1
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Martinecz A, Boeree MJ, Diacon AH, Dawson R, Hemez C, Aarnoutse RE, Abel Zur Wiesch P. High rifampicin peak plasma concentrations accelerate the slow phase of bacterial decline in tuberculosis patients: Evidence for heteroresistance. PLoS Comput Biol 2023; 19:e1011000. [PMID: 37053266 PMCID: PMC10128972 DOI: 10.1371/journal.pcbi.1011000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 04/25/2023] [Accepted: 03/06/2023] [Indexed: 04/15/2023] Open
Abstract
BACKGROUND Antibiotic treatments are often associated with a late slowdown in bacterial killing. This separates the killing of bacteria into at least two distinct phases: a quick phase followed by a slower phase, the latter of which is linked to treatment success. Current mechanistic explanations for the in vitro slowdown are either antibiotic persistence or heteroresistance. Persistence is defined as the switching back and forth between susceptible and non-susceptible states, while heteroresistance is defined as the coexistence of bacteria with heterogeneous susceptibilities. Both are also thought to cause a slowdown in the decline of bacterial populations in patients and therefore complicate and prolong antibiotic treatments. Reduced bacterial death rates over time are also observed within tuberculosis patients, yet the mechanistic reasons for this are unknown and therefore the strategies to mitigate them are also unknown. METHODS AND FINDINGS We analyse a dose ranging trial for rifampicin in tuberculosis patients and show that there is a slowdown in the decline of bacteria. We show that the late phase of bacterial killing depends more on the peak drug concentrations than the total drug exposure. We compare these to pharmacokinetic-pharmacodynamic models of rifampicin heteroresistance and persistence. We find that the observation on the slow phase's dependence on pharmacokinetic measures, specifically peak concentrations are only compatible with models of heteroresistance and incompatible with models of persistence. The quantitative agreement between heteroresistance models and observations is very good ([Formula: see text]). To corroborate the importance of the slowdown, we validate our results by estimating the time to sputum culture conversion and compare the results to a different dose ranging trial. CONCLUSIONS Our findings indicate that higher doses, specifically higher peak concentrations may be used to optimize rifampicin treatments by accelerating bacterial killing in the slow phase. It adds to the growing body of literature supporting higher rifampicin doses for shortening tuberculosis treatments.
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Affiliation(s)
- Antal Martinecz
- Department of Pharmacy, Faculty of Health Sciences, University of Tromsø, Tromsø, Norway
- Center for Infectious Disease Dynamics, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Martin J Boeree
- Department of Lung Diseases, Radboud Institute for Health Sciences, Radboud university medical center, Nijmegen, the Netherlands
| | - Andreas H Diacon
- Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, South Africa
- TASK Applied Science, Cape Town, South Africa
| | - Rodney Dawson
- Division of Pulmonology and Department of Medicine, University of Cape Town, Cape Town, South Africa
- University of Cape Town Lung Institute, Cape Town, South Africa
| | - Colin Hemez
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Graduate program in Biophysics, Harvard University, Boston, Massachusetts, United States of America
| | - Rob E Aarnoutse
- Department of Pharmacy, Radboud Institute for Health Sciences, Radboud university medical center, Nijmegen, The Netherlands
| | - Pia Abel Zur Wiesch
- Department of Pharmacy, Faculty of Health Sciences, University of Tromsø, Tromsø, Norway
- Center for Infectious Disease Dynamics, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- Department of Biology, Eberly College of Science, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- Norwegian Institute of Public Health (Folkehelseinstitutt), Oslo, Norway
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2
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Koehler N, Andres S, Merker M, Dreyer V, John A, Kuhns M, Krieger D, Choong E, Verougstraete N, Zur Wiesch PA, Wicha SG, König C, Kalsdorf B, Sanchez Carballo PM, Schaub D, Werngren J, Schön T, Peloquin CA, Schönfeld N, Verstraete AG, Decosterd LA, Aarnoutse R, Niemann S, Maurer FP, Lange C. Pretomanid-resistant tuberculosis. J Infect 2023; 86:520-524. [PMID: 36738862 DOI: 10.1016/j.jinf.2023.01.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 01/23/2023] [Accepted: 01/30/2023] [Indexed: 02/05/2023]
Affiliation(s)
- Niklas Koehler
- Department of Clinical Infectious Diseases, Research Center Borstel, Leibniz Lung Center, Parkallee 35, 23845 Borstel, Germany; German Center for Infection Research (DZIF), Partner Site Borstel-Hamburg-Lübeck-Riems, Parkallee 1-40, 23845 Borstel, Germany; Respiratory Medicine & International Health, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Sönke Andres
- National and World Health Organization Supranational Reference Laboratory for Mycobacteria, Research Center Borstel, Parkallee 18, 23845 Borstel, Germany
| | - Matthias Merker
- German Center for Infection Research (DZIF), Partner Site Borstel-Hamburg-Lübeck-Riems, Parkallee 1-40, 23845 Borstel, Germany; Evolution of the Resistome, Research Center Borstel, Parkallee 1, 23845 Borstel, Germany
| | - Viola Dreyer
- German Center for Infection Research (DZIF), Partner Site Borstel-Hamburg-Lübeck-Riems, Parkallee 1-40, 23845 Borstel, Germany; Molecular and Experimental Mycobacteriology, National Reference Center for Mycobacteria, Research Center Borstel, Parkallee 1, 23845 Borstel, Germany
