1
|
West J, Adler F, Gallaher J, Strobl M, Brady-Nicholls R, Brown J, Roberson-Tessi M, Kim E, Noble R, Viossat Y, Basanta D, Anderson ARA. A survey of open questions in adaptive therapy: Bridging mathematics and clinical translation. eLife 2023; 12:e84263. [PMID: 36952376 PMCID: PMC10036119 DOI: 10.7554/elife.84263] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 02/27/2023] [Indexed: 03/24/2023] Open
Abstract
Adaptive therapy is a dynamic cancer treatment protocol that updates (or 'adapts') treatment decisions in anticipation of evolving tumor dynamics. This broad term encompasses many possible dynamic treatment protocols of patient-specific dose modulation or dose timing. Adaptive therapy maintains high levels of tumor burden to benefit from the competitive suppression of treatment-sensitive subpopulations on treatment-resistant subpopulations. This evolution-based approach to cancer treatment has been integrated into several ongoing or planned clinical trials, including treatment of metastatic castrate resistant prostate cancer, ovarian cancer, and BRAF-mutant melanoma. In the previous few decades, experimental and clinical investigation of adaptive therapy has progressed synergistically with mathematical and computational modeling. In this work, we discuss 11 open questions in cancer adaptive therapy mathematical modeling. The questions are split into three sections: (1) integrating the appropriate components into mathematical models (2) design and validation of dosing protocols, and (3) challenges and opportunities in clinical translation.
Collapse
Affiliation(s)
- Jeffrey West
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research InstituteTampaUnited States
| | - Fred Adler
- Department of Mathematics, University of UtahSalt Lake CityUnited States
- School of Biological Sciences, University of UtahSalt Lake CityUnited States
| | - Jill Gallaher
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research InstituteTampaUnited States
| | - Maximilian Strobl
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research InstituteTampaUnited States
| | - Renee Brady-Nicholls
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research InstituteTampaUnited States
| | - Joel Brown
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research InstituteTampaUnited States
| | - Mark Roberson-Tessi
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research InstituteTampaUnited States
| | - Eunjung Kim
- Natural Product Informatics Research Center, Korea Institute of Science and TechnologyGangneungRepublic of Korea
| | - Robert Noble
- Department of Mathematics, University of LondonLondonUnited Kingdom
| | - Yannick Viossat
- Ceremade, Université Paris-Dauphine, Université Paris Sciences et LettresParisFrance
| | - David Basanta
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research InstituteTampaUnited States
| | - Alexander RA Anderson
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research InstituteTampaUnited States
| |
Collapse
|
2
|
Bogri A, Otani S, Aarestrup FM, Brinch C. Interplay between strain fitness and transmission frequency determines prevalence of antimicrobial resistance. Front Ecol Evol 2023. [DOI: 10.3389/fevo.2023.981377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2023] Open
Abstract
The steep rise of infections caused by bacteria that are resistant to antimicrobial agents threatens global health. However, the association between antimicrobial use and the prevalence of resistance is not straightforward. Therefore, it is necessary to quantify the importance of additional factors that affect this relationship. We theoretically explore how the prevalence of resistance is affected by the combination of three factors: antimicrobial use, bacterial transmission, and fitness cost of resistance. We present a model that combines within-host, between-hosts and between-populations dynamics, built upon the competitive Lotka-Volterra equations. We developed the model in a manner that allows future experimental validation of the findings with single isolates in the laboratory. Each host may carry two strains (susceptible and resistant) that represent the host’s commensal microbiome and are not the target of the antimicrobial treatment. The model simulates a population of hosts who are treated periodically with antibiotics and transmit bacteria to each other. We show that bacterial transmission results in strain co-existence. Transmission disseminates resistant bacteria in the population, increasing the levels of resistance. Counterintuitively, when the cost of resistance is low, high transmission frequencies reduce resistance prevalence. Transmission between host populations leads to more similar resistance levels, increasing the susceptibility of the population with higher antimicrobial use. Overall, our results indicate that the interplay between bacterial transmission and strain fitness affects the prevalence of resistance in a non-linear way. We then place our results within the context of ecological theory, particularly on temporal niche partitioning and metapopulation rescue, and we formulate testable experimental predictions for future research.
Collapse
|
3
|
Abstract
The emergence of drug resistance during antimicrobial therapy is a major global health problem, especially for chronic infections like human immunodeficiency virus, hepatitis B and C, and tuberculosis. Sub-optimal adherence to long-term treatment is an important contributor to resistance risk. New long-acting drugs are being developed for weekly, monthly or less frequent dosing to improve adherence, but may lead to long-term exposure to intermediate drug levels. In this study, we analyse the effect of dosing frequency on the risk of resistance evolving during time-varying drug levels. We find that long-acting therapies can increase, decrease or have little effect on resistance, depending on the source (pre-existing or de novo) and degree of resistance, and rates of drug absorption and clearance. Long-acting therapies with rapid drug absorption, slow clearance and strong wild-type inhibition tend to reduce resistance caused by partially resistant strains in the early stages of treatment even if they do not improve adherence. However, if subpopulations of microbes persist and can reactivate during sub-optimal treatment, longer-acting therapies may substantially increase the resistance risk. Our results show that drug kinetics affect selection for resistance in a complicated manner, and that pathogen-specific models are needed to evaluate the benefits of new long-acting therapies.
Collapse
Affiliation(s)
- Anjalika Nande
- Program for Evolutionary Dynamics, Harvard University, Cambridge, MA 02138, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Alison L. Hill
- Program for Evolutionary Dynamics, Harvard University, Cambridge, MA 02138, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
| |
Collapse
|
4
|
De Wit G, Svet L, Lories B, Steenackers HP. Microbial Interspecies Interactions and Their Impact on the Emergence and Spread of Antimicrobial Resistance. Annu Rev Microbiol 2022; 76:179-192. [PMID: 35609949 DOI: 10.1146/annurev-micro-041320-031627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Bacteria are social organisms that commonly live in dense communities surrounded by a multitude of other species. The competitive and cooperative interactions between these species not only shape the bacterial communities but also influence their susceptibility to antimicrobials. While several studies have shown that mixed-species communities are more tolerant toward antimicrobials than their monospecies counterparts, only limited empirical data are currently available on how interspecies interactions influence resistance development. We here propose a theoretic framework outlining the potential impact of interspecies social behavior on different aspects of resistance development. We identify factors by which interspecies interactions might influence resistance evolution and distinguish between their effect on (a) the emergence of a resistant mutant and (b) the spread of this resistance throughout the population. Our analysis indicates that considering the social life of bacteria is imperative to the rational design of more effective antibiotic treatment strategies with a minimal hazard for resistance development. Expected final online publication date for the Annual Review of Microbiology, Volume 76 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
Collapse
Affiliation(s)
- Gitta De Wit
- Centre of Microbial and Plant Genetics (CMPG), Department of Microbial and Molecular Systems, KU Leuven, 3001 Leuven, Belgium; , , ,
| | - Luka Svet
- Centre of Microbial and Plant Genetics (CMPG), Department of Microbial and Molecular Systems, KU Leuven, 3001 Leuven, Belgium; , , ,
| | - Bram Lories
- Centre of Microbial and Plant Genetics (CMPG), Department of Microbial and Molecular Systems, KU Leuven, 3001 Leuven, Belgium; , , ,
| | - Hans P Steenackers
- Centre of Microbial and Plant Genetics (CMPG), Department of Microbial and Molecular Systems, KU Leuven, 3001 Leuven, Belgium; , , ,
| |
Collapse
|
5
|
Smith DR, Temime L, Opatowski L. Microbiome-pathogen interactions drive epidemiological dynamics of antibiotic resistance: A modeling study applied to nosocomial pathogen control. eLife 2021; 10:68764. [PMID: 34517942 PMCID: PMC8560094 DOI: 10.7554/elife.68764] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 08/31/2021] [Indexed: 12/16/2022] Open
Abstract
The human microbiome can protect against colonization with pathogenic antibiotic-resistant bacteria (ARB), but its impacts on the spread of antibiotic resistance are poorly understood. We propose a mathematical modeling framework for ARB epidemiology formalizing within-host ARB-microbiome competition, and impacts of antibiotic consumption on microbiome function. Applied to the healthcare setting, we demonstrate a trade-off whereby antibiotics simultaneously clear bacterial pathogens and increase host susceptibility to their colonization, and compare this framework with a traditional strain-based approach. At the population level, microbiome interactions drive ARB incidence, but not resistance rates, reflecting distinct epidemiological relevance of different forces of competition. Simulating a range of public health interventions (contact precautions, antibiotic stewardship, microbiome recovery therapy) and pathogens (Clostridioides difficile, methicillin-resistant Staphylococcus aureus, multidrug-resistant Enterobacteriaceae) highlights how species-specific within-host ecological interactions drive intervention efficacy. We find limited impact of contact precautions for Enterobacteriaceae prevention, and a promising role for microbiome-targeted interventions to limit ARB spread.
