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TSANEVA Krasimira

  • Department of Mathematics and the Living Systems Institute, University of Exeter, Exeter, United Kingdom
  • Cell Biology, Dynamical systems, Medical Sciences, Neuroscience, Physiology, Systems biology
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Recommendation:  1

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Educational and work
Krasimira Tsaneva-Atanasova is Professor of Mathematics for Healthcare in the Living Systems Institute and the Department of Mathematics at the University of Exeter (UoE), UK. She received her MSc (Mathematics) from the University of Plovdiv, Bulgaria and PhD (Applied Mathematica) from the University of Auckland, New Zealand. Krasimira spent 18 months as a post-doctoral fellow at the Laboratory of Biological Modelling, National Institutes of Health, USA and another 15 months as a post-doctoral fellow at the Department of Mathematics and the Department of Biology at Ecole Normale Superieure in Paris, France. She joined the Department of Engineering Mathematics at the University of Bristol in October 2007 as a lecturer and was promoted to a Reader in Applied Mathematics in 2012. In 2013 Krasimira moved to the University of Exeter where she currently serves as Associate Dean - Global in the College of Engineering, Mathematics and Physical Sciences at UoE (2018-2021). Her research and professional activities aim to inform novel applications of mathematics to enable the development of quantitative methods for healthcare and healthcare technologies. In her research, she develops and analyses mathematical models for applications to personalised prediction and clinical decision support in prevention, diagnosis or treatment of health-related conditions. Krasimira is also a Technical University of Munich (TUM) Institute for Advanced Study (IAS) Hans Fisher Senior fellow (2019-2022) and an associate member of the Bulgarian Academy of Sciences, Institute of Biophysics and Biomedical Engineering, section Bioinformatics and Mathematical Modelling (2020- ).

Recommendation:  1

13 Dec 2021
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Within-host evolutionary dynamics of antimicrobial quantitative resistance

Modelling within-host evolutionary dynamics of antimicrobial resistance

Recommended by based on reviews by 2 anonymous reviewers

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 [1]. During antibiotic treatment for example within-host evolutionary AMR dynamics plays an important role [2] and presents significant challenges in terms of optimizing treatment dosage. The study by Djidjou-Demasse et al. [3] 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 [4]. 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 [7]. In [3] 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 [3]. Moreover, consistent with recent experimental evidence [2] integro-differential equations take into account both, the dynamics of the bacterial population density, referred to as “bottleneck size” in [2] as well as the evolution of its level of resistance due to drug-induced selection. 

The model proposed in [3] 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.

References

[1] 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

[2] 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

[3] 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

[4] 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

[5] 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

[6] 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

[7] 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

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TSANEVA Krasimira

  • Department of Mathematics and the Living Systems Institute, University of Exeter, Exeter, United Kingdom
  • Cell Biology, Dynamical systems, Medical Sciences, Neuroscience, Physiology, Systems biology
  • recommender

Recommendation:  1

Reviews:  0

Educational and work
Krasimira Tsaneva-Atanasova is Professor of Mathematics for Healthcare in the Living Systems Institute and the Department of Mathematics at the University of Exeter (UoE), UK. She received her MSc (Mathematics) from the University of Plovdiv, Bulgaria and PhD (Applied Mathematica) from the University of Auckland, New Zealand. Krasimira spent 18 months as a post-doctoral fellow at the Laboratory of Biological Modelling, National Institutes of Health, USA and another 15 months as a post-doctoral fellow at the Department of Mathematics and the Department of Biology at Ecole Normale Superieure in Paris, France. She joined the Department of Engineering Mathematics at the University of Bristol in October 2007 as a lecturer and was promoted to a Reader in Applied Mathematics in 2012. In 2013 Krasimira moved to the University of Exeter where she currently serves as Associate Dean - Global in the College of Engineering, Mathematics and Physical Sciences at UoE (2018-2021). Her research and professional activities aim to inform novel applications of mathematics to enable the development of quantitative methods for healthcare and healthcare technologies. In her research, she develops and analyses mathematical models for applications to personalised prediction and clinical decision support in prevention, diagnosis or treatment of health-related conditions. Krasimira is also a Technical University of Munich (TUM) Institute for Advanced Study (IAS) Hans Fisher Senior fellow (2019-2022) and an associate member of the Bulgarian Academy of Sciences, Institute of Biophysics and Biomedical Engineering, section Bioinformatics and Mathematical Modelling (2020- ).