| | - Agnieszka John
- Department of Clinical Infectious Diseases, Research Center Borstel, Leibniz Lung Center, Parkallee 35, 23845 Borstel, Germany
| | - Martin Kuhns
- National and World Health Organization Supranational Reference Laboratory for Mycobacteria, Research Center Borstel, Parkallee 18, 23845 Borstel, Germany
| | - David Krieger
- Department of Pulmonology, Lungenklinik Heckeshorn, HELIOS Klinikum Emil von Behring, Walterhöferstraße 11, 14165 Berlin, Germany
| | - Eva Choong
- Laboratory of Clinical Pharmacology, Department of Laboratory Medicine and Pathology, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 46, 1011 Lausanne, Switzerland
| | - Nick Verougstraete
- Department of Laboratory Medicine, Ghent University Hospital, Corneel Heymanslaan 10, 9000 Ghent, Belgium
| | - Pia Abel Zur Wiesch
- Department of Pharmacy, Faculty of Health Sciences, UiT - The Arctic University of Norway, Hansine Hansens veg 18, 9019 Tromsø, Norway; Centre for Molecular Medicine Norway, Nordic EMBL Partnership, Forskningsparken, Gaustadalléen 21, 0349 Oslo, Norway; Department of Biology, The Pennsylvania State University, University Park Pennsylvania, Mueller Laboratory, 208 Curtin Rd, State College, PA 16801, USA; Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park Pennsylvania, 101 Huck Life Sciences Building, University Park, PA 16802, USA
| | - Sebastian G Wicha
- Institute of Pharmacy, University of Hamburg, Bundesstraße 45, 20146 Hamburg, Germany
| | - Christina König
- Department of Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, Martinstraße 52, 20246 Hamburg, Germany; Department of Pharmacy, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20251 Hamburg, Germany
| | - Barbara Kalsdorf
- Department of Clinical Infectious Diseases, Research Center Borstel, Leibniz Lung Center, Parkallee 35, 23845 Borstel, Germany; German Center for Infection Research (DZIF), Partner Site Borstel-Hamburg-Lübeck-Riems, Parkallee 1-40, 23845 Borstel, Germany; Respiratory Medicine & International Health, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Patricia M Sanchez Carballo
- Department of Clinical Infectious Diseases, Research Center Borstel, Leibniz Lung Center, Parkallee 35, 23845 Borstel, Germany; German Center for Infection Research (DZIF), Partner Site Borstel-Hamburg-Lübeck-Riems, Parkallee 1-40, 23845 Borstel, Germany; Respiratory Medicine & International Health, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Dagmar Schaub
- Department of Clinical Infectious Diseases, Research Center Borstel, Leibniz Lung Center, Parkallee 35, 23845 Borstel, Germany; German Center for Infection Research (DZIF), Partner Site Borstel-Hamburg-Lübeck-Riems, Parkallee 1-40, 23845 Borstel, Germany; Respiratory Medicine & International Health, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Jim Werngren
- Department of Microbiology, Unit for Laboratory Surveillance of Bacterial Pathogens, Public Health Agency of Sweden, Nobels väg 18, 171 65 Solna, Sweden
| | - Thomas Schön
- Department of Infectious Diseases, Linköping University Hospital, Universitetssjukhuset, 581 85 Linköping, Sweden; Department of Biomedical and Clinical Sciences, Division of Inflammation and Infection, Linköping University, Universitetssjukhuset, 581 85 Linköping, Sweden
| | - Charles A Peloquin
- Infectious Disease Pharmacokinetics Laboratory, Emerging Pathogens Institute, University of Florida, 2055 Mowry Rd, Gainesville, FL 32610, USA; Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, 1225 Center Dr, Gainesville, FL 32610, USA
| | - Nicolas Schönfeld
- Department of Pulmonology, Lungenklinik Heckeshorn, HELIOS Klinikum Emil von Behring, Walterhöferstraße 11, 14165 Berlin, Germany
| | - Alain G Verstraete
- Department of Laboratory Medicine, Ghent University Hospital, Corneel Heymanslaan 10, 9000 Ghent, Belgium; Department of Diagnostic Sciences, Ghent University, Corneel Heymanslaan 10, 9000 Ghent, Belgium
| | - Laurent A Decosterd
- Laboratory of Clinical Pharmacology, Department of Laboratory Medicine and Pathology, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 46, 1011 Lausanne, Switzerland
| | - Rob Aarnoutse
- Department of Pharmacy, Radboud University Medical Center, Radboud Institute for Health Sciences, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, the Netherlands
| | - Stefan Niemann
- German Center for Infection Research (DZIF), Partner Site Borstel-Hamburg-Lübeck-Riems, Parkallee 1-40, 23845 Borstel, Germany; National and World Health Organization Supranational Reference Laboratory for Mycobacteria, Research Center Borstel, Parkallee 18, 23845 Borstel, Germany; Molecular and Experimental Mycobacteriology, National Reference Center for Mycobacteria, Research Center Borstel, Parkallee 1, 23845 Borstel, Germany
| | - Florian P Maurer
- National and World Health Organization Supranational Reference Laboratory for Mycobacteria, Research Center Borstel, Parkallee 18, 23845 Borstel, Germany; Institute for Medical Microbiology, Virology and Hygiene, University Medical Center Hamburg-Eppendorf, Martinstraße 52, 20246 Hamburg, Germany
| | - Christoph Lange
- Department of Clinical Infectious Diseases, Research Center Borstel, Leibniz Lung Center, Parkallee 35, 23845 Borstel, Germany; German Center for Infection Research (DZIF), Partner Site Borstel-Hamburg-Lübeck-Riems, Parkallee 1-40, 23845 Borstel, Germany; Respiratory Medicine & International Health, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany; Baylor College of Medicine and Texas Childrens' Hospital, 1 Baylor Plaza, Houston, TX 77030, USA.