Collapse
Affiliation(s)
- David Rm Smith
- Institut Pasteur, Epidemiology and Modelling of Antibiotic Evasion (EMAE), Paris, France.,Université Paris-Saclay, UVSQ, Inserm, CESP, Anti-infective evasion and pharmacoepidemiology team, Montigny-Le-Bretonneux, France.,Modélisation, épidémiologie et surveillance des risques sanitaires (MESuRS), Conservatoire national des arts et métiers, Paris, France
| | - Laura Temime
- Modélisation, épidémiologie et surveillance des risques sanitaires (MESuRS), Conservatoire national des arts et métiers, Paris, France.,PACRI unit, Institut Pasteur, Conservatoire national des arts et métiers, Paris, France
| | - Lulla Opatowski
- Institut Pasteur, Epidemiology and Modelling of Antibiotic Evasion (EMAE), Paris, France.,Université Paris-Saclay, UVSQ, Inserm, CESP, Anti-infective evasion and pharmacoepidemiology team, Montigny-Le-Bretonneux, France
| |
Collapse
|
6
|
Vakil V, Trappe W. Dosage strategies for delaying resistance emergence in heterogeneous tumors. FEBS Open Bio 2021; 11:1322-1331. [PMID: 33638275 PMCID: PMC8091820 DOI: 10.1002/2211-5463.13129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 02/15/2021] [Accepted: 02/16/2021] [Indexed: 11/09/2022] Open
Abstract
Drug resistance in cancer treatments is a frequent problem that, when it arises, leads to failure in therapeutic efforts. Tumor heterogeneity is the primary reason for resistance emergence and a precise treatment design that takes heterogeneity into account is required to postpone the rise of resistant subpopulations in the tumor environment. In this paper, we present a mathematical framework involving clonal evolution modeling of drug-sensitive and drug-resistant clones. Using our framework, we examine delaying the rise of resistance in heterogeneous tumors during control phase of therapy in a containment treatment approach. We apply pharmacokinetic/pharmacodynamic (PKPD) modeling and show that dosage strategies can be designed to control the resistant subpopulation. Our results show that the drug dosage and schedule determine the relative dynamics of sensitive and resistant clones. We present an optimal control problem that finds the dosing strategy that maximizes the delay in resistance emergence for a given period of containment treatment.
Collapse
Affiliation(s)
- Vahideh Vakil
- Wireless Information Network Laboratory (WINLAB), Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
| | - Wade Trappe
- Wireless Information Network Laboratory (WINLAB), Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
| |
Collapse
|
7
|
Within-host bacterial growth dynamics with both mutation and horizontal gene transfer. J Math Biol 2021; 82:16. [PMID: 33544239 DOI: 10.1007/s00285-021-01571-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Revised: 01/08/2021] [Accepted: 01/19/2021] [Indexed: 10/22/2022]
Abstract
The evolution and emergence of antibiotic resistance is a major public health concern. The understanding of the within-host microbial dynamics combining mutational processes, horizontal gene transfer and resource consumption, is one of the keys to solving this problem. We analyze a generic model to rigorously describe interactions dynamics of four bacterial strains: one fully sensitive to the drug, one with mutational resistance only, one with plasmidic resistance only, and one with both resistances. By defining thresholds numbers (i.e. each strain's effective reproduction and each strain's transition threshold numbers), we first express conditions for the existence of non-trivial stationary states. We find that these thresholds mainly depend on bacteria quantitative traits such as nutrient consumption ability, growth conversion factor, death rate, mutation (forward or reverse), and segregational loss of plasmid probabilities (for plasmid-bearing strains). Next, concerning the order in the set of strain's effective reproduction thresholds numbers, we show that the qualitative dynamics of the model range from the extinction of all strains, coexistence of sensitive and mutational resistance strains, to the coexistence of all strains at equilibrium. Finally, we go through some applications of our general analysis depending on whether bacteria strains interact without or with drug action (either cytostatic or cytotoxic).
Collapse
|
8
|
Hansen E, Read AF. Modifying Adaptive Therapy to Enhance Competitive Suppression. Cancers (Basel) 2020; 12:E3556. [PMID: 33260773 PMCID: PMC7761372 DOI: 10.3390/cancers12123556] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 11/20/2020] [Accepted: 11/26/2020] [Indexed: 12/28/2022] Open
Abstract
Adaptive therapy is a promising new approach to cancer treatment. It is designed to leverage competition between drug-sensitive and drug-resistant cells in order to suppress resistance and maintain tumor control for longer. Prompted by encouraging results from a recent pilot clinical trial, we evaluate the design of this initial test of adaptive therapy and identify three simple modifications that should improve performance. These modifications are designed to increase competition and are easy to implement. Using the mathematical model that supported the recent adaptive therapy trial, we show that the suggested modifications further delay time to tumor progression and also increase the range of patients who can benefit from adaptive therapy.
Collapse
Affiliation(s)
- Elsa Hansen
- Department of Biology, Pennsylvania State University, University Park, PA 16802, USA
| | - Andrew F. Read
- Department of Biology, Pennsylvania State University, University Park, PA 16802, USA
- Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA 16802, USA;
- Department of Entomology, Pennsylvania State University, University Park, PA 16802, USA
| |
Collapse
|
9
|
Gjini E, Paupério FFS, Ganusov VV. Treatment timing shifts the benefits of short and long antibiotic treatment over infection. Evol Med Public Health 2020; 2020:249-263. [PMID: 33376597 PMCID: PMC7750949 DOI: 10.1093/emph/eoaa033] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 08/19/2020] [Indexed: 12/13/2022] Open
Abstract
Antibiotics are the major tool for treating bacterial infections. Rising antibiotic resistance, however, calls for a better use of antibiotics. While classical recommendations favor long and aggressive treatments, more recent clinical trials advocate for moderate regimens. In this debate, two axes of 'aggression' have typically been conflated: treatment intensity (dose) and treatment duration. The third dimension of treatment timing along each individual's infection course has rarely been addressed. By using a generic mathematical model of bacterial infection controlled by immune response, we examine how the relative effectiveness of antibiotic treatment varies with its timing, duration and antibiotic kill rate. We show that short or long treatments may both be beneficial depending on treatment onset, the target criterion for success and on antibiotic efficacy. This results from the dynamic trade-off between immune response build-up and resistance risk in acute, self-limiting infections, and uncertainty relating symptoms to infection variables. We show that in our model early optimal treatments tend to be 'short and strong', while late optimal treatments tend to be 'mild and long'. This suggests a shift in the aggression axis depending on the timing of treatment. We find that any specific optimal treatment schedule may perform more poorly if evaluated by other criteria, or under different host-specific conditions. Our results suggest that major advances in antibiotic stewardship must come from a deeper empirical understanding of bacterial infection processes in individual hosts. To guide rational therapy, mathematical models need to be constrained by data, including a better quantification of personal disease trajectory in humans. Lay summary: Bacterial infections are becoming more difficult to treat worldwide because bacteria are becoming resistant to the antibiotics used. Addressing this problem requires a better understanding of how treatment along with other host factors impact antibiotic resistance. Until recently, most theoretical research has focused on the importance of antibiotic dosing on antibiotic resistance, however, duration and timing of treatment remain less explored. Here, we use a mathematical model of a generic bacterial infection to study three aspects of treatment: treatment dose/efficacy (defined by the antibiotic kill rate), duration, and timing, and their impact on several infection endpoints. We show that short and long treatment success strongly depends on when treatment begins (defined by the symptom threshold), the target criterion to optimize, and on antibiotic efficacy. We find that if administered early in an infection, "strong and short" therapy performs better, while if treatment begins at higher bacterial densities, a "mild and long" course of antibiotics is favored. In the model host immune defenses are key in preventing relapses, controlling antibiotic resistant bacteria and increasing the effectiveness of moderate intervention. In order to improve rational treatments of human infections, we call for a better quantification of individual disease trajectories in bacteria-immunity space.
