Literature DB >> 19380594

Population modeling and Monte Carlo simulation study of the pharmacokinetics and antituberculosis pharmacodynamics of rifampin in lungs.

Sylvain Goutelle1, Laurent Bourguignon, Pascal H Maire, Michael Van Guilder, John E Conte, Roger W Jelliffe.   

Abstract

Little information exists on the pulmonary pharmacology of antituberculosis drugs. We used population pharmacokinetic modeling and Monte Carlo simulation to describe and explore the pulmonary pharmacokinetics and pharmacodynamics of rifampin (RIF; rifampicin). A population pharmacokinetic model that adequately described the plasma, epithelial lining fluid (ELF), and alveolar cell (AC) concentrations of RIF in a population of 34 human volunteers was made by use of the nonparametric adaptive grid (NPAG) algorithm. The estimated concentrations correlated well with the measured concentrations, and there was little bias and good precision. The results obtained with the NPAG algorithm were then imported into Matlab software to perform a 10,000-subject Monte Carlo simulation. The ability of RIF to suppress the development of drug resistance and to induce a sufficient bactericidal effect against Mycobacterium tuberculosis was evaluated by calculating the proportion of subjects achieving specific target values for the maximum concentration of drug (C(max))/MIC ratio and the area under the concentration-time curve from time zero to 24 h (AUC(0-24))/MIC ratio, respectively. At the lowest MIC (0.01 mg/liter), after the administration of one 600-mg oral dose, the rates of target attainment for C(max)/MIC (> or =175) were 95% in ACs, 48.8% in plasma, and 35.9% in ELF. Under the same conditions, the target attainment results for the killing effect were 100% in plasma (AUC(0-24)/MIC > or = 271) but only 54.5% in ELF (AUC(0-24)/MIC > or = 665). The use of a 1,200-mg RIF dose was associated with better results for target attainment. The overall results suggest that the pulmonary concentrations obtained with the standard RIF dose are too low in most subjects. This work supports the need to evaluate higher doses of RIF for the treatment of patients with tuberculosis.

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Year:  2009        PMID: 19380594      PMCID: PMC2704682          DOI: 10.1128/AAC.01520-08

Source DB:  PubMed          Journal:  Antimicrob Agents Chemother        ISSN: 0066-4804            Impact factor:   5.191


  33 in total

1.  Achieving target goals most precisely using nonparametric compartmental models and "multiple model" design of dosage regimens.

Authors:  R Jelliffe; D Bayard; M Milman; M Van Guilder; A Schumitzky
Journal:  Ther Drug Monit       Date:  2000-06       Impact factor: 3.681

2.  What is the 'right' dose of rifampin?

Authors:  Charles Peloquin
Journal:  Int J Tuberc Lung Dis       Date:  2003-01       Impact factor: 2.373

3.  Low isoniazid concentrations and outcome of tuberculosis treatment with once-weekly isoniazid and rifapentine.

Authors:  Marc Weiner; William Burman; Andrew Vernon; Debra Benator; Charles A Peloquin; Awal Khan; Stephen Weis; Barbara King; Nina Shah; Thomas Hodge
Journal:  Am J Respir Crit Care Med       Date:  2003-01-16       Impact factor: 21.405

4.  Telavancin penetration into human epithelial lining fluid determined by population pharmacokinetic modeling and Monte Carlo simulation.

Authors:  Thomas P Lodise; Mark Gotfried; Steven Barriere; George L Drusano
Journal:  Antimicrob Agents Chemother       Date:  2008-04-21       Impact factor: 5.191

5.  Utility of rifampin blood levels in the treatment and follow-up of active pulmonary tuberculosis in patients who were slow to respond to routine directly observed therapy.

Authors:  J B Mehta; H Shantaveerapa; R P Byrd; S E Morton; F Fountain; T M Roy
Journal:  Chest       Date:  2001-11       Impact factor: 9.410

6.  Non-linear pharmacokinetics of rifampicin in healthy Asian Indian volunteers.

Authors:  A Pargal; S Rani
Journal:  Int J Tuberc Lung Dis       Date:  2001-01       Impact factor: 2.373

Review 7.  Role of individual drugs in the chemotherapy of tuberculosis.