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3
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Liang J, Tran VNN, Hemez C, Abel Zur Wiesch P. Current Approaches of Building Mechanistic Pharmacodynamic Drug-Target Binding Models. Methods Mol Biol 2022; 2385:1-17. [PMID: 34888713 DOI: 10.1007/978-1-0716-1767-0_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Mechanistic pharmacodynamic models that incorporate the binding kinetics of drug-target interactions have several advantages in understanding target engagement and the efficacy of a drug dose. However, guidelines on how to build and interpret mechanistic pharmacodynamic drug-target binding models considering both biological and computational factors are still missing in the literature. In this chapter, current approaches of building mechanistic PD models and their advantages are discussed. We also present a methodology on how to select a suitable model considering both biological and computational perspectives, as well as summarize the challenges of current mechanistic PD models.
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Affiliation(s)
- Jingyi Liang
- Department of Pharmacy, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway
- Department of Biology, Eberly College of Science, The Pennsylvania State University, University Park, PA, USA
- Center for Infectious Disease Dynamics, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, USA
| | - Vi Ngoc-Nha Tran
- Department of Pharmacy, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway
| | - Colin Hemez
- Graduate Program in Biophysics, Harvard University, Boston, MA, USA
| | - Pia Abel Zur Wiesch
- Department of Pharmacy, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway.
- Department of Biology, Eberly College of Science, The Pennsylvania State University, University Park, PA, USA.
- Center for Infectious Disease Dynamics, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, USA.
- Centre for Molecular Medicine Norway, Nordic EMBL Partnership, Blindern, Oslo, Norway.
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4
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Kontsevaya I, Lange C, Comella-Del-Barrio P, Coarfa C, DiNardo AR, Gillespie SH, Hauptmann M, Leschczyk C, Mandalakas AM, Martinecz A, Merker M, Niemann S, Reimann M, Rzhepishevska O, Schaible UE, Scheu KM, Schurr E, Abel Zur Wiesch P, Heyckendorf J. Perspectives for systems biology in the management of tuberculosis. Eur Respir Rev 2021; 30:30/160/200377. [PMID: 34039674 DOI: 10.1183/16000617.0377-2020] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 01/28/2021] [Indexed: 12/18/2022] Open
Abstract
Standardised management of tuberculosis may soon be replaced by individualised, precision medicine-guided therapies informed with knowledge provided by the field of systems biology. Systems biology is a rapidly expanding field of computational and mathematical analysis and modelling of complex biological systems that can provide insights into mechanisms underlying tuberculosis, identify novel biomarkers, and help to optimise prevention, diagnosis and treatment of disease. These advances are critically important in the context of the evolving epidemic of drug-resistant tuberculosis. Here, we review the available evidence on the role of systems biology approaches - human and mycobacterial genomics and transcriptomics, proteomics, lipidomics/metabolomics, immunophenotyping, systems pharmacology and gut microbiomes - in the management of tuberculosis including prediction of risk for disease progression, severity of mycobacterial virulence and drug resistance, adverse events, comorbidities, response to therapy and treatment outcomes. Application of the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach demonstrated that at present most of the studies provide "very low" certainty of evidence for answering clinically relevant questions. Further studies in large prospective cohorts of patients, including randomised clinical trials, are necessary to assess the applicability of the findings in tuberculosis prevention and more efficient clinical management of patients.
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Affiliation(s)
- Irina Kontsevaya
- Research Center Borstel, Borstel, Germany.,German Center for Infection Research, Hamburg-Lübeck-Borstel-Riems, Borstel, Germany.,International Health/Infectious Diseases, University of Lübeck, Lübeck, Germany
| | - Christoph Lange
- Research Center Borstel, Borstel, Germany.,German Center for Infection Research, Hamburg-Lübeck-Borstel-Riems, Borstel, Germany.,International Health/Infectious Diseases, University of Lübeck, Lübeck, Germany
| | - Patricia Comella-Del-Barrio
- Research Institute Germans Trias i Pujol, CIBER Respiratory Diseases, Universitat Autònoma de Barcelona, Badalona, Spain
| | - Cristian Coarfa
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA.,Molecular and Cellular Biology, Center for Precision Environmental health, Baylor College of Medicine, Houston, TX, USA
| | - Andrew R DiNardo
- The Global Tuberculosis Program, Texas Children's Hospital, Dept of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | | | - Matthias Hauptmann
- Research Center Borstel, Borstel, Germany.,German Center for Infection Research, Hamburg-Lübeck-Borstel-Riems, Borstel, Germany
| | - Christoph Leschczyk
- Research Center Borstel, Borstel, Germany.,German Center for Infection Research, Hamburg-Lübeck-Borstel-Riems, Borstel, Germany
| | - Anna M Mandalakas
- The Global Tuberculosis Program, Texas Children's Hospital, Dept of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Antal Martinecz
- Dept of Biology, Pennsylvania State University, University Park, PA, USA.,Center for Infectious Disease Dynamics, Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA, USA.,Dept of Pharmacy, Faculty of Health Sciences, UiT, Arctic University of Norway, Tromsø, Norway
| | - Matthias Merker
- Research Center Borstel, Borstel, Germany.,German Center for Infection Research, Hamburg-Lübeck-Borstel-Riems, Borstel, Germany
| | - Stefan Niemann
- Research Center Borstel, Borstel, Germany.,German Center for Infection Research, Hamburg-Lübeck-Borstel-Riems, Borstel, Germany
| | - Maja Reimann
- Research Center Borstel, Borstel, Germany.,German Center for Infection Research, Hamburg-Lübeck-Borstel-Riems, Borstel, Germany.,International Health/Infectious Diseases, University of Lübeck, Lübeck, Germany
| | - Olena Rzhepishevska
- Dept of Chemistry, Umeå University, Umeå, Sweden.,Dept of Clinical Microbiology, Umeå University, Umeå, Sweden
| | - Ulrich E Schaible
- Research Center Borstel, Borstel, Germany.,German Center for Infection Research, Hamburg-Lübeck-Borstel-Riems, Borstel, Germany
| | | | - Erwin Schurr
