Rotating antibiotics selects optimally against antibiotic resistance, in theory

  • Received: 01 September 2009 Accepted: 29 June 2018 Published: 01 June 2010
  • MSC : 93C15, 92B05.

  • The purpose of this paper is to use mathematical models to investigate the claim made in the medical literature over a decade ago that the routine rotation of antibiotics in an intensive care unit (ICU) will select against the evolution and spread of antibiotic-resistant pathogens. In contrast, previous theoretical studies addressing this question have demonstrated that routinely changing the drug of choice for a given pathogenic infection may in fact lead to a greater incidence of drug resistance in comparison to the random deployment of different drugs.
       Using mathematical models that do not explicitly incorporate the spatial dynamics of pathogen transmission within the ICU or hospital and assuming the antibiotics are from distinct functional groups, we use a control theoretic-approach to prove that one can relax the medical notion of what constitutes an antibiotic rotation and so obtain protocols that are arbitrarily close to the optimum. Finally, we show that theoretical feedback control measures that rotate between different antibiotics motivated directly by the outcome of clinical studies can be deployed to good effect to reduce the prevalence of antibiotic resistance below what can be achieved with random antibiotic use.

    Citation: Robert E. Beardmore, Rafael Peña-Miller. Rotating antibiotics selects optimally against antibiotic resistance, in theory[J]. Mathematical Biosciences and Engineering, 2010, 7(3): 527-552. doi: 10.3934/mbe.2010.7.527

    Related Papers:

    [1] Sebastian Bonhoeffer, Pia Abel zur Wiesch, Roger D. Kouyos . Rotating antibiotics does not minimize selection for resistance. Mathematical Biosciences and Engineering, 2010, 7(4): 919-922. doi: 10.3934/mbe.2010.7.919
    [2] Robert E. Beardmore, Rafael Peña-Miller . Antibiotic cycling versus mixing: The difficulty of using mathematical models to definitively quantify their relative merits. Mathematical Biosciences and Engineering, 2010, 7(4): 923-933. doi: 10.3934/mbe.2010.7.923
    [3] Jing Jia, Yanfeng Zhao, Zhong Zhao, Bing Liu, Xinyu Song, Yuanxian Hui . Dynamics of a within-host drug resistance model with impulsive state feedback control. Mathematical Biosciences and Engineering, 2023, 20(2): 2219-2231. doi: 10.3934/mbe.2023103
    [4] Xiaxia Kang, Jie Yan, Fan Huang, Ling Yang . On the mechanism of antibiotic resistance and fecal microbiota transplantation. Mathematical Biosciences and Engineering, 2019, 16(6): 7057-7084. doi: 10.3934/mbe.2019354
    [5] Xiaoxiao Yan, Zhong Zhao, Yuanxian Hui, Jingen Yang . Dynamic analysis of a bacterial resistance model with impulsive state feedback control. Mathematical Biosciences and Engineering, 2023, 20(12): 20422-20436. doi: 10.3934/mbe.2023903
    [6] Michele L. Joyner, Cammey C. Manning, Brandi N. Canter . Modeling the effects of introducing a new antibiotic in a hospital setting: A case study. Mathematical Biosciences and Engineering, 2012, 9(3): 601-625. doi: 10.3934/mbe.2012.9.601
    [7] Hermann Mena, Lena-Maria Pfurtscheller, Jhoana P. Romero-Leiton . Random perturbations in a mathematical model of bacterial resistance: Analysis and optimal control. Mathematical Biosciences and Engineering, 2020, 17(5): 4477-4499. doi: 10.3934/mbe.2020247
    [8] Qimin Huang, Mary Ann Horn, Shigui Ruan . Modeling the effect of antibiotic exposure on the transmission of methicillin-resistant Staphylococcus aureus in hospitals with environmental contamination. Mathematical Biosciences and Engineering, 2019, 16(5): 3641-3673. doi: 10.3934/mbe.2019181
    [9] Avner Friedman, Najat Ziyadi, Khalid Boushaba . A model of drug resistance with infection by health care workers. Mathematical Biosciences and Engineering, 2010, 7(4): 779-792. doi: 10.3934/mbe.2010.7.779
    [10] Mudassar Imran, Hal L. Smith . A model of optimal dosing of antibiotic treatment in biofilm. Mathematical Biosciences and Engineering, 2014, 11(3): 547-571. doi: 10.3934/mbe.2014.11.547
  • The purpose of this paper is to use mathematical models to investigate the claim made in the medical literature over a decade ago that the routine rotation of antibiotics in an intensive care unit (ICU) will select against the evolution and spread of antibiotic-resistant pathogens. In contrast, previous theoretical studies addressing this question have demonstrated that routinely changing the drug of choice for a given pathogenic infection may in fact lead to a greater incidence of drug resistance in comparison to the random deployment of different drugs.
       Using mathematical models that do not explicitly incorporate the spatial dynamics of pathogen transmission within the ICU or hospital and assuming the antibiotics are from distinct functional groups, we use a control theoretic-approach to prove that one can relax the medical notion of what constitutes an antibiotic rotation and so obtain protocols that are arbitrarily close to the optimum. Finally, we show that theoretical feedback control measures that rotate between different antibiotics motivated directly by the outcome of clinical studies can be deployed to good effect to reduce the prevalence of antibiotic resistance below what can be achieved with random antibiotic use.