Authors:  D A Mitchison
Journal:  Int J Tuberc Lung Dis       Date:  2000-09       Impact factor: 2.373

8.  Liquid chromatographic determination of rifampin in human plasma, bronchoalveolar lavage fluid, and alveolar cells.

Authors:  J E Conte; E Lin; E Zurlinden
Journal:  J Chromatogr Sci       Date:  2000-02       Impact factor: 1.618

9.  Bactericidal and sterilizing activities of antituberculosis drugs during the first 14 days.

Authors:  Amina Jindani; Caroline J Doré; Denis A Mitchison
Journal:  Am J Respir Crit Care Med       Date:  2003-01-06       Impact factor: 21.405

10.  Low plasma concentrations of rifampicin in tuberculosis patients in Indonesia.

Authors:  R van Crevel; B Alisjahbana; W C M de Lange; F Borst; H Danusantoso; J W M van der Meer; D Burger; R H H Nelwan
Journal:  Int J Tuberc Lung Dis       Date:  2002-06       Impact factor: 2.373

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  40 in total

1.  In silico children and the glass mouse model: clinical trial simulations to identify and individualize optimal isoniazid doses in children with tuberculosis.

Authors:  Prakash M Jeena; William R Bishai; Jotam G Pasipanodya; Tawanda Gumbo
Journal:  Antimicrob Agents Chemother       Date:  2010-11-22       Impact factor: 5.191

2.  Plasma drug activity assay for treatment optimization in tuberculosis patients.

Authors:  Scott K Heysell; Charles Mtabho; Stellah Mpagama; Solomon Mwaigwisya; Suporn Pholwat; Norah Ndusilo; Jean Gratz; Rob E Aarnoutse; Gibson S Kibiki; Eric R Houpt
Journal:  Antimicrob Agents Chemother       Date:  2011-10-03       Impact factor: 5.191

Review 3.  An oracle: antituberculosis pharmacokinetics-pharmacodynamics, clinical correlation, and clinical trial simulations to predict the future.

Authors:  Jotam Pasipanodya; Tawanda Gumbo
Journal:  Antimicrob Agents Chemother       Date:  2010-10-11       Impact factor: 5.191

4.  Accurate detection of outliers and subpopulations with Pmetrics, a nonparametric and parametric pharmacometric modeling and simulation package for R.

Authors:  Michael N Neely; Michael G van Guilder; Walter M Yamada; Alan Schumitzky; Roger W Jelliffe
Journal:  Ther Drug Monit       Date:  2012-08       Impact factor: 3.681

5.  In vitro pharmacodynamics of simulated pulmonary exposures of tigecycline alone and in combination against Klebsiella pneumoniae isolates producing a KPC carbapenemase.

Authors:  Dora E Wiskirchen; Pornpan Koomanachai; Anthony M Nicasio; David P Nicolau; Joseph L Kuti
Journal:  Antimicrob Agents Chemother       Date:  2011-01-31       Impact factor: 5.191

6.  In silico models of M. tuberculosis infection provide a route to new therapies.

Authors:  Jennifer J Linderman; Denise E Kirschner
Journal:  Drug Discov Today Dis Models       Date:  2014-05-09

7.  Population modeling and simulation study of the pharmacokinetics and antituberculosis pharmacodynamics of isoniazid in lungs.

Authors:  L Lalande; L Bourguignon; S Bihari; P Maire; M Neely; R Jelliffe; S Goutelle
Journal:  Antimicrob Agents Chemother       Date:  2015-06-15       Impact factor: 5.191

8.  Pharmacokinetic Assessment of Pre- and Post-Oxygenator Vancomycin Concentrations in Extracorporeal Membrane Oxygenation: A Prospective Observational Study.

Authors:  Ahmed A Mahmoud; Sean N Avedissian; Abbas Al-Qamari; Tiffany Bohling; Michelle Pham; Marc H Scheetz
Journal:  Clin Pharmacokinet       Date:  2020-12       Impact factor: 6.447

9.  A computational tool integrating host immunity with antibiotic dynamics to study tuberculosis treatment.

Authors:  Elsje Pienaar; Nicholas A Cilfone; Philana Ling Lin; Véronique Dartois; Joshua T Mattila; J Russell Butler; JoAnne L Flynn; Denise E Kirschner; Jennifer J Linderman
Journal:  J Theor Biol       Date:  2014-12-09       Impact factor: 2.691

10.  New susceptibility breakpoints for first-line antituberculosis drugs based on antimicrobial pharmacokinetic/pharmacodynamic science and population pharmacokinetic variability.

Authors:  Tawanda Gumbo
Journal:  Antimicrob Agents Chemother       Date:  2010-01-19       Impact factor: 5.191

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