The many dimensions of combination therapy: How to combine antibiotics to limit resistance evolution

IF 3.5 2区 生物学 Q1 EVOLUTIONARY BIOLOGY
Christin Nyhoegen, Sebastian Bonhoeffer, Hildegard Uecker
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Abstract

In combination therapy, bacteria are challenged with two or more antibiotics simultaneously. Ideally, separate mutations are required to adapt to each of them, which is a priori expected to hinder the evolution of full resistance. Yet, the success of this strategy ultimately depends on how well the combination controls the growth of bacteria with and without resistance mutations. To design a combination treatment, we need to choose drugs and their doses and decide how many drugs get mixed. Which combinations are good? To answer this question, we set up a stochastic pharmacodynamic model and determine the probability to successfully eradicate a bacterial population. We consider bacteriostatic and two types of bactericidal drugs—those that kill independent of replication and those that kill during replication. To establish results for a null model, we consider non-interacting drugs and implement the two most common models for drug independence—Loewe additivity and Bliss independence. Our results show that combination therapy is almost always better in limiting the evolution of resistance than administering just one drug, even though we keep the total drug dose constant for a ‘fair’ comparison. Yet, exceptions exist for drugs with steep dose–response curves. Combining a bacteriostatic and a bactericidal drug which can kill non-replicating cells is particularly beneficial. Our results suggest that a 50:50 drug ratio—even if not always optimal—is usually a good and safe choice. Applying three or four drugs is beneficial for treatment of strains with large mutation rates but adding more drugs otherwise only provides a marginal benefit or even a disadvantage. By systematically addressing key elements of treatment design, our study provides a basis for future models which take further factors into account. It also highlights conceptual challenges with translating the traditional concepts of drug independence to the single-cell level.

Abstract Image

联合疗法的多个层面:如何联合使用抗生素以限制抗药性的演变。
在联合疗法中,细菌要同时面对两种或两种以上抗生素的挑战。理想情况下,细菌需要单独的突变来适应每一种抗生素,这首先会阻碍耐药性的进化。然而,这一策略的成功与否最终取决于联合疗法对有抗药性突变和无抗药性突变细菌的生长控制效果如何。要设计一种联合疗法,我们需要选择药物及其剂量,并决定混合使用多少种药物。哪些组合是好的?为了回答这个问题,我们建立了一个随机药效学模型,并确定成功消灭细菌种群的概率。我们考虑了抑菌药物和两类杀菌药物--独立于复制的杀菌药物和在复制过程中杀菌的药物。为了确定无效模型的结果,我们考虑了不相互影响的药物,并实施了两种最常见的药物独立性模型--Loewe 可加性和 Bliss 独立性。我们的结果表明,在限制耐药性演变方面,联合疗法几乎总是优于只使用一种药物的疗法,即使我们为了进行 "公平 "比较而保持药物总剂量不变。不过,剂量反应曲线陡峭的药物也有例外。将一种抑菌药物和一种能杀死非复制细胞的杀菌药物结合使用尤其有益。我们的研究结果表明,50:50 的药物配比--即使不一定是最佳配比--通常是一个安全的好选择。使用三种或四种药物有利于治疗变异率高的菌株,但增加药物只会带来微不足道的益处,甚至会带来不利影响。通过系统地解决治疗设计的关键因素,我们的研究为未来建立考虑更多因素的模型奠定了基础。它还凸显了将药物独立性的传统概念转化到单细胞水平所面临的概念挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Evolutionary Applications
Evolutionary Applications 生物-进化生物学
CiteScore
8.50
自引率
7.30%
发文量
175
审稿时长
6 months
期刊介绍: Evolutionary Applications is a fully peer reviewed open access journal. It publishes papers that utilize concepts from evolutionary biology to address biological questions of health, social and economic relevance. Papers are expected to employ evolutionary concepts or methods to make contributions to areas such as (but not limited to): medicine, agriculture, forestry, exploitation and management (fisheries and wildlife), aquaculture, conservation biology, environmental sciences (including climate change and invasion biology), microbiology, and toxicology. All taxonomic groups are covered from microbes, fungi, plants and animals. In order to better serve the community, we also now strongly encourage submissions of papers making use of modern molecular and genetic methods (population and functional genomics, transcriptomics, proteomics, epigenetics, quantitative genetics, association and linkage mapping) to address important questions in any of these disciplines and in an applied evolutionary framework. Theoretical, empirical, synthesis or perspective papers are welcome.
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