Collapse
Affiliation(s)
- Erida Gjini
- Mathematical Modeling of Biological Processes Laboratory, Instituto Gulbenkian de Ciência, Rua da Quinta Grande, 6, Oeiras, 2780-156, Portugal
| | - Francisco F S Paupério
- Mathematical Modeling of Biological Processes Laboratory, Instituto Gulbenkian de Ciência, Rua da Quinta Grande, 6, Oeiras, 2780-156, Portugal
- Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Lisbon, 1749-016, Portugal
| | - Vitaly V Ganusov
- Department of Microbiology, University of Tennessee, Knoxville, TN 37996, USA
| |
Collapse
|
10
|
Clarelli F, Palmer A, Singh B, Storflor M, Lauksund S, Cohen T, Abel S, Abel zur Wiesch P. Drug-target binding quantitatively predicts optimal antibiotic dose levels in quinolones. PLoS Comput Biol 2020; 16:e1008106. [PMID: 32797079 PMCID: PMC7449454 DOI: 10.1371/journal.pcbi.1008106] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 08/26/2020] [Accepted: 06/30/2020] [Indexed: 11/19/2022] Open
Abstract
Antibiotic resistance is rising and we urgently need to gain a better quantitative understanding of how antibiotics act, which in turn would also speed up the development of new antibiotics. Here, we describe a computational model (COMBAT-COmputational Model of Bacterial Antibiotic Target-binding) that can quantitatively predict antibiotic dose-response relationships. Our goal is dual: We address a fundamental biological question and investigate how drug-target binding shapes antibiotic action. We also create a tool that can predict antibiotic efficacy a priori. COMBAT requires measurable biochemical parameters of drug-target interaction and can be directly fitted to time-kill curves. As a proof-of-concept, we first investigate the utility of COMBAT with antibiotics belonging to the widely used quinolone class. COMBAT can predict antibiotic efficacy in clinical isolates for quinolones from drug affinity (R2>0.9). To further challenge our approach, we also do the reverse: estimate the magnitude of changes in drug-target binding based on antibiotic dose-response curves. We overexpress target molecules to infer changes in antibiotic-target binding from changes in antimicrobial efficacy of ciprofloxacin with 92-94% accuracy. To test the generality of our approach, we use the beta-lactam ampicillin to predict target molecule occupancy at MIC from antimicrobial action with 90% accuracy. Finally, we apply COMBAT to predict antibiotic concentrations that can select for resistance due to novel resistance mutations. Using ciprofloxacin and ampicillin as well defined test cases, our work demonstrates that drug-target binding is a major predictor of bacterial responses to antibiotics. This is surprising because antibiotic action involves many additional effects downstream of drug-target binding. In addition, COMBAT provides a framework to inform optimal antibiotic dose levels that maximize efficacy and minimize the rise of resistant mutants.
Collapse
Affiliation(s)
- Fabrizio Clarelli
- 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, United States of America
- Center for Infectious Disease Dynamics, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, United States of America
| | - Adam Palmer
- Department of Pharmacology, Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Bhupender Singh
- Department of Pharmacy, Faculty of Health Sciences, UiT—The Arctic University of Norway, Tromsø, Norway
| | - Merete Storflor
- Department of Pharmacy, Faculty of Health Sciences, UiT—The Arctic University of Norway, Tromsø, Norway
- Department of Veterinary and Biomedical Sciences, College of Agricultural Sciences, The Pennsylvania State University, PA, United States of America
| | - Silje Lauksund
- Department of Pharmacy, Faculty of Health Sciences, UiT—The Arctic University of Norway, Tromsø, Norway
| | - Ted Cohen
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, United States of America
| | - Sören Abel
- Department of Pharmacy, Faculty of Health Sciences, UiT—The Arctic University of Norway, Tromsø, Norway
- Center for Infectious Disease Dynamics, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, United States of America
- Department of Veterinary and Biomedical Sciences, College of Agricultural Sciences, The Pennsylvania State University, PA, United States of America
- Centre for Molecular Medicine Norway, Nordic EMBL Partnership, Oslo, Norway
| | - 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, United States of America
- Center for Infectious Disease Dynamics, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, United States of America
- Centre for Molecular Medicine Norway, Nordic EMBL Partnership, Oslo, Norway
- * E-mail:
| |
Collapse
|
11
|
Adamowicz EM, Muza M, Chacón JM, Harcombe WR. Cross-feeding modulates the rate and mechanism of antibiotic resistance evolution in a model microbial community of Escherichia coli and Salmonella enterica. PLoS Pathog 2020; 16:e1008700. [PMID: 32687537 PMCID: PMC7392344 DOI: 10.1371/journal.ppat.1008700] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Revised: 07/30/2020] [Accepted: 06/11/2020] [Indexed: 12/28/2022] Open
Abstract
With antibiotic resistance rates on the rise, it is critical to understand how microbial species interactions influence the evolution of resistance. In obligate mutualisms, the survival of any one species (regardless of its intrinsic resistance) is contingent on the resistance of its cross-feeding partners. This sets the community antibiotic sensitivity at that of the 'weakest link' species. In this study, we tested the hypothesis that weakest link dynamics in an obligate cross-feeding relationship would limit the extent and mechanisms of antibiotic resistance evolution. We experimentally evolved an obligate co-culture and monoculture controls along gradients of two different antibiotics. We measured the rate at which each treatment increased antibiotic resistance, and sequenced terminal populations to question whether mutations differed between mono- and co-cultures. In both rifampicin and ampicillin treatments, we observed that resistance evolved more slowly in obligate co-cultures of E. coli and S. enterica than in monocultures. While we observed similar mechanisms of resistance arising under rifampicin selection, under ampicillin selection different resistance mechanisms arose in co-cultures and monocultures. In particular, mutations in an essential cell division protein, ftsI, arose in S. enterica only in co-culture. A simple mathematical model demonstrated that reliance on a partner is sufficient to slow the rate of adaptation, and can change the distribution of adaptive mutations that are acquired. Our results demonstrate that cooperative metabolic interactions can be an important modulator of resistance evolution in microbial communities.
Collapse
Affiliation(s)
- Elizabeth M. Adamowicz
- Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Michaela Muza
- Department of Ecology, Evolution and Behavior, University of Minnesota, St. Paul, Minnesota, United States of America
- BioTechnology Institute, University of Minnesota, St. Paul, Minnesota, United States of America
| | - Jeremy M. Chacón
- Department of Ecology, Evolution and Behavior, University of Minnesota, St. Paul, Minnesota, United States of America
- BioTechnology Institute, University of Minnesota, St. Paul, Minnesota, United States of America
| | - William R. Harcombe
- Department of Ecology, Evolution and Behavior, University of Minnesota, St. Paul, Minnesota, United States of America
- BioTechnology Institute, University of Minnesota, St. Paul, Minnesota, United States of America
- * E-mail:
| |
Collapse
|
12
|
Tetteh JNA, Matthäus F, Hernandez-Vargas EA. A survey of within-host and between-hosts modelling for antibiotic resistance. Biosystems 2020; 196:104182. [PMID: 32525023 DOI: 10.1016/j.biosystems.2020.104182] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Revised: 05/29/2020] [Accepted: 06/02/2020] [Indexed: 12/13/2022]
Abstract
Antibiotic resistance is a global public health problem which has the attention of many stakeholders including clinicians, the pharmaceutical industry, researchers and policy makers. Despite the existence of many studies, control of resistance transmission has become a rather daunting task as the mechanisms underlying resistance evolution and development are not fully known. Here, we discuss the mechanisms underlying antibiotic resistance development, explore some treatment strategies used in the fight against antibiotic resistance and consider recent findings on collateral susceptibilities amongst antibiotic classes. Mathematical models have proved valuable for unravelling complex mechanisms in biology and such models have been used in the quest of understanding the development and spread of antibiotic resistance. While assessing the importance of such mathematical models, previous systematic reviews were interested in investigating whether these models follow good modelling practice. We focus on theoretical approaches used for resistance modelling considering both within and between host models as well as some pharmacodynamic and pharmakokinetic approaches and further examine the interaction between drugs and host immune response during treatment with antibiotics. Finally, we provide an outlook for future research aimed at modelling approaches for combating antibiotic resistance.