- Infectious Diseases and Immunity in Global Health Program, Research Institute of the McGill University Health Centre, Montréal, Canada
| | - Pia Abel Zur Wiesch
- Dept of Biology, Pennsylvania State University, University Park, PA, USA.,Center for Infectious Disease Dynamics, Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA, USA
| | - Jan Heyckendorf
- Research Center Borstel, Borstel, Germany .,German Center for Infection Research, Hamburg-Lübeck-Borstel-Riems, Borstel, Germany.,International Health/Infectious Diseases, University of Lübeck, Lübeck, Germany
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5
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Mahmutovic A, Gillman AN, Lauksund S, Robson Moe NA, Manzi A, Storflor M, Abel Zur Wiesch P, Abel S. RESTAMP - Rate estimates by sequence-tag analysis of microbial populations. Comput Struct Biotechnol J 2021; 19:1035-1051. [PMID: 33613869 PMCID: PMC7878984 DOI: 10.1016/j.csbj.2021.01.017] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 01/12/2021] [Accepted: 01/14/2021] [Indexed: 12/31/2022] Open
Abstract
Microbial division rates determine the speed of mutation accumulation and thus the emergence of antimicrobial resistance. Microbial death rates are affected by antibiotic action and the immune system. Therefore, measuring these rates has advanced our understanding of host-pathogen interactions and antibiotic action. Several methods based on marker-loss or few inheritable neutral markers exist that allow estimating microbial division and death rates, each of which has advantages and limitations. Technical bottlenecks, i.e., experimental sampling events, during the experiment can distort the rate estimates and are typically unaccounted for or require additional calibration experiments. In this work, we introduce RESTAMP (Rate Estimates by Sequence Tag Analysis of Microbial Populations) as a method for determining bacterial division and death rates. This method uses hundreds of fitness neutral sequence barcodes to measure the rates and account for experimental bottlenecks at the same time. We experimentally validate RESTAMP and compare it to established plasmid loss methods. We find that RESTAMP has a number of advantages over plasmid loss or previous marker based techniques. (i) It enables to correct the distortion of rate estimates by technical bottlenecks. (ii) Rate estimates are independent of the sequence tag distribution in the starting culture allowing the use of an arbitrary number of tags. (iii) It introduces a bottleneck sensitivity measure that can be used to maximize the accuracy of the experiment. RESTAMP allows studying microbial population dynamics with great resolution over a wide dynamic range and can thus advance our understanding of host-pathogen interactions or the mechanisms of antibiotic action.
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Affiliation(s)
- Anel Mahmutovic
- Department of Pharmacy, Faculty of Health Sciences, UiT - The Arctic University of Norway, 9037 Tromsø, Norway
| | - Aaron Nicholas Gillman
- Department of Pharmacy, Faculty of Health Sciences, UiT - The Arctic University of Norway, 9037 Tromsø, Norway.,Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, PA 16802, USA
| | - Silje Lauksund
- Department of Pharmacy, Faculty of Health Sciences, UiT - The Arctic University of Norway, 9037 Tromsø, Norway
| | - Natasha-Anne Robson Moe
- Department of Pharmacy, Faculty of Health Sciences, UiT - The Arctic University of Norway, 9037 Tromsø, Norway
| | - Aime Manzi
- Department of Pharmacy, Faculty of Health Sciences, UiT - The Arctic University of Norway, 9037 Tromsø, Norway
| | - Merete Storflor
- Department of Pharmacy, Faculty of Health Sciences, UiT - The Arctic University of Norway, 9037 Tromsø, Norway.,Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, PA 16802, USA
| | - Pia Abel Zur Wiesch
- Department of Pharmacy, Faculty of Health Sciences, UiT - The Arctic University of Norway, 9037 Tromsø, Norway.,Centre for Molecular Medicine Norway, Nordic EMBL Partnership, 0318 Oslo, Norway.,Department of Biology, The Pennsylvania State University, University Park, PA 16802, USA.,Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA
| | - Sören Abel
- Department of Pharmacy, Faculty of Health Sciences, UiT - The Arctic University of Norway, 9037 Tromsø, Norway.,Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, PA 16802, USA.,Centre for Molecular Medicine Norway, Nordic EMBL Partnership, 0318 Oslo, Norway.,Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA
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6
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Stagg HR, Flook M, Martinecz A, Kielmann K, Abel Zur Wiesch P, Karat AS, Lipman MCI, Sloan DJ, Walker EF, Fielding KL. All nonadherence is equal but is some more equal than others? Tuberculosis in the digital era. ERJ Open Res 2020; 6:00315-2020. [PMID: 33263043 PMCID: PMC7682676 DOI: 10.1183/23120541.00315-2020] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 07/16/2020] [Indexed: 12/20/2022] Open
Abstract
Adherence to treatment for tuberculosis (TB) has been a concern for many decades, resulting in the World Health Organization's recommendation of the direct observation of treatment in the 1990s. Recent advances in digital adherence technologies (DATs) have renewed discussion on how to best address nonadherence, as well as offering important information on dose-by-dose adherence patterns and their variability between countries and settings. Previous studies have largely focussed on percentage thresholds to delineate sufficient adherence, but this is misleading and limited, given the complex and dynamic nature of adherence over the treatment course. Instead, we apply a standardised taxonomy - as adopted by the international adherence community - to dose-by-dose medication-taking data, which divides missed doses into 1) late/noninitiation (starting treatment later than expected/not starting), 2) discontinuation (ending treatment early), and 3) suboptimal implementation (intermittent missed doses). Using this taxonomy, we can consider the implications of different forms of nonadherence for intervention and regimen design. For example, can treatment regimens be adapted to increase the "forgiveness" of common patterns of suboptimal implementation to protect against treatment failure and the development of drug resistance? Is it reasonable to treat all missed doses of treatment as equally problematic and equally common when deploying DATs? Can DAT data be used to indicate the patients that need enhanced levels of support during their treatment course? Critically, we pinpoint key areas where knowledge regarding treatment adherence is sparse and impeding scientific progress.
