Modelling within-host evolutionary dynamics of antimicrobial resistance
Within-host evolutionary dynamics of antimicrobial quantitative resistance
Recommendation: posted 04 December 2021, validated 13 December 2021
Tsaneva, K. (2021) Modelling within-host evolutionary dynamics of antimicrobial resistance . Peer Community in Mathematical and Computational Biology, 100007. https://doi.org/10.24072/pci.mcb.100007
Antimicrobial resistance (AMR) arises due to two main reasons: pathogens are either intrinsically resistant to the antimicrobials, or they can develop new resistance mechanisms in a continuous fashion over time and space. The latter has been referred to as within-host evolution of antimicrobial resistance and studied in infectious disease settings such as Tuberculosis . During antibiotic treatment for example within-host evolutionary AMR dynamics plays an important role  and presents significant challenges in terms of optimizing treatment dosage. The study by Djidjou-Demasse et al.  contributes to addressing such challenges by developing a modelling approach that utilizes integro-differential equations to mathematically capture continuity in the space of the bacterial resistance levels.
Given its importance as a major public health concern with enormous societal consequences around the world, the evolution of drug resistance in the context of various pathogens has been extensively studied using population genetics approaches . This problem has been also addressed using mathematical modelling approaches including Ordinary Differential Equations (ODE)-based [5. 6] and more recently Stochastic Differential Equations (SDE)-based models . In  the authors propose a model of within-host AMR evolution in the absence and presence of drug treatment. The advantage of the proposed modelling approach is that it allows for AMR to be represented as a continuous quantitative trait, describing the level of resistance of the bacterial population termed quantitative AMR (qAMR) in . Moreover, consistent with recent experimental evidence  integro-differential equations take into account both, the dynamics of the bacterial population density, referred to as “bottleneck size” in  as well as the evolution of its level of resistance due to drug-induced selection.
The model proposed in  has been extensively and rigorously analysed to address various scenarios including the significance of host immune response in drug efficiency, treatment failure and preventive strategies. The drug treatment chosen to be investigated in this study, namely chemotherapy, has been characterised in terms of the level of evolved resistance by the bacterial population in presence of antimicrobial pressure at equilibrium.
Furthermore, the minimal duration of drug administration on bacterial growth and the emergence of AMR has been probed in the model by changing the initial population size and average resistance levels. A potential limitation of the proposed model is the assumption that mutations occur frequently (i.e. during growth), which may not be necessarily the case in certain experimental and/or clinical situations.
 Castro RAD, Borrell S, Gagneux S (2021) The within-host evolution of antimicrobial resistance in Mycobacterium tuberculosis. FEMS Microbiology Reviews, 45, fuaa071. https://doi.org/10.1093/femsre/fuaa071
 Mahrt N, Tietze A, Künzel S, Franzenburg S, Barbosa C, Jansen G, Schulenburg H (2021) Bottleneck size and selection level reproducibly impact evolution of antibiotic resistance. Nature Ecology & Evolution, 5, 1233–1242. https://doi.org/10.1038/s41559-021-01511-2
 Djidjou-Demasse R, Sofonea MT, Choisy M, Alizon S (2021) Within-host evolutionary dynamics of antimicrobial quantitative resistance. HAL, hal-03194023, ver. 4 peer-reviewed and recommended by Peer Community in Mathematical and Computational Biology. https://hal.archives-ouvertes.fr/hal-03194023
 Wilson BA, Garud NR, Feder AF, Assaf ZJ, Pennings PS (2016) The population genetics of drug resistance evolution in natural populations of viral, bacterial and eukaryotic pathogens. Molecular Ecology, 25, 42–66. https://doi.org/10.1111/mec.13474
 Blanquart F, Lehtinen S, Lipsitch M, Fraser C (2018) The evolution of antibiotic resistance in a structured host population. Journal of The Royal Society Interface, 15, 20180040. https://doi.org/10.1098/rsif.2018.0040
 Jacopin E, Lehtinen S, Débarre F, Blanquart F (2020) Factors favouring the evolution of multidrug resistance in bacteria. Journal of The Royal Society Interface, 17, 20200105. https://doi.org/10.1098/rsif.2020.0105
 Igler C, Rolff J, Regoes R (2021) Multi-step vs. single-step resistance evolution under different drugs, pharmacokinetics, and treatment regimens (BS Cooper, PJ Wittkopp, Eds,). eLife, 10, e64116. https://doi.org/10.7554/eLife.64116
The recommender in charge of the evaluation of the article and the reviewers declared that they have no conflict of interest (as defined in the code of conduct of PCI) with the authors or with the content of the article. The authors declared that they comply with the PCI rule of having no financial conflicts of interest in relation to the content of the article.
Evaluation round #2
DOI or URL of the preprint: https://hal.archives-ouvertes.fr/hal-03194023
Version of the preprint: 2
Author's Reply, 01 Nov 2021
Decision by Krasimira Tsaneva, posted 13 Dec 2021
You have now received the second round of review(s) for your manuscript that have identified a couple of outstanding issues which need to be resolved before recommendation for publication.
Could you please address these issues in a revised manuscript and provide detailed response in due course.
Reviewed by anonymous reviewer 2, 18 Oct 2021
Evaluation round #1
DOI or URL of the preprint: https://hal.archives-ouvertes.fr/hal-03194023
Version of the preprint: 1
Author's Reply, 14 Sep 2021
Decision by Krasimira Tsaneva, posted 13 Jun 2021
As with all preprints that have been selected for potential recommendation by the PCI Maths & Comp Biol, your manuscript was reviewed by members of the managing board of PCI Math Comp Biol and by several independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to consider this preprint for recommendation, providing that you modify the manuscript according to the review recommendations.
In particular, this manuscript deals with a complex combination of detailed modelling and biomedical interpretations, and the exact combination of model parameters and biological significance that give rise to each finding are not always immediately obvious to the reader. It is crucial that sufficient detail is provided to allow these simulations (and their results) to be replicated. Hence, I encourage the authors to make a concerted effort to simplify, clarify and improve the comprehensibility of the methods for each set of simulations. I would also strongly recommend that their code is made available online, to further this aim.
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