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A survey of within-host and between-hosts modelling for antibiotic resistance.
Biosystems ( IF 2.0 ) Pub Date : 2020-06-07 , DOI: 10.1016/j.biosystems.2020.104182
Josephine N A Tetteh 1 , Franziska Matthäus 2 , Esteban A Hernandez-Vargas 3
Affiliation  

Antibiotic resistance is a global public health problem which has the attention of many stakeholders including clinicians, the pharmaceutical industry, researchers and policy makers. Despite the existence of many studies, control of resistance transmission has become a rather daunting task as the mechanisms underlying resistance evolution and development are not fully known.

Here, we discuss the mechanisms underlying antibiotic resistance development, explore some treatment strategies used in the fight against antibiotic resistance and consider recent findings on collateral susceptibilities amongst antibiotic classes. Mathematical models have proved valuable for unravelling complex mechanisms in biology and such models have been used in the quest of understanding the development and spread of antibiotic resistance. While assessing the importance of such mathematical models, previous systematic reviews were interested in investigating whether these models follow good modelling practice. We focus on theoretical approaches used for resistance modelling considering both within and between host models as well as some pharmacodynamic and pharmakokinetic approaches and further examine the interaction between drugs and host immune response during treatment with antibiotics. Finally, we provide an outlook for future research aimed at modelling approaches for combating antibiotic resistance.



中文翻译:

宿主内部和宿主之间对抗生素耐药性的模型研究。

抗生素耐药性是一个全球性的公共卫生问题,引起了包括临床医生,制药行业,研究人员和决策者在内的许多利益相关者的关注。尽管存在许多研究,但是由于尚未完全了解抗性进化和发展的机制,因此控制抗性传播已成为一项艰巨的任务。

在这里,我们讨论了抗生素耐药性发展的潜在机制,探讨了一些与抗生素耐药性作斗争的治疗策略,并考虑了最近在抗生素类别中附带敏感性的发现。数学模型已被证明对于揭示生物学中的复杂机制非常有价值,并且此类模型已用于寻求理解抗生素抗性的发展和传播的过程。在评估此类数学模型的重要性时,以前的系统评价对调查这些模型是否遵循良好的建模实践很感兴趣。我们着重于用于宿主模型内部和之间的耐药性建模的理论方法,以及一些药代动力学和药代动力学方法,并进一步研究了抗生素治疗期间药物与宿主免疫反应之间的相互作用。最后,我们为旨在模拟抗生素抗性方法的未来研究提供了展望。

更新日期:2020-06-07
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