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Affiliation(s)
- Helen R Stagg
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Mary Flook
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Antal Martinecz
- Department of Biology, Pennsylvania State University, University Park, PA, USA.,Center for Infectious Disease Dynamics, Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA, USA.,Department of Pharmacy, Faculty of Health Sciences, UiT - The Arctic University of Norway, Tromsø, Norway
| | - Karina Kielmann
- The Institute for Global Health and Development, Queen Margaret University, Musselburgh, UK
| | - Pia Abel Zur Wiesch
- Department of Biology, Pennsylvania State University, University Park, PA, USA.,Center for Infectious Disease Dynamics, Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA, USA.,These authors contributed equally
| | - Aaron S Karat
- The Institute for Global Health and Development, Queen Margaret University, Musselburgh, UK.,TB Centre, London School of Hygiene & Tropical Medicine, London, UK.,These authors contributed equally
| | - Marc C I Lipman
- UCL Respiratory, Division of Medicine, University College London, London, UK.,Department of Respiratory Medicine, Royal Free London NHS Foundation Trust, London, UK.,These authors contributed equally
| | - Derek J Sloan
- School of Medicine, University of St Andrews, St Andrews, UK.,These authors contributed equally
| | | | - Katherine L Fielding
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.,School of Public Health, University of the Witwatersrand, Johannesburg, South Africa
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7
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Clarelli F, Liang J, Martinecz A, Heiland I, Abel Zur Wiesch P. Multi-scale modeling of drug binding kinetics to predict drug efficacy. Cell Mol Life Sci 2020; 77:381-394. [PMID: 31768605 PMCID: PMC7010620 DOI: 10.1007/s00018-019-03376-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 11/06/2019] [Accepted: 11/12/2019] [Indexed: 01/18/2023]
Abstract
Optimizing drug therapies for any disease requires a solid understanding of pharmacokinetics (the drug concentration at a given time point in different body compartments) and pharmacodynamics (the effect a drug has at a given concentration). Mathematical models are frequently used to infer drug concentrations over time based on infrequent sampling and/or in inaccessible body compartments. Models are also used to translate drug action from in vitro to in vivo conditions or from animal models to human patients. Recently, mathematical models that incorporate drug-target binding and subsequent downstream responses have been shown to advance our understanding and increase predictive power of drug efficacy predictions. We here discuss current approaches of modeling drug binding kinetics that aim at improving model-based drug development in the future. This in turn might aid in reducing the large number of failed clinical trials.
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Affiliation(s)
- Fabrizio Clarelli
- Department of Pharmacy, Faculty of Health Sciences, UiT The Arctic University of Norway, 9037, Tromsø, Norway
| | - Jingyi Liang
- Department of Pharmacy, Faculty of Health Sciences, UiT The Arctic University of Norway, 9037, Tromsø, Norway
| | - Antal Martinecz
- Department of Pharmacy, Faculty of Health Sciences, UiT The Arctic University of Norway, 9037, Tromsø, Norway
| | - Ines Heiland
- Department of Arctic and Marine Biology, UiT The Arctic University of Norway, 9037, Tromsø, Norway
| | - Pia Abel Zur Wiesch
- Department of Pharmacy, Faculty of Health Sciences, UiT The Arctic University of Norway, 9037, Tromsø, Norway.
- Centre for Molecular Medicine Norway, Nordic EMBL Partnership, Blindern, P.O. Box 1137, 0318, Oslo, Norway.
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8
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Tepekule B, Abel Zur Wiesch P, Kouyos RD, Bonhoeffer S. Quantifying the impact of treatment history on plasmid-mediated resistance evolution in human gut microbiota. Proc Natl Acad Sci U S A 2019; 116:23106-23116. [PMID: 31666328 PMCID: PMC6859334 DOI: 10.1073/pnas.1912188116] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
To understand how antibiotic use affects the risk of a resistant infection, we present a computational model of the population dynamics of gut microbiota including antibiotic resistance-conferring plasmids. We then describe how this model is parameterized based on published microbiota data. Finally, we investigate how treatment history affects the prevalence of resistance among opportunistic enterobacterial pathogens. We simulate treatment histories and identify which properties of prior antibiotic exposure are most influential in determining the prevalence of resistance. We find that resistance prevalence can be predicted by 3 properties, namely the total days of drug exposure, the duration of the drug-free period after last treatment, and the center of mass of the treatment pattern. Overall this work provides a framework for capturing the role of the microbiome in the selection of antibiotic resistance and highlights the role of treatment history for the prevalence of resistance.
