. 2014 Nov 7;281(1794):20140566.

doi: 10.1098/rspb.2014.0566.

The path of least resistance: aggressive or moderate treatment?

Roger D Kouyos¹, C Jessica E Metcalf², Ruthie Birger³, Eili Y Klein⁴, Pia Abel zur Wiesch⁵, Peter Ankomah⁶, Nimalan Arinaminpathy⁷, Tiffany L Bogich⁸, Sebastian Bonhoeffer⁹, Charles Brower¹⁰, Geoffrey Chi-Johnston¹¹, Ted Cohen⁵, Troy Day¹², Bryan Greenhouse¹³, Silvie Huijben¹⁴, Joshua Metlay¹⁵, Nicole Mideo¹⁶, Laura C Pollitt¹⁷, Andrew F Read¹⁸, David L Smith¹⁹, Claire Standley²⁰, Nina Wale²¹, Bryan Grenfell⁸

Affiliations

¹ Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zürich, University of Zürich, Zürich, Switzerland.
² Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA Department of Zoology, Oxford University, Oxford, UK.
³ Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA rbirger@princeton.edu.
⁴ Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA Center for Advanced Modeling, Department of Emergency Medicine, Johns Hopkins University, Baltimore, MD, USA.
⁵ Division of Global Health Equity, Brigham and Women's Hospital and Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA.
⁶ Department of Biology, Emory University, Atlanta, GA, USA.
⁷ Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
⁸ Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA Fogarty International Center, National Institutes of Health, Bethesda, MD, USA.
⁹ Institute of Integrative Biology ETH Zurich, Zurich, Switzerland.
¹⁰ Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA Center for Disease Dynamics, Economics & Policy, Washington, DC, USA.
¹¹ Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
¹² Departments of Mathematics and Biology, Queen's University, Kingston, Ontario, Canada.
¹³ Department of Medicine, Division of Infectious Diseases, University of California, San Francisco, VA, USA.
¹⁴ Barcelona Centre for International Health Research, Hospital Clínic, Universitat de Barcelona, Barcelona, Spain.
¹⁵ General Medicine Division, Massachusetts General Hospital, Boston, MA, USA.
¹⁶ Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada.
¹⁷ Centre for Infectious Disease Dynamics, The Pennsylvania State University, University Park, State College, PA, USA Departments of Biology and Entomology, The Pennsylvania State University, University Park, State College, PA, USA Centre for Immunology, Infection and Evolution, University of Edinburgh, Edinburgh, UK.
¹⁸ Centre for Infectious Disease Dynamics, The Pennsylvania State University, University Park, State College, PA, USA Departments of Biology and Entomology, The Pennsylvania State University, University Park, State College, PA, USA Fogarty International Center, National Institutes of Health, Bethesda, MD, USA.
¹⁹ Department of Zoology, Oxford University, Oxford, UK.
²⁰ Department of Health Policy, George Washington University, Washington, DC, USA.
²¹ Centre for Infectious Disease Dynamics, The Pennsylvania State University, University Park, State College, PA, USA Departments of Biology and Entomology, The Pennsylvania State University, University Park, State College, PA, USA.

PMID: 25253451
PMCID: PMC4211439
DOI: 10.1098/rspb.2014.0566

The path of least resistance: aggressive or moderate treatment?

Roger D Kouyos et al. Proc Biol Sci. 2014.

. 2014 Nov 7;281(1794):20140566.

doi: 10.1098/rspb.2014.0566.

Authors

Affiliations

¹ Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zürich, University of Zürich, Zürich, Switzerland.
² Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA Department of Zoology, Oxford University, Oxford, UK.
³ Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA rbirger@princeton.edu.
⁴ Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA Center for Advanced Modeling, Department of Emergency Medicine, Johns Hopkins University, Baltimore, MD, USA.
⁵ Division of Global Health Equity, Brigham and Women's Hospital and Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA.
⁶ Department of Biology, Emory University, Atlanta, GA, USA.
⁷ Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
⁸ Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA Fogarty International Center, National Institutes of Health, Bethesda, MD, USA.
⁹ Institute of Integrative Biology ETH Zurich, Zurich, Switzerland.
¹⁰ Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA Center for Disease Dynamics, Economics & Policy, Washington, DC, USA.
¹¹ Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
¹² Departments of Mathematics and Biology, Queen's University, Kingston, Ontario, Canada.
¹³ Department of Medicine, Division of Infectious Diseases, University of California, San Francisco, VA, USA.
¹⁴ Barcelona Centre for International Health Research, Hospital Clínic, Universitat de Barcelona, Barcelona, Spain.
¹⁵ General Medicine Division, Massachusetts General Hospital, Boston, MA, USA.
¹⁶ Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada.
¹⁷ Centre for Infectious Disease Dynamics, The Pennsylvania State University, University Park, State College, PA, USA Departments of Biology and Entomology, The Pennsylvania State University, University Park, State College, PA, USA Centre for Immunology, Infection and Evolution, University of Edinburgh, Edinburgh, UK.
¹⁸ Centre for Infectious Disease Dynamics, The Pennsylvania State University, University Park, State College, PA, USA Departments of Biology and Entomology, The Pennsylvania State University, University Park, State College, PA, USA Fogarty International Center, National Institutes of Health, Bethesda, MD, USA.
¹⁹ Department of Zoology, Oxford University, Oxford, UK.
²⁰ Department of Health Policy, George Washington University, Washington, DC, USA.
²¹ Centre for Infectious Disease Dynamics, The Pennsylvania State University, University Park, State College, PA, USA Departments of Biology and Entomology, The Pennsylvania State University, University Park, State College, PA, USA.

PMID: 25253451
PMCID: PMC4211439
DOI: 10.1098/rspb.2014.0566

Abstract

The evolution of resistance to antimicrobial chemotherapy is a major and growing cause of human mortality and morbidity. Comparatively little attention has been paid to how different patient treatment strategies shape the evolution of resistance. In particular, it is not clear whether treating individual patients aggressively with high drug dosages and long treatment durations, or moderately with low dosages and short durations can better prevent the evolution and spread of drug resistance. Here, we summarize the very limited available empirical evidence across different pathogens and provide a conceptual framework describing the information required to effectively manage drug pressure to minimize resistance evolution.

Keywords: drug resistance; evolution; treatment strategies.

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Figures

**Figure 1.**
The curve defining resistance evolution as a function of drug pressure. The x-axis is the possible range of drug pressures, measured as either drug dosage, or duration of treatment, with the realistic/neutral range of drug pressures highlighted in green, showing (a) a case where aggressive chemotherapy is likely to be optimal, as there is a level of drug pressure for which the pathogen can be completely cleared and (b) a case where moderate chemotherapy is likely to be optimal for managing resistance, as there is no realistic degree of drug pressure that can clear the pathogen. The y-axis is the rate of resistance emergence, or the inverse of time from introduction of treatment until a resistant strain is established. Numbers 1–3 refer to the three qualitative evolutionary regimes: no evolution of resistance because selection is too weak (1), no evolution of resistance because pathogen cannot replicate (2), maximal speed of resistance evolution (3).

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The path of least resistance: aggressive or moderate treatment?

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