Collapse
Affiliation(s)
- Josephine N A Tetteh
- Frankfurt Institute for Advanced Studies, Ruth-Moufang-Strasse 1, 60438, Frankfurt am Main, Germany; Institut für Mathematik, Goethe-Universität, Frankfurt am Main, Germany
| | - Franziska Matthäus
- Frankfurt Institute for Advanced Studies, Ruth-Moufang-Strasse 1, 60438, Frankfurt am Main, Germany; Faculty of Biological Sciences, Goethe University, Frankfurt am Main, Germany
| | - Esteban A Hernandez-Vargas
- Frankfurt Institute for Advanced Studies, Ruth-Moufang-Strasse 1, 60438, Frankfurt am Main, Germany; Instituto de Matemáticas, UNAM, Unidad Juriquilla, Blvd. Juriquilla 3001, Juriquilla, Queretaro, 76230, Mexico.
| |
Collapse
|
13
|
Hansen E, Karslake J, Woods RJ, Read AF, Wood KB. Antibiotics can be used to contain drug-resistant bacteria by maintaining sufficiently large sensitive populations. PLoS Biol 2020; 18:e3000713. [PMID: 32413038 PMCID: PMC7266357 DOI: 10.1371/journal.pbio.3000713] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Revised: 06/02/2020] [Accepted: 04/23/2020] [Indexed: 12/15/2022] Open
Abstract
Standard infectious disease practice calls for aggressive drug treatment that rapidly eliminates the pathogen population before resistance can emerge. When resistance is absent, this elimination strategy can lead to complete cure. However, when resistance is already present, removing drug-sensitive cells as quickly as possible removes competitive barriers that may slow the growth of resistant cells. In contrast to the elimination strategy, a containment strategy aims to maintain the maximum tolerable number of pathogens, exploiting competitive suppression to achieve chronic control. Here, we combine in vitro experiments in computer-controlled bioreactors with mathematical modeling to investigate whether containment strategies can delay failure of antibiotic treatment regimens. To do so, we measured the "escape time" required for drug-resistant Escherichia coli populations to eclipse a threshold density maintained by adaptive antibiotic dosing. Populations containing only resistant cells rapidly escape the threshold density, but we found that matched resistant populations that also contain the maximum possible number of sensitive cells could be contained for significantly longer. The increase in escape time occurs only when the threshold density-the acceptable bacterial burden-is sufficiently high, an effect that mathematical models attribute to increased competition. The findings provide decisive experimental confirmation that maintaining the maximum number of sensitive cells can be used to contain resistance when the size of the population is sufficiently large.
Collapse
Affiliation(s)
- Elsa Hansen
- Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Jason Karslake
- Department of Biophysics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Robert J. Woods
- Division of Infectious Diseases, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Andrew F. Read
- Center for Infectious Disease Dynamics, Huck Institutes of the Life Sciences and Departments of Biology and Entomology, Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Kevin B. Wood
- Department of Biophysics, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Physics, University of Michigan, Ann Arbor, Michigan, United States of America
- * E-mail:
| |
Collapse
|
14
|
Mulberry N, Rutherford A, Colijn C. Systematic comparison of coexistence in models of drug-sensitive and drug-resistant pathogen strains. Theor Popul Biol 2019; 133:150-158. [PMID: 31887315 DOI: 10.1016/j.tpb.2019.12.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Revised: 12/03/2019] [Accepted: 12/04/2019] [Indexed: 10/25/2022]
Abstract
A number of mathematical models have recently been proposed to explain empirical trends of pathogen diversity. In particular, long-term coexistence of both drug-sensitive and drug-resistant variants of a single pathogen is something of a mystery, given that simple models of pathogens competing for the same ecological niche predict competitive exclusion, and more complex models admitting coexistence require assumptions that may not be justified. Coinfection is among the candidate mechanisms to generate coexistence, as it occurs in many pathogens and provides the opportunity for strains to interact directly. Recently, coinfection and competitive release have been described as creating a form of negative frequency-dependent selection that promotes coexistence, and a range of models containing coinfection have been proposed as having generic stable coexistence of multiple strains. This abundance of new models presents the challenge of comparison and interpretation. To this end, we describe a dimensionless quantity that can be used to compare the amount of coexistence generated by different models. We focus on models that include coinfection, although this framework could be generalized to a larger class of structured models.
Collapse
|
15
|
Meehan MT, Cope RC, McBryde ES. On the probability of strain invasion in endemic settings: Accounting for individual heterogeneity and control in multi-strain dynamics. J Theor Biol 2019; 487:110109. [PMID: 31816294 PMCID: PMC7094110 DOI: 10.1016/j.jtbi.2019.110109] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 11/28/2019] [Accepted: 12/05/2019] [Indexed: 01/21/2023]
Abstract
Endemic infection can insulate host populations from invasion by mutant variants. The timing of control implementation strongly influences its efficacy. Controls that exacerbate host heterogeneity outperform those that curtail it. Differential control can facilitate strain invasion and eventual replacement.
Pathogen evolution is an imminent threat to global health that has warranted, and duly received, considerable attention within the medical, microbiological and modelling communities. Outbreaks of new pathogens are often ignited by the emergence and transmission of mutant variants descended from wild-type strains circulating in the community. In this work we investigate the stochastic dynamics of the emergence of a novel disease strain, introduced into a population in which it must compete with an existing endemic strain. In analogy with past work on single-strain epidemic outbreaks, we apply a branching process approximation to calculate the probability that the new strain becomes established. As expected, a critical determinant of the survival prospects of any invading strain is the magnitude of its reproduction number relative to that of the background endemic strain. Whilst in most circumstances this ratio must exceed unity in order for invasion to be viable, we show that differential control scenarios can lead to less-fit novel strains invading populations hosting a fitter endemic one. This analysis and the accompanying findings will inform our understanding of the mechanisms that have led to past instances of successful strain invasion, and provide valuable lessons for thwarting future drug-resistant strain incursions.
Collapse
Affiliation(s)
- Michael T Meehan
- James Cook University, Australian Institute of Tropical Health and Medicine, Townsville, Australia.
| | - Robert C Cope
- The University of Adelaide, School of Mathematical Sciences, Adelaide, Australia
| | - Emma S McBryde
- James Cook University, Australian Institute of Tropical Health and Medicine, Townsville, Australia
| |
Collapse
|
16
|
Knight GM, Davies NG, Colijn C, Coll F, Donker T, Gifford DR, Glover RE, Jit M, Klemm E, Lehtinen S, Lindsay JA, Lipsitch M, Llewelyn MJ, Mateus ALP, Robotham JV, Sharland M, Stekel D, Yakob L, Atkins KE. Mathematical modelling for antibiotic resistance control policy: do we know enough? BMC Infect Dis 2019; 19:1011. [PMID: 31783803 PMCID: PMC6884858 DOI: 10.1186/s12879-019-4630-y] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Accepted: 11/11/2019] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Antibiotics remain the cornerstone of modern medicine. Yet there exists an inherent dilemma in their use: we are able to prevent harm by administering antibiotic treatment as necessary to both humans and animals, but we must be mindful of limiting the spread of resistance and safeguarding the efficacy of antibiotics for current and future generations. Policies that strike the right balance must be informed by a transparent rationale that relies on a robust evidence base. MAIN TEXT One way to generate the evidence base needed to inform policies for managing antibiotic resistance is by using mathematical models. These models can distil the key drivers of the dynamics of resistance transmission from complex infection and evolutionary processes, as well as predict likely responses to policy change in silico. Here, we ask whether we know enough about antibiotic resistance for mathematical modelling to robustly and effectively inform policy. We consider in turn the challenges associated with capturing antibiotic resistance evolution using mathematical models, and with translating mathematical modelling evidence into policy. CONCLUSIONS We suggest that in spite of promising advances, we lack a complete understanding of key principles. From this we advocate for priority areas of future empirical and theoretical research.
