Literature DB >> 31144539

Quantitative assessment of the activity of antituberculosis drugs and regimens.

Maxwell T Chirehwa1, Gustavo E Velásquez2,3, Tawanda Gumbo4, Helen McIlleron1.   

Abstract

Introduction: Identification of optimal drug doses and drug combinations is crucial for optimized treatment of tuberculosis. Areas covered: An unprecedented level of research activity involving multiple approaches is seeking to improve tuberculosis treatment. This report is a review of the quantitative methods currently used on clinical data sets to identify drug exposure targets and optimal drug combinations for tuberculosis treatment. A high-level summary of the methods, including the strengths and weaknesses of each method and potential methodological improvements is presented. Methods incorporating data generated from multiple sources such as in vitro and clinical studies, and their potential to provide better estimates of pharmacokinetic/pharmacodynamic (PK/PD) targets, are discussed. PK/PD relationships identified are compared between different studies and data analysis methods. Expert opinion: The relationships between drug exposures and tuberculosis treatment outcomes are complex and require analytical methods capable of handling the multidimensional nature of the relationships. The choice of a method is guided by its complexity, interpretability of results, and type of data available.

Entities:  

Keywords:  Tuberculosis; classification and regression trees; machine learning; multivariate adaptive regression splines; pharmacodynamic; pharmacokinetic; random forests

Mesh:

Substances:

Year:  2019        PMID: 31144539      PMCID: PMC6581212          DOI: 10.1080/14787210.2019.1621747

Source DB:  PubMed          Journal:  Expert Rev Anti Infect Ther        ISSN: 1478-7210            Impact factor:   5.091


  36 in total

1.  Pharmacokinetics-pharmacodynamics of pyrazinamide in a novel in vitro model of tuberculosis for sterilizing effect: a paradigm for faster assessment of new antituberculosis drugs.

Authors:  Tawanda Gumbo; Chandima S W Siyambalapitiyage Dona; Claudia Meek; Richard Leff
Journal:  Antimicrob Agents Chemother       Date:  2009-05-18       Impact factor: 5.191

2.  Impact of nonlinear interactions of pharmacokinetics and MICs on sputum bacillary kill rates as a marker of sterilizing effect in tuberculosis.

Authors:  Emmanuel Chigutsa; Jotam G Pasipanodya; Marianne E Visser; Paul D van Helden; Peter J Smith; Frederick A Sirgel; Tawanda Gumbo; Helen McIlleron
Journal:  Antimicrob Agents Chemother       Date:  2014-10-13       Impact factor: 5.191

3.  Serum drug concentrations predictive of pulmonary tuberculosis outcomes.

Authors:  Jotam G Pasipanodya; Helen McIlleron; André Burger; Peter A Wash; Peter Smith; Tawanda Gumbo
Journal:  J Infect Dis       Date:  2013-07-29       Impact factor: 5.226

4.  Isoniazid bactericidal activity and resistance emergence: integrating pharmacodynamics and pharmacogenomics to predict efficacy in different ethnic populations.

Authors:  Tawanda Gumbo; Arnold Louie; Weiguo Liu; David Brown; Paul G Ambrose; Sujata M Bhavnani; George L Drusano
Journal:  Antimicrob Agents Chemother       Date:  2007-04-16       Impact factor: 5.191

5.  A dose-ranging trial to optimize the dose of rifampin in the treatment of tuberculosis.

Authors:  Martin J Boeree; Andreas H Diacon; Rodney Dawson; Kim Narunsky; Jeannine du Bois; Amour Venter; Patrick P J Phillips; Stephen H Gillespie; Timothy D McHugh; Michael Hoelscher; Norbert Heinrich; Sunita Rehal; Dick van Soolingen; Jakko van Ingen; Cecile Magis-Escurra; David Burger; Georgette Plemper van Balen; Rob E Aarnoutse
Journal:  Am J Respir Crit Care Med       Date:  2015-05-01       Impact factor: 21.405

6.  Selection of a moxifloxacin dose that suppresses drug resistance in Mycobacterium tuberculosis, by use of an in vitro pharmacodynamic infection model and mathematical modeling.

Authors:  Tawanda Gumbo; Arnold Louie; Mark R Deziel; Linda M Parsons; Max Salfinger; George L Drusano
Journal:  J Infect Dis       Date:  2004-09-24       Impact factor: 5.226

7.  Concentration-dependent Mycobacterium tuberculosis killing and prevention of resistance by rifampin.

Authors:  Tawanda Gumbo; Arnold Louie; Mark R Deziel; Weiguo Liu; Linda M Parsons; Max Salfinger; George L Drusano
Journal:  Antimicrob Agents Chemother       Date:  2007-08-27       Impact factor: 5.191

8.  Isoniazid, rifampin, ethambutol, and pyrazinamide pharmacokinetics and treatment outcomes among a predominantly HIV-infected cohort of adults with tuberculosis from Botswana.

Authors:  Sekai Chideya; Carla A Winston; Charles A Peloquin; William Z Bradford; Philip C Hopewell; Charles D Wells; Arthur L Reingold; Thomas A Kenyon; Themba L Moeti; Jordan W Tappero
Journal:  Clin Infect Dis       Date:  2009-06-15       Impact factor: 9.079

9.  A time-to-event pharmacodynamic model describing treatment response in patients with pulmonary tuberculosis using days to positivity in automated liquid mycobacterial culture.

Authors:  Emmanuel Chigutsa; Kashyap Patel; Paolo Denti; Marianne Visser; Gary Maartens; Carl M J Kirkpatrick; Helen McIlleron; Mats O Karlsson
Journal:  Antimicrob Agents Chemother       Date:  2012-11-26       Impact factor: 5.191

Review 10.  Heterogeneity in tuberculosis pathology, microenvironments and therapeutic responses.

Authors:  Anne Lenaerts; Clifton E Barry; Véronique Dartois
Journal:  Immunol Rev       Date:  2015-03       Impact factor: 12.988

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  1 in total

1.  Drug exposure and susceptibility of second-line drugs correlate with treatment response in patients with multidrug-resistant tuberculosis: a multicentre prospective cohort study in China.

Authors:  Xubin Zheng; Lina Davies Forsman; Ziwei Bao; Yan Xie; Zhu Ning; Thomas Schön; Judith Bruchfeld; Biao Xu; Jan-Willem Alffenaar; Yi Hu
Journal:  Eur Respir J       Date:  2022-03-24       Impact factor: 16.671

  1 in total

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