Literature DB >> 26409276

The Distribution of Fitness Costs of Resistance-Conferring Mutations Is a Key Determinant for the Future Burden of Drug-Resistant Tuberculosis: A Model-Based Analysis.

Gwenan M Knight1, Caroline Colijn2, Sourya Shrestha3, Mariam Fofana3, Frank Cobelens4, Richard G White1, David W Dowdy3, Ted Cohen5.   

Abstract

BACKGROUND: Drug resistance poses a serious challenge for the control of tuberculosis in many settings. It is well established that the expected future trend in resistance depends on the reproductive fitness of drug-resistant Mycobacterium tuberculosis. However, the variability in fitness between strains with different resistance-conferring mutations has been largely ignored when making these predictions.
METHODS: We developed a novel approach for incorporating the variable fitness costs of drug resistance-conferring mutations and for tracking this distribution of fitness costs over time within a transmission model. We used this approach to describe the effects of realistic fitness cost distributions on the future prevalence of drug-resistant tuberculosis.
RESULTS: The shape of the distribution of fitness costs was a strong predictor of the long-term prevalence of resistance. While, as expected, lower average fitness costs of drug resistance-conferring mutations were associated with more severe epidemics of drug-resistant tuberculosis, fitness distributions with greater variance also led to higher levels of drug resistance. For example, compared to simulations in which the fitness cost of resistance was fixed, introducing a realistic amount of variance resulted in a 40% increase in prevalence of drug-resistant tuberculosis after 20 years.
CONCLUSIONS: The differences in the fitness costs associated with drug resistance-conferring mutations are a key determinant of the future burden of drug-resistant tuberculosis. Future studies that can better establish the range of fitness costs associated with drug resistance-conferring mutations will improve projections and thus facilitate better public health planning efforts.
© The Author 2015. Published by Oxford University Press on behalf of the Infectious Diseases Society of America. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  antibiotic resistance; fitness costs; mathematical modeling; tuberculosis

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Year:  2015        PMID: 26409276      PMCID: PMC4583567          DOI: 10.1093/cid/civ579

Source DB:  PubMed          Journal:  Clin Infect Dis        ISSN: 1058-4838            Impact factor:   9.079


  37 in total

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Review 3.  Advances in the development of new tuberculosis drugs and treatment regimens.

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Journal:  Antimicrob Agents Chemother       Date:  2012-04-02       Impact factor: 5.191

5.  The competitive cost of antibiotic resistance in Mycobacterium tuberculosis.

Authors:  Sebastien Gagneux; Clara Davis Long; Peter M Small; Tran Van; Gary K Schoolnik; Brendan J M Bohannan
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6.  Selection and covariance.

Authors:  G R Price
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7.  Characterization of spontaneous, In vitro-selected, rifampin-resistant mutants of Mycobacterium tuberculosis strain H37Rv.

Authors:  G P Morlock; B B Plikaytis; J T Crawford
Journal:  Antimicrob Agents Chemother       Date:  2000-12       Impact factor: 5.191

Review 8.  Erasing the world's slow stain: strategies to beat multidrug-resistant tuberculosis.

Authors:  Christopher Dye; Brian G Williams; Marcos A Espinal; Mario C Raviglione
Journal:  Science       Date:  2002-03-15       Impact factor: 47.728

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Authors:  M de Vos; B Müller; S Borrell; P A Black; P D van Helden; R M Warren; S Gagneux; T C Victor
Journal:  Antimicrob Agents Chemother       Date:  2012-12-03       Impact factor: 5.191

10.  The intrinsic transmission dynamics of tuberculosis epidemics.

Authors:  S M Blower; A R McLean; T C Porco; P M Small; P C Hopewell; M A Sanchez; A R Moss
Journal:  Nat Med       Date:  1995-08       Impact factor: 53.440

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Journal:  Tuberculosis (Edinb)       Date:  2018-10-01       Impact factor: 3.131

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Authors:  Palwasha Y Khan; Tom A Yates; Muhammad Osman; Robin M Warren; Yuri van der Heijden; Nesri Padayatchi; Edward A Nardell; David Moore; Barun Mathema; Neel Gandhi; Vegard Eldholm; Keertan Dheda; Anneke C Hesseling; Valerie Mizrahi; Roxana Rustomjee; Alexander Pym
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4.  Whole genome sequencing to complement tuberculosis drug resistance surveys in Uganda.

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Journal:  Infect Genet Evol       Date:  2016-02-23       Impact factor: 3.342

5.  Priority-Setting for Novel Drug Regimens to Treat Tuberculosis: An Epidemiologic Model.

Authors:  Emily A Kendall; Sourya Shrestha; Ted Cohen; Eric Nuermberger; Kelly E Dooley; Lice Gonzalez-Angulo; Gavin J Churchyard; Payam Nahid; Michael L Rich; Cathy Bansbach; Thomas Forissier; Christian Lienhardt; David W Dowdy
Journal:  PLoS Med       Date:  2017-01-03       Impact factor: 11.069

6.  Drug Resistance of Mycobacterium tuberculosis Complex in a Rural Setting, Angola.

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7.  Experimental Determination and Prediction of the Fitness Effects of Random Point Mutations in the Biosynthetic Enzyme HisA.

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8.  The relative fitness of drug-resistant Mycobacterium tuberculosis: a modelling study of household transmission in Peru.

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9.  Heterogeneous Streptomycin Resistance Level Among Mycobacterium tuberculosis Strains From the Same Transmission Cluster.

Authors:  Deisy M G C Rocha; Carlos Magalhães; Baltazar Cá; Angelica Ramos; Teresa Carvalho; Iñaki Comas; João Tiago Guimarães; Helder Novais Bastos; Margarida Saraiva; Nuno S Osório
Journal:  Front Microbiol       Date:  2021-06-11       Impact factor: 5.640

10.  Linking Individual Natural History to Population Outcomes in Tuberculosis.

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