Literature DB >> 30157391

Bacterial Factors That Predict Relapse after Tuberculosis Therapy.

Roberto Colangeli1, Hannah Jedrey1, Soyeon Kim1, Roy Connell1, Shuyi Ma1, Uma D Chippada Venkata1, Soumitesh Chakravorty1, Aditi Gupta1, Erin E Sizemore1, Lois Diem1, David R Sherman1, Alphonse Okwera1, Reynaldo Dietze1, W Henry Boom1, John L Johnson1, William R Mac Kenzie1, David Alland1.   

Abstract

BACKGROUND: Approximately 5% of patients with drug-susceptible tuberculosis have a relapse after 6 months of first-line therapy, as do approximately 20% of patients after 4 months of short-course therapy. We postulated that by analyzing pretreatment isolates of Mycobacterium tuberculosis obtained from patients who subsequently had a relapse or were cured, we could determine any correlations between the minimum inhibitory concentration (MIC) of a drug below the standard resistance breakpoint and the relapse risk after treatment.
METHODS: Using data from the Tuberculosis Trials Consortium Study 22 (development cohort), we assessed relapse and cure isolates to determine the MIC values of isoniazid and rifampin that were below the standard resistance breakpoint (0.1 μg per milliliter for isoniazid and 1.0 μg per milliliter for rifampin). We combined this analysis with clinical, radiologic, and laboratory data to generate predictive relapse models, which we validated by analyzing data from the DMID 01-009 study (validation cohort).
RESULTS: In the development cohort, the mean (±SD) MIC of isoniazid below the breakpoint was 0.0334±0.0085 μg per milliliter in the relapse group and 0.0286±0.0092 μg per milliliter in the cure group, which represented a higher value in the relapse group by a factor of 1.17 (P=0.02). The corresponding MIC values of rifampin were 0.0695±0.0276 and 0.0453±0.0223 μg per milliliter, respectively, which represented a higher value in the relapse group by a factor of 1.53 (P<0.001). Higher MIC values remained associated with relapse in a multivariable analysis that included other significant between-group differences. In an analysis of receiver-operating-characteristic curves of relapse based on these MIC values, the area under the curve (AUC) was 0.779. In the development cohort, the AUC in a multivariable model that included MIC values was 0.875. In the validation cohort, the MIC values either alone or combined with other patient characteristics were also predictive of relapse, with AUC values of 0.964 and 0.929, respectively. The use of a model score for the MIC values of isoniazid and rifampin to achieve 75.0% sensitivity in cross-validation analysis predicted relapse with a specificity of 76.5% in the development cohort and a sensitivity of 70.0% and a specificity of 100% in the validation cohort.
CONCLUSIONS: In pretreatment isolates of M. tuberculosis with decrements of MIC values of isoniazid or rifampin below standard resistance breakpoints, higher MIC values were associated with a greater risk of relapse than lower MIC values. (Funded by the National Institute of Allergy and Infectious Diseases.).

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Year:  2018        PMID: 30157391      PMCID: PMC6317071          DOI: 10.1056/NEJMoa1715849

Source DB:  PubMed          Journal:  N Engl J Med        ISSN: 0028-4793            Impact factor:   91.245


  41 in total

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Journal:  Contemp Clin Trials       Date:  2020-01-22       Impact factor: 2.226

2.  Phase variation in Mycobacterium tuberculosis glpK produces transiently heritable drug tolerance.

Authors:  Hassan Safi; Pooja Gopal; Subramanya Lingaraju; Shuyi Ma; Carly Levine; Veronique Dartois; Michelle Yee; Liping Li; Landry Blanc; Hsin-Pin Ho Liang; Seema Husain; Mainul Hoque; Patricia Soteropoulos; Tige Rustad; David R Sherman; Thomas Dick; David Alland
Journal:  Proc Natl Acad Sci U S A       Date:  2019-09-05       Impact factor: 11.205

Review 3.  Biology of antimicrobial resistance and approaches to combat it.

Authors:  Sarah M Schrader; Julien Vaubourgeix; Carl Nathan
Journal:  Sci Transl Med       Date:  2020-06-24       Impact factor: 17.956

4.  Individualised dosing algorithm and personalised treatment of high-dose rifampicin for tuberculosis.

Authors:  Robin J Svensson; Katarina Niward; Lina Davies Forsman; Judith Bruchfeld; Jakob Paues; Erik Eliasson; Thomas Schön; Ulrika S H Simonsson
Journal:  Br J Clin Pharmacol       Date:  2019-07-25       Impact factor: 4.335

5.  Variants in Bedaquiline-Candidate-Resistance Genes: Prevalence in Bedaquiline-Naive Patients, Effect on MIC, and Association with Mycobacterium tuberculosis Lineage.

Authors:  Emmanuel Rivière; Lennert Verboven; Anzaan Dippenaar; Sander Goossens; Elise De Vos; Elizabeth Streicher; Bart Cuypers; Kris Laukens; Fathia Ben-Rached; Timothy C Rodwell; Arnab Pain; Robin M Warren; Tim H Heupink; Annelies Van Rie
Journal:  Antimicrob Agents Chemother       Date:  2022-06-27       Impact factor: 5.938

6.  Mutations in rv0678 Confer Low-Level Resistance to Benzothiazinone DprE1 Inhibitors in Mycobacterium tuberculosis.

Authors:  Nicholas C Poulton; Zachary A Azadian; Michael A DeJesus; Jeremy M Rock
Journal:  Antimicrob Agents Chemother       Date:  2022-08-03       Impact factor: 5.938

Review 7.  Tuberculosis Treatment Monitoring and Outcome Measures: New Interest and New Strategies.

Authors:  Jan Heyckendorf; Sophia B Georghiou; Nicole Frahm; Norbert Heinrich; Irina Kontsevaya; Maja Reimann; David Holtzman; Marjorie Imperial; Daniela M Cirillo; Stephen H Gillespie; Morten Ruhwald
Journal:  Clin Microbiol Rev       Date:  2022-03-21       Impact factor: 50.129

8.  Model-based integration of genomics and metabolomics reveals SNP functionality in Mycobacterium tuberculosis.

Authors:  Ove Øyås; Sonia Borrell; Andrej Trauner; Michael Zimmermann; Julia Feldmann; Thomas Liphardt; Sebastien Gagneux; Jörg Stelling; Uwe Sauer; Mattia Zampieri
Journal:  Proc Natl Acad Sci U S A       Date:  2020-03-30       Impact factor: 11.205

9.  Use of Whole-Genome Sequencing to Predict Mycobacterium tuberculosis Complex Drug Resistance from Early Positive Liquid Cultures.

Authors:  Xiaocui Wu; Guangkun Tan; Wei Sha; Haican Liu; Jinghui Yang; Yinjuan Guo; Xin Shen; Zheyuan Wu; Hongbo Shen; Fangyou Yu
Journal:  Microbiol Spectr       Date:  2022-03-21

10.  Genetic models of latent tuberculosis in mice reveal differential influence of adaptive immunity.

Authors:  Hongwei Su; Kan Lin; Divya Tiwari; Claire Healy; Carolina Trujillo; Yao Liu; Thomas R Ioerger; Dirk Schnappinger; Sabine Ehrt
Journal:  J Exp Med       Date:  2021-07-16       Impact factor: 14.307

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