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Individualised dosing algorithm and personalised treatment of high-dose rifampicin for tuberculosis
Uppsala Univ, Sweden.
Linköping University, Department of Clinical and Experimental Medicine, Division of Microbiology, Infection and Inflammation. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Infectious Diseases.
Karolinska Inst, Sweden; Karolinska Univ Hosp Solna, Sweden.
Karolinska Inst, Sweden; Karolinska Univ Hosp Solna, Sweden.
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2019 (English)In: British Journal of Clinical Pharmacology, ISSN 0306-5251, E-ISSN 1365-2125Article in journal (Refereed) Epub ahead of print
Abstract [en]

Aims To propose new exposure targets for Bayesian dose optimisation suited for high-dose rifampicin and to apply them using measured plasma concentrations coupled with a Bayesian forecasting algorithm allowing predictions of future doses, considering rifampicins auto-induction, saturable pharmacokinetics and high interoccasion variability. Methods Rifampicin exposure targets for Bayesian dose optimisation were defined based on literature data on safety and anti-mycobacterial activity in relation to rifampicins pharmacokinetics i.e. highest plasma concentration up to 24 hours and area under the plasma concentration-time curve up to 24 hours (AUC(0-24h)). 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 exposure target for Bayesian dose optimisation was a steady state AUC(0-24h) of 181-214 h x 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 (20-50 mg/kg) with a mode of 1800 mg (30 mg/kg, 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 AUC(0-24h)-only target. Conclusions A new up-to-date exposure target for Bayesian dose optimisation suited for high-dose rifampicin was derived. Using measured plasma concentrations coupled with Bayesian forecasting allowed prediction of the future dose whilst accounting for the auto-induction, saturable pharmacokinetics and high between-occasion variability of rifampicin.

Place, publisher, year, edition, pages
WILEY , 2019.
Keywords [en]
clinical pharmacology; modelling and simulation; pharmacodynamics; pharmacokinetics; pharmacometrics; population analysis; therapeutic drug monitoring
National Category
Pharmaceutical Sciences
Identifiers
URN: urn:nbn:se:liu:diva-159723DOI: 10.1111/bcp.14048ISI: 000478463100001PubMedID: 31269277OAI: oai:DiVA.org:liu-159723DiVA, id: diva2:1343830
Note

Funding Agencies|Research Council of Southeast Sweden (FORSS); Marianne and Marcus Wallenberg Foundation; Swedish Heart and Lung Foundation; Swedish Research Council; Stockholm County Council [ALF20160331]

Available from: 2019-08-19 Created: 2019-08-19 Last updated: 2019-08-19

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Niward, KatarinaPaues, JakobSchön, Thomas
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Division of Microbiology, Infection and InflammationFaculty of Medicine and Health SciencesDepartment of Infectious Diseases
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