规则的例外:在人类细菌感染中,抗药性进化何时不会破坏抗生素治疗?

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
ACS Applied Electronic Materials Pub Date : 2024-03-02 eCollection Date: 2024-08-01 DOI:10.1093/evlett/qrae005
Amrita Bhattacharya, Anton Aluquin, David A Kennedy
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引用次数: 0

摘要

使用抗生素治疗细菌感染往往会产生强烈的抗生素耐药性。然而,在病原体和药物的不同组合中,抗生素耐药性的发生率差异很大。是什么导致了这种差异?能够整合多项研究数据的系统综述、荟萃分析和文献调查都曾试图回答这个问题,但这些研究绝大多数只关注耐药性普遍或存在问题的病例。然而,从抗药性不常见或不存在的病例中大概可以学到很多东西。在此,我们进行了文献调查和系统综述,研究了抗生素耐药性在病原体与药物组合(57 种病原体和 15 类 53 种抗生素)中的演变情况。利用基于阿凯克信息准则的模型选择和模型平均参数估计,我们探讨了 14 种假定与耐药性演变相关的不同因素。我们发现,高耐药性最可靠的预测因素是院内传播(即医院获得的病原体)和间接传播(如病媒、水、空气或车辆传播的病原体)。根据以往的研究,前者是意料之中的,但据我们所知,高耐药性频率与间接传播之间的正相关是一种新的见解。低耐药性最可靠的预测因素是来自野生动物储库的人畜共患病。我们还发现部分证据支持抗药性与病原体类型、水平基因转移、共生和人际传播有关。我们没有发现耐药性与环境储库、药物作用机制和全球药物使用之间的相关性。这项工作探讨了各种病原体和药物因素对耐药性演变的相对解释力,这对于确定管理努力的优先目标以减缓耐药性病原体的传播是必要的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exceptions to the rule: When does resistance evolution not undermine antibiotic therapy in human bacterial infections?

The use of antibiotics to treat bacterial infections often imposes strong selection for antibiotic resistance. However, the prevalence of antibiotic resistance varies greatly across different combinations of pathogens and drugs. What underlies this variation? Systematic reviews, meta-analyses, and literature surveys capable of integrating data across many studies have tried to answer this question, but the vast majority of these studies have focused only on cases where resistance is common or problematic. Yet much could presumably be learned from the cases where resistance is infrequent or absent. Here we conducted a literature survey and a systematic review to study the evolution of antibiotic resistance across a wide range of pathogen-by-drug combinations (57 pathogens and 53 antibiotics from 15 drug classes). Using Akaike information criterion-based model selection and model-averaged parameter estimation we explored 14 different factors posited to be associated with resistance evolution. We find that the most robust predictors of high resistance are nosocomial transmission (i.e., hospital-acquired pathogens) and indirect transmission (e.g., vector-, water-, air-, or vehicle-borne pathogens). While the former was to be expected based on prior studies, the positive correlation between high resistance frequencies and indirect transmission is, to our knowledge, a novel insight. The most robust predictor of low resistance is zoonosis from wild animal reservoirs. We also found partial support that resistance was associated with pathogen type, horizontal gene transfer, commensalism, and human-to-human transmission. We did not find support for correlations between resistance and environmental reservoirs, mechanisms of drug action, and global drug use. This work explores the relative explanatory power of various pathogen and drug factors on resistance evolution, which is necessary to identify priority targets of stewardship efforts to slow the spread of drug-resistant pathogens.

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CiteScore
7.20
自引率
4.30%
发文量
567
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