Literature DB >> 23357766

A physiologically based pharmacokinetic model of rifampin in mice.

Michael A Lyons1, Brad Reisfeld, Raymond S H Yang, Anne J Lenaerts.   

Abstract

One problem associated with regimen-based development of antituberculosis (anti-TB) drugs is the difficulty of a systematic and thorough in vivo evaluation of the large number of possible regimens that arise from consideration of multiple drugs tested together. A mathematical model capable of simulating the pharmacokinetics and pharmacodynamics of experimental combination chemotherapy of TB offers a way to mitigate this problem by extending the use of available data to investigate regimens that are not initially tested. In order to increase the available mathematical tools needed to support such a model for preclinical anti-TB drug development, we constructed a preliminary whole-body physiologically based pharmacokinetic (PBPK) model of rifampin in mice, using data from the literature. Interindividual variability was approximated using Monte Carlo (MC) simulation with assigned probability distributions for the model parameters. An MC sensitivity analysis was also performed to determine correlations between model parameters and plasma concentration to inform future model development. Model predictions for rifampin concentrations in plasma, liver, kidneys, and lungs, following oral administration, were generally in agreement with published experimental data from multiple studies. Sensitive model parameters included those descriptive of oral absorption, total clearance, and partitioning of rifampin between blood and muscle. This PBPK model can serve as a starting point for the integration of rifampin pharmacokinetics in mice into a larger mathematical framework, including the immune response to Mycobacterium tuberculosis infection, and pharmacokinetic models for other anti-TB drugs.

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Year:  2013        PMID: 23357766      PMCID: PMC3623338          DOI: 10.1128/AAC.01567-12

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


  58 in total

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Authors:  Raymond S H Yang; Hisham A El-Masri; Russell S Thomas; Ivan D Dobrev; James E Dennison; Dong-Soon Bae; Julie A Campain; Kai H Liao; Brad Reisfeld; Melvin E Andersen; Moiz Mumtaz
Journal:  Environ Toxicol Pharmacol       Date:  2004-11       Impact factor: 4.860

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Authors:  Simeone Marino; Jennifer J Linderman; Denise E Kirschner
Journal:  Wiley Interdiscip Rev Syst Biol Med       Date:  2010-12-31

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7.  Mouse model of necrotic tuberculosis granulomas develops hypoxic lesions.

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Review 8.  Update on rifampin and rifabutin drug interactions.

Authors:  Anne M Baciewicz; Cary R Chrisman; Christopher K Finch; Timothy H Self
Journal:  Am J Med Sci       Date:  2008-02       Impact factor: 2.378

9.  Pharmacokinetics-pharmacodynamics of rifampin in an aerosol infection model of tuberculosis.

Authors:  Ramesh Jayaram; Sheshagiri Gaonkar; Parvinder Kaur; B L Suresh; B N Mahesh; R Jayashree; Vrinda Nandi; Sowmya Bharat; R K Shandil; E Kantharaj; V Balasubramanian
Journal:  Antimicrob Agents Chemother       Date:  2003-07       Impact factor: 5.191

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  16 in total

1.  Predicting the Disposition of the Antimalarial Drug Artesunate and Its Active Metabolite Dihydroartemisinin Using Physiologically Based Pharmacokinetic Modeling.

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Journal:  Antimicrob Agents Chemother       Date:  2021-02-17       Impact factor: 5.191

2.  Physiologically Based Pharmacokinetic Model of Rifapentine and 25-Desacetyl Rifapentine Disposition in Humans.

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Journal:  Antimicrob Agents Chemother       Date:  2016-07-22       Impact factor: 5.191

3.  A multi-scale approach to designing therapeutics for tuberculosis.

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Journal:  Integr Biol (Camb)       Date:  2015-04-30       Impact factor: 2.192

4.  Computational pharmacokinetics/pharmacodynamics of rifampin in a mouse tuberculosis infection model.

Authors:  Michael A Lyons; Anne J Lenaerts
Journal:  J Pharmacokinet Pharmacodyn       Date:  2015-05-31       Impact factor: 2.745

5.  A Comparative Analysis of Physiologically Based Pharmacokinetic Models for Human Immunodeficiency Virus and Tuberculosis Infections.

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

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Journal:  J Theor Biol       Date:  2014-12-09       Impact factor: 2.691

7.  Characterizing the Effects of Race/Ethnicity on Acetaminophen Pharmacokinetics Using Physiologically Based Pharmacokinetic Modeling.

Authors:  Todd J Zurlinden; Brad Reisfeld
Journal:  Eur J Drug Metab Pharmacokinet       Date:  2017-02       Impact factor: 2.441

8.  Synergistic Antimicrobial Activity of Colistin in Combination with Rifampin and Azithromycin against Escherichia coli Producing MCR-1.

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9.  Pregnancy-specific physiologically-based toxicokinetic models for bisphenol A and bisphenol S.

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10.  Can the duration of tuberculosis treatment be shortened with higher dosages of rifampicin?

Authors:  Noton K Dutta; Petros C Karakousis
Journal:  Front Microbiol       Date:  2015-10-14       Impact factor: 5.640

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