Literature DB >> 18391026

Population pharmacokinetics of rifampin in pulmonary tuberculosis patients, including a semimechanistic model to describe variable absorption.

Justin J Wilkins1, Radojka M Savic, Mats O Karlsson, Grant Langdon, Helen McIlleron, Goonaseelan Pillai, Peter J Smith, Ulrika S H Simonsson.   

Abstract

This article describes the population pharmacokinetics of rifampin in South African pulmonary tuberculosis patients. Three datasets containing 2,913 rifampin plasma concentration-time data points, collected from 261 South African pulmonary tuberculosis patients aged 18 to 72 years and weighing 28.5 to 85.5 kg and receiving regular daily treatment that included administration of rifampin (450 to 600 mg) for at least 10 days, were pooled. A compartmental pharmacokinetic model was developed using nonlinear mixed-effects modeling. Variability in the shape of the absorption curve was described using a flexible transit compartment model, in which a delay in the onset of absorption and a gradually changing absorption rate were modeled as the passage of drug through a chain of hypothetical compartments, ultimately reaching the absorption compartment. A previously described implementation was extended to allow its application to multiple-dosing data. The typical population estimate of oral clearance was 19.2 liters x h(-1), while the volume of distribution was estimated to be 53.2 liters. Interindividual variability was estimated to be 52.8% for clearance and 43.4% for volume of distribution. Interoccasional variability was estimated for CL/F (22.5%) and mean transit time during absorption (67.9%). The use of single-drug formulations was found to increase both the mean transit time (by 104%) and clearance (by 23.6%) relative to fixed-dose-combination use. A strong correlation between clearance and volume of distribution suggested substantial variability in bioavailability, which could have clinical implications, given the dependence of treatment effectiveness on exposure. The final model successfully described rifampin pharmacokinetics in the population studied and is suitable for simulation in this context.

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Year:  2008        PMID: 18391026      PMCID: PMC2415769          DOI: 10.1128/AAC.00461-07

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


  65 in total

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Journal:  Pharmacol Ther       Date:  1987       Impact factor: 12.310

4.  Models of hepatic elimination: comparison of stochastic models to describe residence time distributions and to predict the influence of drug distribution, enzyme heterogeneity, and systemic recycling on hepatic elimination.

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Journal:  Soc Sci Med       Date:  1988       Impact factor: 4.634

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Journal:  Eur J Clin Pharmacol       Date:  1983       Impact factor: 2.953

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Journal:  Pharmacol Res Commun       Date:  1978-03

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Journal:  Klin Wochenschr       Date:  1985-12-02

9.  U.S. Public Health Service Cooperative trial of three rifampin-isoniazid regimens in treatment of pulmonary tuberculosis.

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Journal:  Am Rev Respir Dis       Date:  1979-06

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Authors:  G Acocella
Journal:  Clin Pharmacokinet       Date:  1978 Mar-Apr       Impact factor: 6.447

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  76 in total

1.  Dose-ranging comparison of rifampin and rifapentine in two pathologically distinct murine models of tuberculosis.

Authors:  Ian M Rosenthal; Rokeya Tasneen; Charles A Peloquin; Ming Zhang; Deepak Almeida; Khisimuzi E Mdluli; Petros C Karakousis; Jacques H Grosset; Eric L Nuermberger
Journal:  Antimicrob Agents Chemother       Date:  2012-06-04       Impact factor: 5.191

2.  Handling data below the limit of quantification in mixed effect models.

Authors:  Martin Bergstrand; Mats O Karlsson
Journal:  AAPS J       Date:  2009-05-19       Impact factor: 4.009

3.  Implementation of dose superimposition to introduce multiple doses for a mathematical absorption model (transit compartment model).

Authors:  Jun Shen; Alison Boeckmann; Andrew Vick
Journal:  J Pharmacokinet Pharmacodyn       Date:  2012-05-04       Impact factor: 2.745

4.  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

5.  Validation and Application of a Dried Blood Spot Assay for Biofilm-Active Antibiotics Commonly Used for Treatment of Prosthetic Implant Infections.

Authors:  Ben Knippenberg; Madhu Page-Sharp; Sam Salman; Ben Clark; John Dyer; Kevin T Batty; Timothy M E Davis; Laurens Manning
Journal:  Antimicrob Agents Chemother       Date:  2016-07-22       Impact factor: 5.191

6.  Effect of adherence as measured by MEMS, ritonavir boosting, and CYP3A5 genotype on atazanavir pharmacokinetics in treatment-naive HIV-infected patients.

Authors:  R M Savic; A Barrail-Tran; X Duval; G Nembot; X Panhard; D Descamps; C Verstuyft; B Vrijens; A-M Taburet; C Goujard; F Mentré
Journal:  Clin Pharmacol Ther       Date:  2012-10-03       Impact factor: 6.875

7.  Population pharmacokinetics and dose optimization of mycophenolic acid in HCT recipients receiving oral mycophenolate mofetil.

Authors:  H Li; D E Mager; B M Sandmaier; D G Maloney; M J Bemer; J S McCune
Journal:  J Clin Pharmacol       Date:  2013-02-04       Impact factor: 3.126

8.  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

9.  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

10.  Characterizing the Pharmacokinetic Interaction Between Simeprevir and Odalasvir in Healthy Volunteers Using a Population Modeling Approach.

Authors:  Elodie Valade; Belén Valenzuela; Thomas N Kakuda; Christopher Westland; Matthew W McClure; Sivi Ouwerkerk-Mahadevan; Juan José Perez-Ruixo; Oliver Ackaert
Journal:  AAPS J       Date:  2018-10-22       Impact factor: 4.009

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