Collapse
Affiliation(s)
- Gwenan M Knight
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine (LSHTM), London, UK.
| | - Nicholas G Davies
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine (LSHTM), London, UK
| | - Caroline Colijn
- Department of Mathematics, Simon Fraser University, Burnaby, Canada
| | - Francesc Coll
- Department of Infection Biology, Faculty of Infectious and Tropical Diseases, LSHTM, London, UK
| | - Tjibbe Donker
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Danna R Gifford
- Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Rebecca E Glover
- Department of Health Services Research and Policy, Faculty of Public Health and Policy, LSHTM, London, UK
| | - Mark Jit
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine (LSHTM), London, UK
| | | | - Sonja Lehtinen
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Jodi A Lindsay
- Institute for Infection and Immunity, St George's, University of London, Cranmer Terrace, London, UK
| | - Marc Lipsitch
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Martin J Llewelyn
- Department of Global Health and Infection, Brighton and Sussex Medical School, Brighton, UK
| | - Ana L P Mateus
- Population Sciences and Pathobiology Department, Royal Veterinary College, London, UK
| | - Julie V Robotham
- Modelling and Economics Unit, National Infection Service, Public Health England, London, UK
| | - Mike Sharland
- Paediatric Infectious Disease Research Group, St George's University of London, London, UK
| | - Dov Stekel
- School of Biosciences, University of Nottingham, Loughborough, UK
| | - Laith Yakob
- Department of Disease Control, Faculty of Infectious and Tropical Diseases, LSHTM, London, UK
| | - Katherine E Atkins
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine (LSHTM), London, UK
- Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, UK
| |
Collapse
|
17
|
Scire J, Hozé N, Uecker H. Aggressive or moderate drug therapy for infectious diseases? Trade-offs between different treatment goals at the individual and population levels. PLoS Comput Biol 2019; 15:e1007223. [PMID: 31404059 PMCID: PMC6742410 DOI: 10.1371/journal.pcbi.1007223] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Revised: 09/12/2019] [Accepted: 06/25/2019] [Indexed: 01/28/2023] Open
Abstract
Antimicrobial resistance is one of the major public health threats of the 21st century. There is a pressing need to adopt more efficient treatment strategies in order to prevent the emergence and spread of resistant strains. The common approach is to treat patients with high drug doses, both to clear the infection quickly and to reduce the risk of de novo resistance. Recently, several studies have argued that, at least in some cases, low-dose treatments could be more suitable to reduce the within-host emergence of antimicrobial resistance. However, the choice of a drug dose may have consequences at the population level, which has received little attention so far. Here, we study the influence of the drug dose on resistance and disease management at the host and population levels. We develop a nested two-strain model and unravel trade-offs in treatment benefits between an individual and the community. We use several measures to evaluate the benefits of any dose choice. Two measures focus on the emergence of resistance, at the host level and at the population level. The other two focus on the overall treatment success: the outbreak probability and the disease burden. We find that different measures can suggest different dosing strategies. In particular, we identify situations where low doses minimize the risk of emergence of resistance at the individual level, while high or intermediate doses prove most beneficial to improve the treatment efficiency or even to reduce the risk of resistance in the population.
Collapse
Affiliation(s)
- Jérémie Scire
- Institute of Integrative Biology, ETH Zürich, Zürich, Switzerland
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Nathanaël Hozé
- Institute of Integrative Biology, ETH Zürich, Zürich, Switzerland
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Paris, France
- * E-mail: (NH); (HU)
| | - Hildegard Uecker
- Institute of Integrative Biology, ETH Zürich, Zürich, Switzerland
- Research group Stochastic Evolutionary Dynamics, Department of Evolutionary Theory, Max Planck Institute for Evolutionary Biology, Plön, Germany
- * E-mail: (NH); (HU)
| |
Collapse
|
18
|
Jayasundara P, Regan DG, Seib KL, Jayasundara D, Wood JG. Modelling the in-host dynamics of Neisseria gonorrhoeae infection. Pathog Dis 2019; 77:5320890. [PMID: 30770529 DOI: 10.1093/femspd/ftz008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Accepted: 02/14/2019] [Indexed: 12/11/2022] Open
Abstract
The bacterial species Neisseria gonorrhoeae (NG) has evolved to replicate effectively and exclusively in human epithelia, with its survival dependent on complex interactions between bacteria, host cells and antimicrobial agents. A better understanding of these interactions is needed to inform development of new approaches to gonorrhoea treatment and prevention but empirical studies have proven difficult, suggesting a role for mathematical modelling. Here, we describe an in-host model of progression of untreated male symptomatic urethral infection, including NG growth and interactions with epithelial cells and neutrophils, informed by in vivo and in vitro studies. The model reproduces key observations on bacterial load and clearance and we use multivariate sensitivity analysis to refine plausible ranges for model parameters. Model variants are also shown to describe mouse infection dynamics with altered parameter ranges that correspond to observed differences between human and mouse infection. Our results highlight the importance of NG internalisation, particularly within neutrophils, in sustaining infection in the human model, with ∼80% of the total NG population internalised from day 25 on. This new mechanistic model of in-host NG infection dynamics should also provide a platform for future studies relating to antimicrobial treatment and resistance and infection at other anatomical sites.
Collapse
Affiliation(s)
- Pavithra Jayasundara
- Faculty of Medicine, School of Public Health and Community Medicine, UNSW Sydney, Samuels Avenue, Kensington, NSW 2052, Australia
| | - David G Regan
- The Kirby Institute, UNSW Sydney, High Street, Kensington, NSW 2052, Australia
| | - Kate L Seib
- Institute for Glycomics, Griffith University, Gold Coast campus, Parklands Dr, Southport, QLD 4222, Australia
| | - Duleepa Jayasundara
- Faculty of Medicine, School of Public Health and Community Medicine, UNSW Sydney, Samuels Avenue, Kensington, NSW 2052, Australia
| | - James G Wood
- Faculty of Medicine, School of Public Health and Community Medicine, UNSW Sydney, Samuels Avenue, Kensington, NSW 2052, Australia
| |
Collapse
|
19
|
Orwa TO, Mbogo RW, Luboobi LS. Multiple-Strain Malaria Infection and Its Impacts on Plasmodium falciparum Resistance to Antimalarial Therapy: A Mathematical Modelling Perspective. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2019; 2019:9783986. [PMID: 31341510 PMCID: PMC6594251 DOI: 10.1155/2019/9783986] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Accepted: 05/15/2019] [Indexed: 11/18/2022]
Abstract
The emergence of parasite resistance to antimalarial drugs has contributed significantly to global human mortality and morbidity due to malaria infection. The impacts of multiple-strain malarial parasite infection have further generated a lot of scientific interest. In this paper, we demonstrate, using the epidemiological model, the effects of parasite resistance and competition between the strains on the dynamics and control of Plasmodium falciparum malaria. The analysed model has a trivial equilibrium point which is locally asymptotically stable when the parasite's effective reproduction number is less than unity. Using contour plots, we observed that the efficacy of antimalarial drugs used, the rate of development of resistance, and the rate of infection by merozoites are the most important parameters in the multiple-strain P. falciparum infection and control model. Although the drug-resistant strain is shown to be less fit, the presence of both strains in the human host has a huge impact on the cost and success of antimalarial treatment. To reduce the emergence of resistant strains, it is vital that only effective antimalarial drugs are administered to patients in hospitals, especially in malaria-endemic regions. Our results emphasize the call for regular and strict surveillance on the use and distribution of antimalarial drugs in health facilities in malaria-endemic countries.