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Affiliation(s)
- Burcu Tepekule
- Department of Environmental Systems Science, Eidgenössische Technische Hochschule Zurich, 8092 Zurich, Switzerland;
| | - Pia Abel Zur Wiesch
- Department of Pharmacy, Faculty of Health Sciences, UiT-The Arctic University of Norway, 9037 Tromsø, Norway
- Centre for Molecular Medicine Norway, 0318 Oslo, Norway
| | - Roger D Kouyos
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, 8091 Zurich, Switzerland
- Institute of Medical Virology, University of Zurich, 8057 Zurich, Switzerland
| | - Sebastian Bonhoeffer
- Department of Environmental Systems Science, Eidgenössische Technische Hochschule Zurich, 8092 Zurich, Switzerland
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Martinecz A, Clarelli F, Abel S, Abel Zur Wiesch P. Reaction Kinetic Models of Antibiotic Heteroresistance. Int J Mol Sci 2019; 20:E3965. [PMID: 31443146 PMCID: PMC6719119 DOI: 10.3390/ijms20163965] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Revised: 08/12/2019] [Accepted: 08/13/2019] [Indexed: 11/16/2022] Open
Abstract
Bacterial heteroresistance (i.e., the co-existence of several subpopulations with different antibiotic susceptibilities) can delay the clearance of bacteria even with long antibiotic exposure. Some proposed mechanisms have been successfully described with mathematical models of drug-target binding where the mechanism's downstream of drug-target binding are not explicitly modeled and subsumed in an empirical function, connecting target occupancy to antibiotic action. However, with current approaches it is difficult to model mechanisms that involve multi-step reactions that lead to bacterial killing. Here, we have a dual aim: first, to establish pharmacodynamic models that include multi-step reaction pathways, and second, to model heteroresistance and investigate which molecular heterogeneities can lead to delayed bacterial killing. We show that simulations based on Gillespie algorithms, which have been employed to model reaction kinetics for decades, can be useful tools to model antibiotic action via multi-step reactions. We highlight the strengths and weaknesses of current models and Gillespie simulations. Finally, we show that in our models, slight normally distributed variances in the rates of any event leading to bacterial death can (depending on parameter choices) lead to delayed bacterial killing (i.e., heteroresistance). This means that a slowly declining residual bacterial population due to heteroresistance is most likely the default scenario and should be taken into account when planning treatment length.
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Affiliation(s)
- Antal Martinecz
- Department of Pharmacy, Faculty of Health Sciences, UiT-The Arctic University of Norway, 9037 Tromsø, Norway
| | - Fabrizio Clarelli
- Department of Pharmacy, Faculty of Health Sciences, UiT-The Arctic University of Norway, 9037 Tromsø, Norway
| | - Sören Abel
- Department of Pharmacy, Faculty of Health Sciences, UiT-The Arctic University of Norway, 9037 Tromsø, Norway
- Centre for Molecular Medicine Norway, P.O. Box 1137, Blindern, 0318 Oslo, Norway
| | - Pia Abel Zur Wiesch
- Department of Pharmacy, Faculty of Health Sciences, UiT-The Arctic University of Norway, 9037 Tromsø, Norway.
- Centre for Molecular Medicine Norway, P.O. Box 1137, Blindern, 0318 Oslo, Norway.
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Abstract
Treatment of infectious diseases is often long and requires patients to take drugs even after they have seemingly recovered. This is because of a phenomenon called persistence, which allows small fractions of the bacterial population to survive treatment despite being genetically susceptible. The surviving subpopulation is often below detection limit and therefore is empirically inaccessible but can cause treatment failure when treatment is terminated prematurely. Mathematical models could aid in predicting bacterial survival and thereby determine sufficient treatment length. However, the mechanisms of persistence are hotly debated, necessitating the development of multiple mechanistic models. Here we develop a generalized mathematical framework that can accommodate various persistence mechanisms from measurable heterogeneities in pathogen populations. It allows the estimation of the relative increase in treatment length necessary to eradicate persisters compared to the majority population. To simplify and generalize, we separate the model into two parts: the distribution of the molecular mechanism of persistence in the bacterial population (e.g. number of efflux pumps or target molecules, growth rates) and the elimination rate of single bacteria as a function of that phenotype. Thereby, we obtain an estimate of the required treatment length for each phenotypic subpopulation depending on its size and susceptibility.
