| 1. |
- Björnsson, Marcus A., et al.
(författare)
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Modelling of pain intensity and informative dropout in a dental pain model after naproxcinod, naproxen and placebo administration
- 2011
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Ingår i: British Journal of Clinical Pharmacology. - : Wiley. - 0306-5251 .- 1365-2125. ; 71:6, s. 899-906
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Tidskriftsartikel (refereegranskat)abstract
- AIMS To describe pain intensity (PI) measured on a visual analogue scale (VAS) and dropout due to request for rescue medication after administration of naproxcinod, naproxen or placebo in 242 patients after wisdom tooth removal. METHODS Non-linear mixed effects modelling was used to describe the plasma concentrations of naproxen, either formed from naproxcinod or from naproxen itself, and their relationship to PI and dropout. Goodness of fit was assessed by simultaneous simulations of PI and dropout. RESULTS Baseline PI for the typical patient was 52.7 mm. The PI was influenced by placebo effects, using an exponential model, and by naproxen concentrations using a sigmoid E-max model. Typical maximal placebo effect was a decrease in PI by 20.2%, with an onset rate constant of 0.237 h-1. EC50 was 0.135 mu mol l-1. A Weibull time-to-event model was used for the dropout, where the hazard was dependent on the predicted PI and by the PI at baseline. Since the dropout was not at random, it was necessary to include the simulated dropout in visual predictive checks (VPC) of PI. CONCLUSIONS This model describes the relationship between drug effects, PI and the likelihood of dropout after naproxcinod, naproxen and placebo administration. The model provides an opportunity to describe the effects of other doses or formulations, after dental extraction. VPC created by simultaneous simulations of PI and dropout provides a good way of assessing the goodness of fit when there is informative dropout.
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| 2. |
- Wilkins, Justin J., et al.
(författare)
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Variability in the population pharmacokinetics of isoniazid in South African tuberculosis patients
- 2011
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Ingår i: British Journal of Clinical Pharmacology. - : Wiley. - 0306-5251 .- 1365-2125. ; 72:1, s. 51-62
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Tidskriftsartikel (refereegranskat)abstract
- AIM This study was designed to characterize the population pharmacokinetics of isoniazid in South African pulmonary tuberculosis patients. METHODS Concentration-time measurements obtained from 235 patients receiving oral doses of isoniazid as part of routine tuberculosis chemotherapy in two clinical studies were pooled and subjected to nonlinear mixed-effects analysis. RESULTS A two-compartmental model, including first-order absorption and elimination with allometric scaling, was found to describe the observed dose-exposure relationship for oral isoniazid adequately. A mixture model was used to characterize dual rates of isoniazid elimination. Estimates of apparent clearance in slow and fast eliminators were 9.70 and 21.6 l h(-1), respectively. The proportion of fast eliminators in the population was estimated to be 13.2%. Central volume of distribution was estimated to be 10% smaller in female patients and clearance was found to be 17% lower in patients with HIV. Variability in absorption rate (90%) was completely interoccasional in nature, whereas in relative bioavailability, interoccasional variability (8.4%) was lower than interindividual variability (26%). Oral doses, given once daily according to dosing policies at the time, were sufficient to reach therapeutic concentrations in the majority of the studied population, regardless of eliminator phenotype. Simulations suggested that current treatment guidelines (5 mg kg(-1)) may be suboptimal in fast eliminators with low body weight. CONCLUSIONS A population pharmacokinetic model was developed to characterize the highly variable pharmacokinetics of isoniazid in a South African pulmonary tuberculosis patient population. Current treatment guidelines may lead to underexposure in rapid isoniazid eliminators.
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| 3. |
- Svensson, Robin J., et al.
(författare)
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Individualised dosing algorithm and personalised treatment of high‐dose rifampicin for tuberculosis
- 2019
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Ingår i: British Journal of Clinical Pharmacology. - : Wiley. - 0306-5251 .- 1365-2125. ; 85:10, s. 2341-2350
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Tidskriftsartikel (refereegranskat)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 (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.ResultsThe suggested exposure target for Bayesian dose optimisation 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 (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 AUC0–24h‐only target.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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