Collapse
Affiliation(s)
- Titus Okello Orwa
- Institute of Mathematical Sciences, Strathmore University, P.O. Box 59857-00200, Nairobi, Kenya
| | - Rachel Waema Mbogo
- Institute of Mathematical Sciences, Strathmore University, P.O. Box 59857-00200, Nairobi, Kenya
| | | |
Collapse
|
20
|
Bhattacharya A, Stacy A, Bashey F. Suppression of bacteriocin resistance using live, heterospecific competitors. Evol Appl 2019; 12:1191-1200. [PMID: 31293631 PMCID: PMC6597863 DOI: 10.1111/eva.12797] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Revised: 03/15/2019] [Accepted: 03/21/2019] [Indexed: 12/21/2022] Open
Abstract
Rapidly spreading antibiotic resistance has led to the need for novel alternatives and sustainable strategies for antimicrobial use. Bacteriocins are a class of proteinaceous anticompetitor toxins under consideration as novel therapeutic agents. However, bacteriocins, like other antimicrobial agents, are susceptible to resistance evolution and will require the development of sustainable strategies to prevent or decelerate the evolution of resistance. Here, we conduct proof-of-concept experiments to test whether introducing a live, heterospecific competitor along with a bacteriocin dose can effectively suppress the emergence of bacteriocin resistance in vitro. Previous work with conventional chemotherapeutic agents suggests that competition between conspecific sensitive and resistant pathogenic cells can effectively suppress the emergence of resistance in pathogenic populations. However, the threshold of sensitive cells required for such competitive suppression of resistance may often be too high to maintain host health. Therefore, here we aim to ask whether the principle of competitive suppression can be effective if a heterospecific competitor is used. Our results show that a live competitor introduced in conjunction with low bacteriocin dose can effectively control resistance and suppress sensitive cells. Further, this efficacy can be matched by using a bacteriocin-producing competitor without any additional bacteriocin. These results provide strong proof of concept for the effectiveness of competitive suppression using live, heterospecific competitors. Currently used probiotic strains or commensals may provide promising candidates for the therapeutic use of bacteriocin-mediated competitive suppression.
Collapse
Affiliation(s)
| | | | - Farrah Bashey
- Department of BiologyIndiana UniversityBloomingtonIndiana
| |
Collapse
|
21
|
Thaulow CM, Blix HS, Eriksen BH, Ask I, Myklebust TÅ, Berild D. Using a period incidence survey to compare antibiotic use in children between a university hospital and a district hospital in a country with low antimicrobial resistance: a prospective observational study. BMJ Open 2019; 9:e027836. [PMID: 31138583 PMCID: PMC6549646 DOI: 10.1136/bmjopen-2018-027836] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
OBJECTIVES To describe and compare antibiotic use in relation to indications, doses, adherence rate to guidelines and rates of broad-spectrum antibiotics (BSA) in two different paediatric departments with different academic cultures, and identify areas with room for improvement. DESIGN Prospective observational survey of antibiotic use. SETTING Paediatric departments in a university hospital (UH) and a district hospital (DH) in Norway, 2017. The registration period was 1 year at the DH and 4 months at the UH. PARTICIPANTS 201 children at the DH (mean age 3.8: SD 5.1) and 137 children at the UH (mean age 2.0: SD 5.9) were treated with systemic antibiotics by a paediatrician in the study period and included in the study. OUTCOME MEASURES Main outcome variables were prescriptions of antibiotics, treatments with antibiotics, rates of BSA, median doses and adherence rate to national guidelines. RESULTS In total, 744 prescriptions of antibiotics were given at the UH and 638 at the DH. Total adherence rate to guidelines was 75% at the UH and 69% at the DH (p=0.244). The rate of treatments involving BSA did not differ significantly between the hospitals (p=0.263). Use of BSA was related to treatment of central nervous system (CNS) infections, patients with underlying medical conditions or targeted microbiological treatment in 92% and 86% of the treatments, at the UH and DH, respectively (p=0.217). A larger proportion of the children at the DH were treated for respiratory tract infections (p<0.01) compared with the UH. Children at the UH were treated with higher doses of ampicillin and cefotaxime (p<0.05) compared with the DH. CONCLUSION Our results indicate that Norwegian paediatricians have a common understanding of main aspects in rational antibiotic use independently of working in a UH or DH. Variations in treatment of respiratory tract infections and in doses of antibiotics should be further studied.
Collapse
Affiliation(s)
| | - Hege Salvesen Blix
- Faculty of Medicine, Department of Pharmacology, University of Oslo, Oslo, Norway
- Department of Drug Statistics, Norwegian Institute of Public Health, Oslo, Norway
| | | | - Ingvild Ask
- Pediatric Department, Oslo University Hospital, Oslo, Norway
| | - Tor Åge Myklebust
- Department of Research and Innovation, Møre and Romsdal Hospital Trust, Ålesund, Norway
| | - Dag Berild
- Department of Infectious Diseases, University of Oslo, Oslo, Norway
| |
Collapse
|
22
|
Davies NG, Flasche S, Jit M, Atkins KE. Within-host dynamics shape antibiotic resistance in commensal bacteria. Nat Ecol Evol 2019; 3:440-449. [PMID: 30742105 PMCID: PMC6420107 DOI: 10.1038/s41559-018-0786-x] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Accepted: 12/11/2018] [Indexed: 12/21/2022]
Abstract
The spread of antibiotic resistance, a major threat to human health, is poorly understood. Simple population-level models of bacterial transmission predict that above a certain rate of antibiotic consumption in a population, resistant bacteria should completely eliminate non-resistant strains, while below this threshold they should be unable to persist at all. This prediction stands at odds with empirical evidence showing that resistant and non-resistant strains coexist stably over a wide range of antibiotic consumption rates. Not knowing what drives this long-term coexistence is a barrier to developing evidence-based strategies for managing the spread of resistance. Here, we argue that competition between resistant and sensitive pathogens within individual hosts gives resistant pathogens a relative fitness benefit when they are rare, promoting coexistence between strains at the population level. To test this hypothesis, we embed mechanistically explicit within-host dynamics in a structurally neutral pathogen transmission model. Doing so allows us to reproduce patterns of resistance observed in the opportunistic pathogens Escherichia coli and Streptococcus pneumoniae across European countries and to identify factors that may shape resistance evolution in bacteria by modulating the intensity and outcomes of within-host competition.
Collapse
Affiliation(s)
- Nicholas G Davies
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK.
- Department for Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.
| | - Stefan Flasche
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
- Department for Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Mark Jit
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
- Department for Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
- Modelling and Economics Unit, Public Health England, London, UK
| | - Katherine E Atkins
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
- Department for Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
- Centre for Global Health, Usher Institute of Population Health Sciences and Informatics, Edinburgh Medical School, The University of Edinburgh, Edinburgh, UK
| |
Collapse
|
23
|
Blanquart F. Evolutionary epidemiology models to predict the dynamics of antibiotic resistance. Evol Appl 2019; 12:365-383. [PMID: 30828361 PMCID: PMC6383707 DOI: 10.1111/eva.12753] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Revised: 11/22/2018] [Accepted: 11/29/2018] [Indexed: 12/12/2022] Open
Abstract
The evolution of resistance to antibiotics is a major public health problem and an example of rapid adaptation under natural selection by antibiotics. The dynamics of antibiotic resistance within and between hosts can be understood in the light of mathematical models that describe the epidemiology and evolution of the bacterial population. "Between-host" models describe the spread of resistance in the host community, and in more specific settings such as hospitalized hosts (treated by antibiotics at a high rate), or farm animals. These models make predictions on the best strategies to limit the spread of resistance, such as reducing transmission or adapting the prescription of several antibiotics. Models can be fitted to epidemiological data in the context of intensive care units or hospitals to predict the impact of interventions on resistance. It has proven harder to explain the dynamics of resistance in the community at large, in particular because models often do not reproduce the observed coexistence of drug-sensitive and drug-resistant strains. "Within-host" models describe the evolution of resistance within the treated host. They show that the risk of resistance emergence is maximal at an intermediate antibiotic dose, and some models successfully explain experimental data. New models that include the complex host population structure, the interaction between resistance-determining loci and other loci, or integrating the within- and between-host levels will allow better interpretation of epidemiological and genomic data from common pathogens and better prediction of the evolution of resistance.