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Affiliation(s)
- Antal Martinecz
- Department of Pharmacy, Faculty of Health Sciences, UiT The Arctic University of Norway, 9037 Tromsø
| | - Pia Abel Zur Wiesch
- Department of Pharmacy, Faculty of Health Sciences, UiT The Arctic University of Norway, 9037 Tromsø.,Centre for Molecular Medicine Norway, Nordic EMBL Partnership, P.O. Box 1137, Blindern, 0318 Oslo, Norway
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Kunkel A, Abel Zur Wiesch P, Nathavitharana RR, Marx FM, Jenkins HE, Cohen T. Smear positivity in paediatric and adult tuberculosis: systematic review and meta-analysis. BMC Infect Dis 2016; 16:282. [PMID: 27296716 PMCID: PMC4906576 DOI: 10.1186/s12879-016-1617-9] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2015] [Accepted: 06/03/2016] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND Tuberculosis (TB) diagnosis continues to rely on sputum smear microscopy in many settings. We conducted a meta-analysis to estimate the percentage of children and adults with tuberculosis that are sputum smear positive. METHODS We searched PubMed, MEDLINE, Embase, and Global Health databases for studies that included both children and adults with all forms of active TB. The pooled percentages of children and adults with smear positive TB were estimated using the inverse variance heterogeneity model. This review was registered in the PROSPERO database under registration number CRD42015015331. RESULTS We identified 20 studies meeting our inclusion criteria that reported smear positivity for a total of 18,316 children and 162,574 adults from 14 countries. The pooled percentage of paediatric TB cases that were sputum smear positive was 6.8 % (95 % Confidence Interval (CI) 2.2-12.2 %), compared with 52.0 % (95 % CI 40.0-64.0 %) among adult cases. Eight studies reported data separately for children aged 0-4 and 5-14. The percentage of children aged 0-4 that were smear positive was 0.5 % (95 % CI 0.0-1.9 %), compared with 14.0 % (95 % CI 8.9-19.4 %) among children aged 5-14. CONCLUSIONS Children, especially those aged 0-4, are much less likely to be sputum smear positive than adults. National TB programs relying on sputum smear for diagnosis are at risk of under-diagnosing and underestimating the burden of TB in children.
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Affiliation(s)
- Amber Kunkel
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, USA.,Department of Epidemiology, Harvard School of Public Health, Boston, USA
| | - Pia Abel Zur Wiesch
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, USA.,Centre for Molecular Medicine Norway, Nordic EMBL Partnership, Oslo, Norway.,Department of Pharmacy, University of Tromso, Tromso, Norway
| | | | - Florian M Marx
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, USA.,Division of Global Health Equity, Brigham and Women's Hospital and Harvard Medical School, Boston, USA
| | - Helen E Jenkins
- Division of Global Health Equity, Brigham and Women's Hospital and Harvard Medical School, Boston, USA
| | - Ted Cohen
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, USA.
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Abel Zur Wiesch P, Abel S, Gkotzis S, Ocampo P, Engelstädter J, Hinkley T, Magnus C, Waldor MK, Udekwu K, Cohen T. Classic reaction kinetics can explain complex patterns of antibiotic action. Sci Transl Med 2016; 7:287ra73. [PMID: 25972005 DOI: 10.1126/scitranslmed.aaa8760] [Citation(s) in RCA: 56] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Finding optimal dosing strategies for treating bacterial infections is extremely difficult, and improving therapy requires costly and time-intensive experiments. To date, an incomplete mechanistic understanding of drug effects has limited our ability to make accurate quantitative predictions of drug-mediated bacterial killing and impeded the rational design of antibiotic treatment strategies. Three poorly understood phenomena complicate predictions of antibiotic activity: post-antibiotic growth suppression, density-dependent antibiotic effects, and persister cell formation. We show that chemical binding kinetics alone are sufficient to explain these three phenomena, using single-cell data and time-kill curves of Escherichia coli and Vibrio cholerae exposed to a variety of antibiotics in combination with a theoretical model that links chemical reaction kinetics to bacterial population biology. Our model reproduces existing observations, has a high predictive power across different experimental setups (R(2) = 0.86), and makes several testable predictions, which we verified in new experiments and by analyzing published data from a clinical trial on tuberculosis therapy. Although a variety of biological mechanisms have previously been invoked to explain post-antibiotic growth suppression, density-dependent antibiotic effects, and especially persister cell formation, our findings reveal that a simple model that considers only binding kinetics provides a parsimonious and unifying explanation for these three complex, phenotypically distinct behaviours. Current antibiotic and other chemotherapeutic regimens are often based on trial and error or expert opinion. Our "chemical reaction kinetics"-based approach may inform new strategies, which are based on rational design.
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Affiliation(s)
- Pia Abel Zur Wiesch
- Division of Global Health Equity, Brigham and Women's Hospital and Harvard Medical School, 641 Huntington Avenue, Boston, MA 02115, USA. Department of Epidemiology of Microbial Diseases, Yale School of Public Health, 60 College Street, New Haven, CT 06510, USA.
