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Personalized Tuberculosis Treatment Through Model-Informed Dosing of Rifampicin
Radboud Univ Nijmegen, Radboud Inst Hlth Sci, Dept Pharm, Med Ctr, Nijmegen, Netherlands.ORCID iD: 0000-0001-8020-1934
Radboud Univ Nijmegen, Radboud Inst Hlth Sci, Dept Pharm, Med Ctr, Nijmegen, Netherlands.
InsightRX, San Francisco, CA USA.
Radboud Univ Nijmegen, Dept Resp Dis, Med Ctr Dekkerswald, Groesbeek, Netherlands.
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2019 (English)In: Clinical Pharmacokinetics, ISSN 0312-5963, E-ISSN 1179-1926, Vol. 58, no 6, p. 815-826Article in journal (Refereed) Published
Abstract [en]

Background and objective: Thisstudy proposes a model-informed approach for therapeutic drug monitoring (TDM) of rifampicin to improve tuberculosis (TB) treatment.

Methods: Two datasets from pulmonary TB patients were used: a pharmacokinetic study (34 patients, 373 samples), and TDM data (96 patients, 391 samples) collected at Radboud University Medical Center, The Netherlands. Nine suitable population pharmacokinetic models of rifampicin were identified in the literature and evaluated on the datasets. A model developed by Svensson et al. was found to be the most suitable based on graphical goodness of fit, residual diagnostics, and predictive performance. Prediction of individual area under the concentration-time curve from time zero to 24h (AUC(24)) and maximum concentration (C-max) employing various sampling strategies was compared with a previously established linear regression TDM strategy, using sampling at 2, 4, and 6h, in terms of bias and precision (mean error [ME] and root mean square error [RMSE]).

Results: A sampling strategy using 2- and 4-h blood collection was selected to be the most suitable. The bias and precision of the two strategies were comparable, except that the linear regression strategy was more biased in prediction of the AUC(24)than the model-informed approach (ME of 9.9% and 1.5%, respectively). A comparison of resulting dose advice, using predictions on a simulated dataset, showed no significant difference in sensitivity or specificity between the two methods. The model was successfully implemented in the InsightRX precision dosing platform.

Conclusion: Blood sampling at 2 and 4h, combined with model-based prediction, can be used instead of the currently used linear regression strategy, shortening the sampling by 2h and one sampling point without performance loss while simultaneously offering flexibility in sampling times.

Place, publisher, year, edition, pages
ADIS INT LTD , 2019. Vol. 58, no 6, p. 815-826
National Category
Pharmacology and Toxicology
Identifiers
URN: urn:nbn:se:uu:diva-385001DOI: 10.1007/s40262-018-00732-2ISI: 000467628400009PubMedID: 30671890OAI: oai:DiVA.org:uu-385001DiVA, id: diva2:1322960
Available from: 2019-06-11 Created: 2019-06-11 Last updated: 2019-06-11Bibliographically approved

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