Collapse
Affiliation(s)
- François Blanquart
- Centre for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS, INSERMPSL Research UniversityParisFrance
- IAME, UMR 1137, INSERMUniversité Paris DiderotParisFrance
| |
Collapse
|
24
|
Wright DW, Nowak B, Oppedal F, Crosbie P, Stien LH, Dempster T. Repeated sublethal freshwater exposures reduce the amoebic gill disease parasite, Neoparamoeba perurans, on Atlantic salmon. JOURNAL OF FISH DISEASES 2018; 41:1403-1410. [PMID: 29938799 DOI: 10.1111/jfd.12834] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Revised: 05/07/2018] [Accepted: 05/08/2018] [Indexed: 06/08/2023]
Abstract
Freshwater bathing is one of the main treatment options available against amoebic gill disease (AGD) affecting multiple fish hosts in mariculture systems. Prevailing freshwater treatments are designed to be long enough to kill Neoparamoeba perurans, the ectoparasite causing AGD, which may select for freshwater tolerance. Here, we tested whether using shorter, sublethal freshwater treatment durations are a viable alternative to lethal ones for N. perurans (2-4 hr). Under in vitro conditions, gill-isolated N. perurans attached to plastic substrate in sea water lifted off after ≥2 min in freshwater, but survival was not impacted until 60 min. In an in vivo experiment, AGD-affected Atlantic salmon Salmo salar subjected daily to 30 min (sublethal to N. perurans) and 120 min (lethal to N. perurans) freshwater treatments for 6 days consistently reduced N. perurans cell numbers on gills (based on qPCR analysis) compared to daily 3 min freshwater or seawater treatments for 6 days. Our results suggest that targeting cell detachment rather than cell death with repeated freshwater treatments of shorter duration than typical baths could be used in AGD management. However, the consequences of modifying the intensity of freshwater treatment regimes on freshwater tolerance evolution in N. perurans populations require careful consideration.
Collapse
Affiliation(s)
- Daniel William Wright
- Sustainable Aquaculture Laboratory - Temperate and Tropical, School of BioSciences, Melbourne, Vic., Australia
- Institute of Marine Research, Matre Research Station, Matredal, Norway
| | - Barbara Nowak
- Institute of Marine and Antarctic Studies, University of Tasmania, Launceston, TAS, Australia
| | - Frode Oppedal
- Institute of Marine Research, Matre Research Station, Matredal, Norway
| | - Phil Crosbie
- Institute of Marine and Antarctic Studies, University of Tasmania, Launceston, TAS, Australia
| | - Lars Helge Stien
- Institute of Marine Research, Matre Research Station, Matredal, Norway
| | - Tim Dempster
- Sustainable Aquaculture Laboratory - Temperate and Tropical, School of BioSciences, Melbourne, Vic., Australia
| |
Collapse
|
25
|
Application of dynamic modelling techniques to the problem of antibacterial use and resistance: a scoping review. Epidemiol Infect 2018; 146:2014-2027. [PMID: 30062979 DOI: 10.1017/s0950268818002091] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Selective pressure exerted by the widespread use of antibacterial drugs is accelerating the development of resistant bacterial populations. The purpose of this scoping review was to summarise the range of studies that use dynamic models to analyse the problem of bacterial resistance in relation to antibacterial use in human and animal populations. A comprehensive search of the peer-reviewed literature was performed and non-duplicate articles (n = 1486) were screened in several stages. Charting questions were used to extract information from the articles included in the final subset (n = 81). Most studies (86%) represent the system of interest with an aggregate model; individual-based models are constructed in only seven articles. There are few examples of inter-host models outside of human healthcare (41%) and community settings (38%). Resistance is modelled for a non-specific bacterial organism and/or antibiotic in 40% and 74% of the included articles, respectively. Interventions with implications for antibacterial use were investigated in 67 articles and included changes to total antibiotic consumption, strategies for drug management and shifts in category/class use. The quality of documentation related to model assumptions and uncertainty varies considerably across this subset of articles. There is substantial room to improve the transparency of reporting in the antibacterial resistance modelling literature as is recommended by best practice guidelines.
Collapse
|
26
|
Cobey S, Baskerville EB, Colijn C, Hanage W, Fraser C, Lipsitch M. Host population structure and treatment frequency maintain balancing selection on drug resistance. J R Soc Interface 2018; 14:rsif.2017.0295. [PMID: 28835542 PMCID: PMC5582124 DOI: 10.1098/rsif.2017.0295] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2017] [Accepted: 07/28/2017] [Indexed: 11/15/2022] Open
Abstract
It is a truism that antimicrobial drugs select for resistance, but explaining pathogen- and population-specific variation in patterns of resistance remains an open problem. Like other common commensals, Streptococcus pneumoniae has demonstrated persistent coexistence of drug-sensitive and drug-resistant strains. Theoretically, this outcome is unlikely. We modelled the dynamics of competing strains of S. pneumoniae to investigate the impact of transmission dynamics and treatment-induced selective pressures on the probability of stable coexistence. We find that the outcome of competition is extremely sensitive to structure in the host population, although coexistence can arise from age-assortative transmission models with age-varying rates of antibiotic use. Moreover, we find that the selective pressure from antibiotics arises not so much from the rate of antibiotic use per se but from the frequency of treatment: frequent antibiotic therapy disproportionately impacts the fitness of sensitive strains. This same phenomenon explains why serotypes with longer durations of carriage tend to be more resistant. These dynamics may apply to other potentially pathogenic, microbial commensals and highlight how population structure, which is often omitted from models, can have a large impact.
Collapse
Affiliation(s)
- Sarah Cobey
- Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA
| | | | - Caroline Colijn
- Department of Mathematics, Imperial College London, London, UK
| | - William Hanage
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Christophe Fraser
- Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Marc Lipsitch
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| |
Collapse
|
27
|
Beyond dose: Pulsed antibiotic treatment schedules can maintain individual benefit while reducing resistance. Sci Rep 2018; 8:5866. [PMID: 29650999 PMCID: PMC5897575 DOI: 10.1038/s41598-018-24006-w] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2017] [Accepted: 03/19/2018] [Indexed: 12/13/2022] Open
Abstract
The emergence of treatment-resistant microbes is a key challenge for disease treatment and a leading threat to human health and wellbeing. New drugs are always in development, but microbes regularly and rapidly acquire resistance. We must consider if altering how we administer drugs at the individual level could slow development of resistance. Here we use mathematical models to show that exposing microbes to drug pulses could greatly reduce resistance without increasing individual pathogen load. Our results stem from two key factors: the presence of antibiotics creates a selection pressure for antibiotic resistant microbes, and large populations of bacteria are more likely to harbor drug resistance than small populations. Drug pulsing targets these factors simultaneously. Short duration pulses minimize the time during which there is selection for resistance, and high drug concentrations minimize pathogen abundance. Our work provides a theoretical basis for the design of in vitro and in vivo experiments to test how drug pulsing might reduce the impact of drug resistant infections.