| | - Sören Abel
- Division of Infectious Diseases, Brigham and Women's Hospital and Harvard Medical School, 181 Longwood Avenue, Boston, MA 02115, USA. Department of Pharmacy, UiT, The Arctic University of Norway, 9037 Tromsø, Norway
| | - Spyridon Gkotzis
- Department of Neuroscience, Karolinska Institutet, Retzius väg 8, 17177 Stockholm, Sweden
| | - Paolo Ocampo
- Institute of Integrative Biology, ETH Zürich, Universitätsstrasse 16, 8092 Zürich, Switzerland. Department of Environmental Microbiology, EAWAG, Überlandstrasse 133, 8600 Dübendorf, Switzerland
| | - Jan Engelstädter
- School of Biological Sciences, University of Queensland, Brisbane, Queensland 4072, Australia
| | - Trevor Hinkley
- School of Chemistry, University of Glasgow, University Avenue, Glasgow G12 8QQ, UK
| | - Carsten Magnus
- Institute of Medical Virology, University of Zürich, Winterthurerstrasse 190, 8057 Zürich, Switzerland
| | - Matthew K Waldor
- Division of Infectious Diseases, Brigham and Women's Hospital and Harvard Medical School, 181 Longwood Avenue, Boston, MA 02115, USA. Howard Hughes Medical Institute, 181 Longwood Avenue, Boston, MA 02115, USA
| | - Klas Udekwu
- Department of Neuroscience, Karolinska Institutet, Retzius väg 8, 17177 Stockholm, Sweden
| | - Ted Cohen
- Division of Global Health Equity, Brigham and Women's Hospital and Harvard Medical School, 641 Huntington Avenue, Boston, MA 02115, USA. Department of Epidemiology of Microbial Diseases, Yale School of Public Health, 60 College Street, New Haven, CT 06510, USA. Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA
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Kouyos RD, Abel Zur Wiesch P, Bonhoeffer S. On being the right size: the impact of population size and stochastic effects on the evolution of drug resistance in hospitals and the community. PLoS Pathog 2011; 7:e1001334. [PMID: 21533212 PMCID: PMC3077359 DOI: 10.1371/journal.ppat.1001334] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2010] [Accepted: 03/15/2011] [Indexed: 11/18/2022] Open
Abstract
The evolution of drug resistant bacteria is a severe public health problem, both in hospitals and in the community. Currently, some countries aim at concentrating highly specialized services in large hospitals in order to improve patient outcomes. Emergent resistant strains often originate in health care facilities, but it is unknown to what extent hospital size affects resistance evolution and the resulting spillover of hospital-associated pathogens to the community. We used two published datasets from the US and Ireland to investigate the effects of hospital size and controlled for several confounders such as antimicrobial usage, sampling frequency, mortality, disinfection and length of stay. The proportion of patients acquiring both sensitive and resistant infections in a hospital strongly correlated with hospital size. Moreover, we observe the same pattern for both the percentage of resistant infections and the increase of hospital-acquired infections over time. One interpretation of this pattern is that chance effects in small hospitals impede the spread of drug-resistance. To investigate to what extent the size distribution of hospitals can directly affect the prevalence of antibiotic resistance, we use a stochastic epidemiological model describing the spread of drug resistance in a hospital setting as well as the interaction between one or several hospitals and the community. We show that the level of drug resistance typically increases with population size: In small hospitals chance effects cause large fluctuations in pathogen population size or even extinctions, both of which impede the acquisition and spread of drug resistance. Finally, we show that indirect transmission via environmental reservoirs can reduce the effect of hospital size because the slow turnover in the environment can prevent extinction of resistant strains. This implies that reducing environmental transmission is especially important in small hospitals, because such a reduction not only reduces overall transmission but might also facilitate the extinction of resistant strains. Overall, our study shows that the distribution of hospital sizes is a crucial factor for the spread of drug resistance. The increasing spread of bacteria, which are resistant to antibiotics, is a serious threat to clinical care. Currently, several countries aim at concentrating highly specialized services in large hospitals in order to improve patient outcomes. However, empirical studies have shown that resistance levels correlate with hospital size. To illustrate this correlation, we analyze two published datasets from the US and Ireland and controlled for antimicrobial usage, disinfection and length of stay. The proportion of patients acquiring both sensitive and resistant infections in hospitals strongly correlated with hospital size. Moreover, we observe the same pattern for both the percentage of resistant infections and the temporal increase of hospital-acquired infections. To investigate to what extent hospital size can directly affect the prevalence of antibiotic resistance, we use mathematical models describing the epidemic spread of resistance in hospitals and the community. We find that small hospitals typically lead to considerably lower resistance levels than large hospitals. However, this beneficial effect of small hospital size may be reduced if bacteria are transmitted indirectly via the environment. Therefore, reducing environmental transmission might be particularly important in small hospitals. Overall, our findings suggest that the short-term benefits of larger hospitals may come at the price of increasing resistance in the long term.
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Affiliation(s)
- Roger D Kouyos
- Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland.
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Kouyos RD, Abel Zur Wiesch P, Bonhoeffer S. Informed switching strongly decreases the prevalence of antibiotic resistance in hospital wards. PLoS Comput Biol 2011; 7:e1001094. [PMID: 21390265 PMCID: PMC3048378 DOI: 10.1371/journal.pcbi.1001094] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2010] [Accepted: 01/27/2011] [Indexed: 11/18/2022] Open
Abstract
Antibiotic resistant nosocomial infections are an important cause of mortality and morbidity in hospitals. Antibiotic cycling has been proposed to contain this spread by a coordinated use of different antibiotics. Theoretical work, however, suggests that often the random deployment of drugs ("mixing") might be the better strategy. We use an epidemiological model for a single hospital ward in order to assess the performance of cycling strategies which take into account the frequency of antibiotic resistance in the hospital ward. We assume that information on resistance frequencies stems from microbiological tests, which are performed in order to optimize individual therapy. Thus the strategy proposed here represents an optimization at population-level, which comes as a free byproduct of optimizing treatment at the individual level. We find that in most cases such an informed switching strategy outperforms both periodic cycling and mixing, despite the fact that information on the frequency of resistance is derived only from a small sub-population of patients. Furthermore we show that the success of this strategy is essentially a stochastic phenomenon taking advantage of the small population sizes in hospital wards. We find that the performance of an informed switching strategy can be improved substantially if information on resistance tests is integrated over a period of one to two weeks. Finally we argue that our findings are robust against a (moderate) preexistence of doubly resistant strains and against transmission via environmental reservoirs. Overall, our results suggest that switching between different antibiotics might be a valuable strategy in small patient populations, if the switching strategies take the frequencies of resistance alleles into account.
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Affiliation(s)
- Roger D Kouyos
- Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland.
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