Zwep Laura B, Haakman Yob, Duisters Kevin L W, Meulman Jacqueline J, Liakopoulos Apostolos, van Hasselt J G Coen
Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands.
Mathematical Institute, Leiden University, Leiden, The Netherlands.
JAC Antimicrob Resist. 2021 Nov 28;3(4):dlab175. doi: 10.1093/jacamr/dlab175. eCollection 2021 Dec.
Collateral effects of antibiotic resistance occur when resistance to one antibiotic agent leads to increased resistance or increased sensitivity to a second agent, known respectively as collateral resistance (CR) and collateral sensitivity (CS). Collateral effects are relevant to limit impact of antibiotic resistance in design of antibiotic treatments. However, methods to detect antibiotic collateral effects in clinical population surveillance data of antibiotic resistance are lacking.
To develop a methodology to quantify collateral effect directionality and effect size from large-scale antimicrobial resistance population surveillance data.
We propose a methodology to quantify and test collateral effects in clinical surveillance data based on a conditional t-test. Our methodology was evaluated using MIC data for 419 strains, containing MIC data for 20 antibiotics, which were obtained from the Pathosystems Resource Integration Center (PATRIC) database.
We demonstrate that the proposed approach identifies several antibiotic combinations that show symmetrical or non-symmetrical CR and CS. For several of these combinations, collateral effects were previously confirmed in experimental studies. We furthermore provide insight into the power of our method for multiple collateral effect sizes and MIC distributions.
Our proposed approach is of relevance as a tool for analysis of large-scale population surveillance studies to provide broad systematic identification of collateral effects related to antibiotic resistance, and is made available to the community as an R package. This method can help mapping CS and CR, which could guide combination therapy and prescribing in the future.
当对一种抗生素的耐药性导致对另一种抗生素的耐药性增加或敏感性增加时，就会出现抗生素耐药性的附带效应，分别称为附带耐药性（CR）和附带敏感性（CS）。附带效应与在抗生素治疗设计中限制抗生素耐药性的影响相关。然而，在抗生素耐药性的临床人群监测数据中缺乏检测抗生素附带效应的方法。
开发一种从大规模抗菌药物耐药性人群监测数据中量化附带效应方向性和效应大小的方法。
我们提出了一种基于条件t检验在临床监测数据中量化和测试附带效应的方法。我们使用从病原体系统资源整合中心（PATRIC）数据库获得的419株菌株的MIC数据对我们的方法进行了评估，这些数据包含20种抗生素的MIC数据。
我们证明，所提出的方法识别出了几种显示对称或非对称CR和CS的抗生素组合。对于其中几种组合，附带效应先前已在实验研究中得到证实。我们还深入了解了我们的方法对于多种附带效应大小和MIC分布的效能。
我们提出的方法作为分析大规模人群监测研究的工具具有相关性，可广泛系统地识别与抗生素耐药性相关的附带效应，并作为一个R包提供给社区。这种方法有助于绘制CS和CR图谱，这可能会在未来指导联合治疗和处方。