Collapse
|
28
|
Corander J, Fraser C, Gutmann MU, Arnold B, Hanage WP, Bentley SD, Lipsitch M, Croucher NJ. Frequency-dependent selection in vaccine-associated pneumococcal population dynamics. Nat Ecol Evol 2017; 1:1950-1960. [PMID: 29038424 PMCID: PMC5708525 DOI: 10.1038/s41559-017-0337-x] [Citation(s) in RCA: 81] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2017] [Accepted: 09/01/2017] [Indexed: 12/21/2022]
Abstract
Many bacterial species are composed of multiple lineages distinguished by extensive variation in gene content. These often cocirculate in the same habitat, but the evolutionary and ecological processes that shape these complex populations are poorly understood. Addressing these questions is particularly important for Streptococcus pneumoniae, a nasopharyngeal commensal and respiratory pathogen, because the changes in population structure associated with the recent introduction of partial-coverage vaccines have substantially reduced pneumococcal disease. Here we show that pneumococcal lineages from multiple populations each have a distinct combination of intermediate-frequency genes. Functional analysis suggested that these loci may be subject to negative frequency-dependent selection (NFDS) through interactions with other bacteria, hosts or mobile elements. Correspondingly, these genes had similar frequencies in four populations with dissimilar lineage compositions. These frequencies were maintained following substantial alterations in lineage prevalences once vaccination programmes began. Fitting a multilocus NFDS model of post-vaccine population dynamics to three genomic datasets using Approximate Bayesian Computation generated reproducible estimates of the influence of NFDS on pneumococcal evolution, the strength of which varied between loci. Simulations replicated the stable frequency of lineages unperturbed by vaccination, patterns of serotype switching and clonal replacement. This framework highlights how bacterial ecology affects the impact of clinical interventions.
Collapse
Affiliation(s)
- Jukka Corander
- Helsinki Institute for Information Technology, Department of Mathematics and Statistics, University of Helsinki, 00014, Helsinki, Finland
- Department of Biostatistics, University of Oslo, 0317, Oslo, Norway
- Infection Genomics, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Christophe Fraser
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7LF, UK
| | - Michael U Gutmann
- School of Informatics, University of Edinburgh, Edinburgh, EH8 9AB, UK
| | - Brian Arnold
- Center for Communicable Disease Dynamics, Harvard T. H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
| | - William P Hanage
- Center for Communicable Disease Dynamics, Harvard T. H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
| | - Stephen D Bentley
- Infection Genomics, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Marc Lipsitch
- Center for Communicable Disease Dynamics, Harvard T. H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
- Departments of Epidemiology and Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
| | - Nicholas J Croucher
- MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, W2 1PG, UK.
| |
Collapse
|
29
|
Hansen E, Woods RJ, Read AF. How to Use a Chemotherapeutic Agent When Resistance to It Threatens the Patient. PLoS Biol 2017; 15:e2001110. [PMID: 28182734 PMCID: PMC5300106 DOI: 10.1371/journal.pbio.2001110] [Citation(s) in RCA: 69] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Accepted: 01/06/2017] [Indexed: 12/21/2022] Open
Abstract
When resistance to anticancer or antimicrobial drugs evolves in a patient, highly effective chemotherapy can fail, threatening patient health and lifespan. Standard practice is to treat aggressively, effectively eliminating drug-sensitive target cells as quickly as possible. This prevents sensitive cells from acquiring resistance de novo but also eliminates populations that can competitively suppress resistant populations. Here we analyse that evolutionary trade-off and consider recent suggestions that treatment regimens aimed at containing rather than eliminating tumours or infections might more effectively delay the emergence of resistance. Our general mathematical analysis shows that there are situations in which regimens aimed at containment will outperform standard practice even if there is no fitness cost of resistance, and, in those cases, the time to treatment failure can be more than doubled. But, there are also situations in which containment will make a bad prognosis worse. Our analysis identifies thresholds that define these situations and thus can guide treatment decisions. The analysis also suggests a variety of interventions that could be used in conjunction with cytotoxic drugs to inhibit the emergence of resistance. Fundamental principles determine, across a wide range of disease settings, the circumstances under which standard practice best delays resistance emergence-and when it can be bettered.
Collapse
Affiliation(s)
- Elsa Hansen
- Center for Infectious Disease Dynamics, Departments of Biology and Entomology, Pennsylvania State University, Pennsylvania, United States of America
- * E-mail:
| | - Robert J. Woods
- Division of Infectious Diseases, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Andrew F. Read
- Center for Infectious Disease Dynamics, Departments of Biology and Entomology, Pennsylvania State University, Pennsylvania, United States of America
| |
Collapse
|
30
|
Knipl D, Röst G, Moghadas SM. Population dynamics of epidemic and endemic states of drug-resistance emergence in infectious diseases. PeerJ 2017; 5:e2817. [PMID: 28097052 PMCID: PMC5228518 DOI: 10.7717/peerj.2817] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2016] [Accepted: 11/22/2016] [Indexed: 12/19/2022] Open
Abstract
The emergence and spread of drug-resistance during treatment of many infectious diseases continue to degrade our ability to control and mitigate infection outcomes using therapeutic measures. While the coverage and efficacy of treatment remain key factors in the population dynamics of resistance, the timing for the start of the treatment in infectious individuals can significantly influence such dynamics. We developed a between-host disease transmission model to investigate the short-term (epidemic) and long-term (endemic) states of infections caused by two competing pathogen subtypes, namely the wild-type and resistant-type, when the probability of developing resistance is a function of delay in start of the treatment. We characterize the behaviour of disease equilibria and obtain a condition to minimize the fraction of population infectious at the endemic state in terms of probability of developing resistance and its transmission fitness. For the short-term epidemic dynamics, we illustrate that depending on the likelihood of resistance development at the time of treatment initiation, the same epidemic size may be achieved with different delays in start of the treatment, which may correspond to significantly different treatment coverages. Our results demonstrate that early initiation of treatment may not necessarily be the optimal strategy for curtailing the incidence of resistance or the overall disease burden. The risk of developing drug-resistance in-host remains an important factor in the management of resistance in the population.
Collapse
Affiliation(s)
- Diána Knipl
- Department of Mathematics, University College London, London, United Kingdom; MTA-SZTE Analysis and Stochastic Research Group, University of Szeged, Szeged, Hungary
| | - Gergely Röst
- Bolyai Institute, University of Szeged , Szeged , Hungary
| | - Seyed M Moghadas
- Agent-Based Modelling Laboratory, York University , Toronto , Canada
| |
Collapse
|
31
|
Gjini E, Madec S. A slow-fast dynamic decomposition links neutral and non-neutral coexistence in interacting multi-strain pathogens. THEOR ECOL-NETH 2016. [DOI: 10.1007/s12080-016-0320-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
|
32
|
Beams AB, Toth DJA, Khader K, Adler FR. Harnessing Intra-Host Strain Competition to Limit Antibiotic Resistance: Mathematical Model Results. Bull Math Biol 2016; 78:1828-1846. [PMID: 27670431 DOI: 10.1007/s11538-016-0201-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2016] [Accepted: 08/25/2016] [Indexed: 11/24/2022]
Abstract
Antibiotic overuse has promoted the spread of antibiotic resistance. To compound the issue, treating individuals dually infected with antibiotic-resistant and antibiotic-vulnerable strains can make their infections completely resistant through competitive release. We formulate mathematical models of transmission dynamics accounting for dual infections and extensions accounting for lag times between infection and treatment or between cure and ending treatment. Analysis using the Next-Generation Matrix reveals how competition within hosts and the costs of resistance determine whether vulnerable and resistant strains persist, coexist, or drive each other to extinction. Invasion analysis predicts that treatment of dually infected cases will promote resistance. By varying antibiotic strength, the models suggest that physicians have two ways to achieve a particular resistance target: prescribe relatively weak antibiotics to everyone infected with an antibiotic-vulnerable strain or give more potent prescriptions to only those patients singly infected with the vulnerable strain after ruling out the possibility of them being dually infected with resistance. Through selectivity and moderation in antibiotic prescription, resistance might be limited.
Collapse
Affiliation(s)
- Alexander B Beams
- Department of Mathematics, University of Utah, Salt Lake City, UT, USA.
| | - Damon J A Toth
- Informatics, Decision Enhancement, and Analytical Sciences (IDEAS) 2.0 Center, VA Salt Lake City Health Care System, Salt Lake City, UT, USA.,Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Karim Khader
- Informatics, Decision Enhancement, and Analytical Sciences (IDEAS) 2.0 Center, VA Salt Lake City Health Care System, Salt Lake City, UT, USA.,Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Frederick R Adler
- Department of Mathematics, University of Utah, Salt Lake City, UT, USA.,Department of Biology, University of Utah, Salt Lake City, UT, USA
| |
Collapse
|