Change search
CiteExportLink to record
Permanent link

Direct link
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
A multistate tuberculosis pharmacometric model: a framework for studying anti-tubercular drug effects in vitro
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (farmakometri)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Show others and affiliations
2016 (English)In: Journal of Antimicrobial Chemotherapy, ISSN 0305-7453, E-ISSN 1460-2091, Vol. 71, no 4, 964-974 p.Article in journal (Refereed) Published
Abstract [en]

OBJECTIVES: Mycobacterium tuberculosis can exist in different states in vitro, which can be denoted as fast multiplying, slow multiplying and non-multiplying. Characterizing the natural growth of M. tuberculosis could provide a framework for accurate characterization of drug effects on the different bacterial states.

METHODS: The natural growth data of M. tuberculosis H37Rv used in this study consisted of viability defined as cfu versus time based on data from an in vitro hypoxia system. External validation of the natural growth model was conducted using data representing the rate of incorporation of radiolabelled methionine into proteins by the bacteria. Rifampicin time-kill curves from log-phase (0.25-16 mg/L) and stationary-phase (0.5-64 mg/L) cultures were used to assess the model's ability to describe drug effects by evaluating different linear and non-linear exposure-response relationships.

RESULTS: The final pharmacometric model consisted of a three-compartment differential equation system representing fast-, slow- and non-multiplying bacteria. Model predictions correlated well with the external data (R(2) = 0.98). The rifampicin effects on log-phase and stationary-phase cultures were separately and simultaneously described by including the drug effect on the different bacterial states. The predicted reduction in log10 cfu after 14 days and at 0.5 mg/L was 2.2 and 0.8 in the log-phase and stationary-phase systems, respectively.

CONCLUSIONS: The model provides predictions of the change in bacterial numbers for the different bacterial states with and without drug effect and could thus be used as a framework for studying anti-tubercular drug effects in vitro.

Place, publisher, year, edition, pages
2016. Vol. 71, no 4, 964-974 p.
National Category
Pharmaceutical Sciences Infectious Medicine
URN: urn:nbn:se:uu:diva-273316DOI: 10.1093/jac/dkv416ISI: 000374232500018PubMedID: 26702921OAI: diva2:894730
Swedish Research Council, 521-2011-3442EU, FP7, Seventh Framework Programme, 115337
Available from: 2016-01-15 Created: 2016-01-15 Last updated: 2017-11-30Bibliographically approved
In thesis
1. Novel Pharmacometric Methods for Informed Tuberculosis Drug Development
Open this publication in new window or tab >>Novel Pharmacometric Methods for Informed Tuberculosis Drug Development
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

With approximately nine million new cases and the attributable cause of death of an estimated two millions people every year there is an urgent need for new and effective drugs and treatment regimens targeting tuberculosis. The tuberculosis drug development pathway is however not ideal, containing non-predictive model systems and unanswered questions that may increase the risk of failure during late-phase drug development. The aim of this thesis was hence to develop pharmacometric tools in order to optimize the development of new anti-tuberculosis drugs and treatment regimens.

The General Pulmonary Distribution model was developed allowing for prediction of both rate and extent of distribution from plasma to pulmonary tissue. A distribution characterization that is of high importance as most current used anti-tuberculosis drugs were introduced into clinical use without considering the pharmacokinetic properties influencing drug distribution to the site of action. The developed optimized bronchoalveolar lavage sampling design provides a simplistic but informative approach to gathering of the data needed to allow for a model based characterization of both rate and extent of pulmonary distribution using as little as one sample per subject. The developed Multistate Tuberculosis Pharmacometric model provides predictions over time for a fast-, slow- and non-multiplying bacterial state with and without drug effect. The Multistate Tuberculosis Pharmacometric model was further used to quantify the in vitro growth of different strains of Mycobacterium tuberculosis and the exposure-response relationships of three first line anti-tuberculosis drugs. The General Pharmacodynamic Interaction model was successfully used to characterize the pharmacodynamic interactions of three first line anti-tuberculosis drugs, showing the possibility of distinguishing drug A’s interaction with drug B from drug B’s interaction with drug A. The successful separation of all three drugs effect on each other is a necessity for future work focusing on optimizing the selection of anti-tuberculosis combination regimens.

With a focus on pharmacokinetics and pharmacodynamics, the work included in this thesis provides multiple new methods and approaches that individually, but maybe more important the combination of, has the potential to inform development of new but also to provide additional information of the existing anti-tuberculosis drugs and drug regimen.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2016. 70 p.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 222
pharmacokinetics, pharmacodynamics, PKPD, pharmacometric, nonlinear mixed-effects models, multistate tuberculosis pharmacometric model, general pharmacodynamic interaction model, general pulmonary distribution model, tuberculosis, rifampicin, isoniazid, ethambutol
National Category
Pharmaceutical Sciences
Research subject
Pharmaceutical Science
urn:nbn:se:uu:diva-303872 (URN)978-91-554-9718-7 (ISBN)
Public defence
2016-11-25, B42, BMC, Husargatan 3, Uppsala, 13:15 (English)
Available from: 2016-11-03 Created: 2016-09-26 Last updated: 2016-11-16

Open Access in DiVA

fulltext(557 kB)