ArticleBritish journal of clinical pharmacology2019
Individualised dosing algorithm and personalised treatment of high-dose rifampicin for tuberculosis.
Article in British journal of clinical pharmacology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Toward a Clinical Decision Support System for Monitoring Therapeutic Antituberculosis Medical Drugs in Tanzania (Project TuberXpert): Protocol for an Algorithm' Development and Implementation.JMIR research protocols · 2024Article
- Towards Model-Informed Precision Dosing of Voriconazole: Challenging Published Voriconazole Nonlinear Mixed-Effects Models with Real-World Clinical Data.Clinical pharmacokinetics · 2023Article
- Model-Informed Precision Dosing of Linezolid in Patients with Drug-Resistant Tuberculosis.Pharmaceutics · 2022Article
- Article
- Pharmacokinetics and pharmacodynamics of anti-tuberculosis drugs: An evaluation ofFrontiers in pharmacology · 2022Review
- A high-throughput screening assay based on automated microscopy for monitoring antibiotic susceptibility of Mycobacterium tuberculosis phenotypes.BMC microbiology · 2021Article
- Individualized Dosing With High Inter-Occasion Variability Is Correctly Handled With Model-Informed Precision Dosing-Using Rifampicin as an Example.Frontiers in pharmacology · 2020Article
- Model-Based Biomarker Selection for Dose Individualization of Tyrosine-Kinase Inhibitors.Frontiers in pharmacology · 2020Article
- Individualised dosing algorithm and personalised treatment of high-dose rifampicin for tuberculosis.British journal of clinical pharmacology · 2019Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsTo 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 rifampicin's auto-induction, saturable pharmacokinetics and high interoccasion variability.
methodsRifampicin exposure targets for Bayesian dose optimisation were defined based on literature data on safety and anti-mycobacterial activity in relation to rifampicin's pharmacokinetics i.e. highest plasma concentration up to 24 hours and area under the plasma concentration-time curve up to 24 hours (AUC
resultsThe suggested exposure target for Bayesian dose optimisation was a steady state AUC
conclusionsA 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.
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