Harnessing Intra-Host Strain Competition to Limit Antibiotic Resistance: Mathematical Model Results

Beams, Alexander; Toth, Damon; Khader, Karim; Adler, Frederick R.

doi:10.1007/s11538-016-0201-1

Cited by 4 publications

(8 citation statements)

References 27 publications

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“…One paper [ 23 ] compared SIR models of four, six, eight and 12 compartments to include dual infection and time lag between treatment and AMR development. The inclusion of dual infections covers situations where patients may recover to a state with a coexistence of strains or strain takeover by the sensitive or resistant strain, depending on parameters.…”

Section: Resultsmentioning

confidence: 99%

Send more data: a systematic review of mathematical models of antimicrobial resistance

Birkegård

Halasa

Toft

et al. 2018

Antimicrob Resist Infect Control

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BackgroundAntimicrobial resistance is a global health problem that demands all possible means to control it. Mathematical modelling is a valuable tool for understanding the mechanisms of AMR development and spread, and can help us to investigate and propose novel control strategies. However, it is of vital importance that mathematical models have a broad utility, which can be assured if good modelling practice is followed.ObjectiveThe objective of this study was to provide a comprehensive systematic review of published models of AMR development and spread. Furthermore, the study aimed to identify gaps in the knowledge required to develop useful models.MethodsThe review comprised a comprehensive literature search with 38 selected studies. Information was extracted from the selected papers using an adaptation of previously published frameworks, and was evaluated using the TRACE good modelling practice guidelines.ResultsNone of the selected papers fulfilled the TRACE guidelines. We recommend that future mathematical models should: a) model the biological processes mechanistically, b) incorporate uncertainty and variability in the system using stochastic modelling, c) include a sensitivity analysis and model external and internal validation.ConclusionMany mathematical models of AMR development and spread exist. There is still a lack of knowledge about antimicrobial resistance, which restricts the development of useful mathematical models.

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Section: Resultsmentioning

confidence: 99%

Send more data: a systematic review of mathematical models of antimicrobial resistance

Birkegård

Halasa

Toft

et al. 2018

Antimicrob Resist Infect Control

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“…While most articles describe to some extent the source(s) of data used to parameterise the model (86%), 11 articles do not comment on the source of the input values [41, 68, 71, 74, 77, 84, 85, 89, 107, 108, 113]. An additional eight articles rely solely on expert opinion or ‘guesstimates’ to parameterise the model and/or purposively choose values in order to highlight specific dynamic behaviours [33, 40, 52, 81, 86, 95, 106, 110].…”

Section: Resultsmentioning

confidence: 99%

“…Additional outcomes of interest include the likelihood of and/or time to resistance emergence or extinction [65, 66, 78, 83, 86, 87, 91, 93, 97, 98, 110], the likelihood of coexistence (i.e. of sensitive and resistant strains) [59, 62, 80, 106], the fraction of resistant infections attributable to antibiotic use [71, 96, 97, 101] and the number of treatment failures or inappropriately-treated patients [58, 66, 104]. The outcome of interest in four articles [94, 98–100] is the distribution of resistance levels determined by the minimum inhibitory concentration.…”

Section: Resultsmentioning

confidence: 99%

Application of dynamic modelling techniques to the problem of antibacterial use and resistance: a scoping review

et al. 2018

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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.

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“…Within-host competition creates frequency-dependent selection for resistance (24,25,(31)(32)(33) Yes Within-host competition (B: growth cost)…”

Section: Within-host Competition (A: Transmission Cost)mentioning

confidence: 99%

“…Individuals currently in treatment maintain resistant strains (24,32,35,39) No: Only supports a small amount of coexistence (24)…”

Section: Treated Classmentioning

confidence: 99%

Stabilising effects of competition and diversity determine vaccine impact on antibiotic resistance evolution

Davies

Jit

Atkins

2019

Preprint

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One sentence summary: Competition and diversity are key to antibiotic resistance 17 evolution and determine whether vaccines will prevent or increase resistant infections. 18Bacterial vaccines can protect recipients from contracting potentially antibiotic-19 resistant infections. But by altering the selective balance between sensitive and 20 resistant strains, vaccines may also help suppress-or spread-antibiotic 21 resistance among unvaccinated individuals. Predicting the outcome requires 22 knowing the drivers of resistance evolution. Using mathematical modelling, we 23 identify competition and diversity as key mediators of resistance evolution. 24Specifically, we show that the frequency of penicillin resistance in Streptococcus 25 pneumoniae (pneumococcus) across 27 European countries can be explained by 26 between-host diversity in antibiotic use, heritable diversity in pneumococcal 27 carriage duration, or within-host competition. We use our calibrated model to 28 predict the impact of universal pneumococcal vaccination upon the prevalence of 29 carriage, incidence of disease, and frequency of resistance for S. pneumoniae. The 30 relative strength and directionality of competition between resistant and 31 sensitive pneumococcal strains determines whether vaccination promotes, 32 inhibits, or has little effect on the evolution of antibiotic resistance. Finally, we 33 find that differences in overall bacterial transmission and carriage alter 34 predictions, suggesting that evidence-based policies for managing resistance with 35 vaccines must be tailored to both pathogen and setting. 36 37In an age of widespread antibiotic resistance, there is growing interest in using vaccines 38 to prevent bacterial infections that would otherwise call for treatment with antibiotics 39(1-4). This interest arises for two main reasons: first, vaccines are effective against both 40 antibiotic-resistant and antibiotic-sensitive bacteria; and second, successful prophylaxis 41 removes the need for a course of antibiotic therapy that might promote more resistance 42(2-5). Over the past two decades, the use of pneumococcal conjugate vaccines (PCVs) 43 has seemingly borne out these advantages. Administering PCVs to young children has 44 substantially reduced disease caused by S. pneumoniae (5-8)-a common asymptomatic 45 coloniser of the nasopharynx which can cause pneumonia, meningitis and other 46 infections when invasive-and has decreased demand for antibiotic therapy, largely by 47 reducing cases of otitis media (5, 9). But because PCV formulations target only a fraction 48 of the ~100 known pneumococcal serotypes, the niche vacated by PCV-targeted 49 serotypes has been filled by non-vaccine serotypes, and overall pneumococcal carriage 50 has rebounded to pre-vaccine levels (10, 11). Concomitantly, the incidence of infections 51 attributed to non-vaccine serotypes (12) and the proportion of non-vaccine-type 52 infections exhibiting antibiotic resistance (5, 13) have risen in many settings. Concern 53 over serotype replacement-along ...

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Harnessing Intra-Host Strain Competition to Limit Antibiotic Resistance: Mathematical Model Results

Cited by 4 publications

References 27 publications

Send more data: a systematic review of mathematical models of antimicrobial resistance

Send more data: a systematic review of mathematical models of antimicrobial resistance

Application of dynamic modelling techniques to the problem of antibacterial use and resistance: a scoping review

Stabilising effects of competition and diversity determine vaccine impact on antibiotic resistance evolution

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