  • This article has been cited by:

    1. A. A. Cheng, H. Ding, T. K. Lu, Enhanced killing of antibiotic-resistant bacteria enabled by massively parallel combinatorial genetics, 2014, 111, 0027-8424, 12462, 10.1073/pnas.1400093111
    2. Nienke L Plantinga, Bastiaan HJ Wittekamp, Pleun J van Duijn, Marc JM Bonten, Fighting antibiotic resistance in the intensive care unit using antibiotics, 2015, 10, 1746-0913, 391, 10.2217/fmb.14.146
    3. Gabriel G. Perron, Sergey Kryazhimskiy, Daniel P. Rice, Angus Buckling, Multidrug Therapy and Evolution of Antibiotic Resistance: When Order Matters, 2012, 78, 0099-2240, 6137, 10.1128/AEM.01078-12
    4. Uri Obolski, Lilach Hadany, Implications of stress-induced genetic variation for minimizing multidrug resistance in bacteria, 2012, 10, 1741-7015, 10.1186/1741-7015-10-89
    5. François Blanquart, Evolutionary epidemiology models to predict the dynamics of antibiotic resistance, 2019, 12, 1752-4571, 365, 10.1111/eva.12753
    6. Antonio L. C. Gomes, James E. Galagan, Daniel Segrè, James M. McCaw, Resource Competition May Lead to Effective Treatment of Antibiotic Resistant Infections, 2013, 8, 1932-6203, e80775, 10.1371/journal.pone.0080775
    7. David McAdams, Kristofer Wollein Waldetoft, Christine Tedijanto, Marc Lipsitch, Sam P. Brown, Andrew Fraser Read, Resistance diagnostics as a public health tool to combat antibiotic resistance: A model-based evaluation, 2019, 17, 1545-7885, e3000250, 10.1371/journal.pbio.3000250
    8. Nicolas Houy, Julien Flaig, Informed and uninformed empirical therapy policies, 2020, 37, 1477-8599, 334, 10.1093/imammb/dqz015
    9. Philipp Schuetz, Robert Eric Beardmore, Antibiotic strategies in critical care: back to square one?, 2018, 18, 14733099, 360, 10.1016/S1473-3099(18)30057-4
    10. Roger D. Kouyos, Pia Abel zur Wiesch, Sebastian Bonhoeffer, Christophe Fraser, Informed Switching Strongly Decreases the Prevalence of Antibiotic Resistance in Hospital Wards, 2011, 7, 1553-7358, e1001094, 10.1371/journal.pcbi.1001094
    11. Portia M. Mira, Kristina Crona, Devin Greene, Juan C. Meza, Bernd Sturmfels, Miriam Barlow, Paul J Planet, Rational Design of Antibiotic Treatment Plans: A Treatment Strategy for Managing Evolution and Reversing Resistance, 2015, 10, 1932-6203, e0122283, 10.1371/journal.pone.0122283
    12. Nicolas Houy, Julien Flaig, Hospital-wide surveillance-based antimicrobial treatments: a Monte-Carlo look-ahead method, 2021, 01692607, 106050, 10.1016/j.cmpb.2021.106050
    13. Gabriel G. Perron, R. Fredrik Inglis, Pleuni S. Pennings, Sarah Cobey, Fighting microbial drug resistance: a primer on the role of evolutionary biology in public health, 2015, 8, 1752-4571, 211, 10.1111/eva.12254
    14. Christiane P. Goulart, Mentar Mahmudi, Kristina A. Crona, Stephen D. Jacobs, Marcelo Kallmann, Barry G. Hall, Devin C. Greene, Miriam Barlow, Norman Johnson, Designing Antibiotic Cycling Strategies by Determining and Understanding Local Adaptive Landscapes, 2013, 8, 1932-6203, e56040, 10.1371/journal.pone.0056040
    15. Ellsworth M. Campbell, Lin Chao, Bryan A. White, A Population Model Evaluating the Consequences of the Evolution of Double-Resistance and Tradeoffs on the Benefits of Two-Drug Antibiotic Treatments, 2014, 9, 1932-6203, e86971, 10.1371/journal.pone.0086971
