monotherapy is the most widely used therapeutic approach in the treatment of hepatitis B virus (HBV) chronic infection. Unfortunately, under therapy, in some patients the hepatitis virus mutates and gives rise to variants which are drug resistant. We wonder whether those patients would have benefited from the choice of combination therapy instead of sequential monotherapy. To study the action of these two therapeutic approaches and to explain the emergence of drug resistance, we propose a stochastic model for the infection within a patient who is treated with two drugs, either sequentially or contemporaneously, and who, under the first kind of therapy develops a strain of the virus which is resistant to both drugs. Our stochastic model has a deterministic approximation which is a slight modification of a classic three-strain model. We discuss why stochastic simulations are more suitable than the study of the deterministic approximation, when modelling the rise of mutations (this is mainly due to the amplitude of the stochastic fluctuations). We run stochastic simulations with suitable parameters and compare the time when, under the two therapeutic approaches, the resistant strain first reaches detectability in the serum viral load. Our results show that the best choice is to start an early combination therapy, which allows one to stay drug resistance free for a longer time and in many cases leads to viral eradication.
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