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Individualised dosing algorithm and personalised treatment of rifampicin for tuberculosis
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Farmakometri)
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

Aims: To propose new Bayesian TDM targets well-suited for high-dose rifampicin and to apply them using a TDM coupled with Bayesian forecasting algorithm allowing predictions of future doses, considering rifampicin’s auto-induction, saturable pharmacokinetics and high inter-occasion variability. Methods: Rifampicin Bayesian TDM targets were defined based on literature data on safety and anti-mycobacterial activity in relation to rifampicin’s pharmacokinetics i.e. highest plasma concentration during 24 hours (Cmax) and area under the plasma concentration-time curve during 24 hours (AUC0-24h). Targets were suggested with and without considering minimum inhibitory concentration (MIC) information. Individual optimal doses were predicted for patients treated with rifampicin (10 mg/kg) using the targets with Bayesian forecasting together with sparse measurements of rifampicin plasma concentrations and baseline rifampicin MIC. Results: The suggested Bayesian TDM target was a steady state AUC0-24h of 181-214 h×mg/L. The observed MICs ranged from 0.016-0.125 mg/L (mode: 0.064 mg/L). The predicted optimal dose in patients using the suggested target ranged from 1200-3000 mg (mode 1800 mg, n=24). The predicted optimal doses when taking MIC into account were highly dependent on the known technical variability of measured individual MIC and the dose was substantially lower compared to when using the AUC0-24h-only target. Conclusions: A new up-to-date Bayesian TDM target well-suited for high-dose rifampicin was derived. The TDM coupled with Bayesian forecasting approach allowed prediction of the future dose whilst accounting for the auto-induction, saturable pharmacokinetics and high between-occasion variability of rifampicin.

Keywords [en]
Therapeutic drug monitoring, Pharmacokinetics, Modelling and simulation, Pharmacometrics, Population pharmacokinetics
National Category
Pharmaceutical Sciences
Research subject
Pharmaceutical Science
Identifiers
URN: urn:nbn:se:uu:diva-379357OAI: oai:DiVA.org:uu-379357DiVA, id: diva2:1296395
Available from: 2019-03-15 Created: 2019-03-15 Last updated: 2019-03-15
In thesis
1. Pharmacometric Models to Improve the Treatment and Development of Drugs against Tuberculosis
Open this publication in new window or tab >>Pharmacometric Models to Improve the Treatment and Development of Drugs against Tuberculosis
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

With 10 million new infections yearly, tuberculosis has a major impact on the human well-being of the world. Most patients have infections susceptible to a first-line treatment with a treatment success rate of 80%, a number that can potentially be improved by optimising the first-line treatment. Besides susceptible disease, each year half a million patients are infected by tuberculosis with resistance to first-line treatment where only 50% of patients get cured. Thus, new drugs against resistant tuberculosis are desperately needed but given the inefficiency of developing new anti-tuberculosis drugs, enough new drugs will not reach patients in time. The aim of this thesis was to develop pharmacometric models to optimise the development and use of current and future drugs for treating tuberculosis.

A population pharmacokinetic model for rifampicin, the most prominent first-line drug, was developed and later used for developing exposure-response models followed by clinical trial simulations. The developed exposure-response models were based on liquid culture data and were expanded to describe the relationship between liquid culture results and a new biomarker, the molecular bacterial load assay which is a quicker alternative to liquid culture and is also contamination-free.

The in vitro-derived semi-mechanistic Multistate Tuberculosis Pharmacometric (MTP) model was applied to clinical rifampicin and clofazimine colony forming unit datasets. This novel application of the MTP model allowed detection of statistically significant exposure-response relationships between rifampicin and clofazimine for the specific killing of non-multiplying, persister bacteria. Furthermore, the MTP model was compared to conventional statistical analyses for detecting drug effects in Phase IIa. If designing and analysing Phase IIa using the MTP model, the required sample size for detecting drug effects can be lowered. An improved design and analysis of pre-clinical treatment outcome assessments was developed which increased the information gain compared to a conventional design yet kept the animal use at a minimum. Lastly, a therapeutic drug monitoring approach was suggested based on updated targets for rifampicin, a framework easily expandable to second-line drugs.

In conclusion this thesis presents the development of pharmacometric models which will streamline both the development and use of drugs against tuberculosis.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2019. p. 77
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 267
Keywords
Pharmacokinetics, Pharmacodynamics, Biomarkers, Rifampicin, Clofazimine, Therapeutic drug monitoring, Time-to-event, Time-to-positivity, Molecular bacterial load assay
National Category
Pharmaceutical Sciences
Research subject
Pharmaceutical Science
Identifiers
urn:nbn:se:uu:diva-379359 (URN)978-91-513-0598-1 (ISBN)
Public defence
2019-05-03, B21, BMC, Husargatan 3, Uppsala, 09:15 (English)
Opponent
Supervisors
Available from: 2019-04-12 Created: 2019-03-15 Last updated: 2019-05-07

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Svensson, Robin J.Simonsson, Ulrika S H

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