    16. Nicolas Houy, Julien Flaig, Optimal dynamic empirical therapy in a health care facility: A Monte-Carlo look-ahead method, 2021, 198, 01692607, 105767, 10.1016/j.cmpb.2020.105767
    17. Pleun J van Duijn, Marc JM Bonten, Antibiotic rotation strategies to reduce antimicrobial resistance in Gram-negative bacteria in European intensive care units: study protocol for a cluster-randomized crossover controlled trial, 2014, 15, 1745-6215, 10.1186/1745-6215-15-277
    18. Esther van Kleef, Julie V Robotham, Mark Jit, Sarah R Deeny, William J Edmunds, Modelling the transmission of healthcare associated infections: a systematic review, 2013, 13, 1471-2334, 10.1186/1471-2334-13-294
    19. Chang-Ro Lee, Ill Cho, Byeong Jeong, Sang Lee, Strategies to Minimize Antibiotic Resistance, 2013, 10, 1660-4601, 4274, 10.3390/ijerph10094274
    20. Francisco Pimenta, Ana Cristina Abreu, Lúcia Chaves Simões, Manuel Simões, What should be considered in the treatment of bacterial infections by multi-drug therapies: A mathematical perspective?, 2014, 17, 13687646, 51, 10.1016/j.drup.2014.08.001
    21. D. E. Ramsay, J. Invik, S. L. Checkley, S. P. Gow, N. D. Osgood, C. L. Waldner, Application of dynamic modelling techniques to the problem of antibacterial use and resistance: a scoping review, 2018, 146, 0950-2688, 2014, 10.1017/S0950268818002091
    22. Daniel C. Angst, Burcu Tepekule, Lei Sun, Balázs Bogos, Sebastian Bonhoeffer, Comparing treatment strategies to reduce antibiotic resistance in an in vitro epidemiological setting, 2021, 118, 0027-8424, e2023467118, 10.1073/pnas.2023467118
    23. Hildegard Uecker, Sebastian Bonhoeffer, Antibiotic treatment protocols revisited: the challenges of a conclusive assessment by mathematical modelling, 2021, 18, 1742-5662, 20210308, 10.1098/rsif.2021.0308
    24. Daria Roithmayr, Justin Chin, Fei Fang, Bruce Levin, 2021, Chapter 6, 978-3-030-77516-2, 73, 10.1007/978-3-030-77517-9_6
    25. Pirommas Techitnutsarut, Farida Chamchod, Modeling bacterial resistance to antibiotics: bacterial conjugation and drug effects, 2021, 2021, 1687-1847, 10.1186/s13662-021-03423-8
    26. Alastair Jamieson-Lane, Alexander Friedrich, Bernd Blasius, Comparing optimization criteria in antibiotic allocation protocols, 2022, 9, 2054-5703, 10.1098/rsos.220181
    27. Pleun J. van Duijn, Walter Verbrugghe, Philippe G. Jorens, Fabian Spöhr, Dirk Schedler, Maria Deja, Andreas Rothbart, Djillali Annane, Christine Lawrence, Matjaz Jereb, Katja Seme, Franc Šifrer, Viktorija Tomič, Francisco Estevez, Jandira Carneiro, Stephan Harbarth, Marc J. M. Bonten, Dafna Yahav, The effects of antibiotic cycling and mixing on acquisition of antibiotic resistant bacteria in the ICU: A post-hoc individual patient analysis of a prospective cluster-randomized crossover study, 2022, 17, 1932-6203, e0265720, 10.1371/journal.pone.0265720
    28. Ali Hosiani, James Brown, Indika T. Alahakoon, Giovanni Monni, Delayed Interval Delivery in Preterm Premature Rupture of Membranes in Dichorionic Triamniotic Triplets: Ethical Considerations for Maternal Health Case Report, 2022, 2022, 2090-6692, 1, 10.1155/2022/4766523
  • Reader Comments
  • © 2010 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(3599) PDF downloads(627) Cited by(27)

Article outline

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog