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- Tunblad, Karin, et al.
(författare)
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Influence of probenecid on the delivery of morphine-6-glucuronide to the brain
- 2005
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Ingår i: European Journal of Pharmaceutical Sciences. - : Elsevier BV. - 0928-0987 .- 1879-0720. ; 24:1, s. 49-57
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Tidskriftsartikel (refereegranskat)abstract
- The objective was to evaluate the influence of probenecid on the blood-brain barrier (BBB) transport of morphine-6-glucuronide (M6G). Microdialysis probes were placed in the striatum and into the jugular vein of Sprague-Dawley rats. Each probe was calibrated in vivo using retrodialysis by drug. M6G was administered as a 4-h exponential i.v. infusion, and the experiment was repeated the following day with the addition of probenecid. The data were analysed using NONMEM. An integrated model including the total arterial concentrations, the dialysate concentrations in brain and blood, and the recovery measurements, was developed. The extent of BBB transport, expressed as the ratio between clearance into the brain and clearance out of the brain (CL(in)/CL(out)), was estimated as 0.29 on both days, indicating that efflux transporters act on M6G at the BBB. However, the probenecid-sensitive transporters are not involved in the brain efflux, as the ratio was unaltered although probenecid was co-administered. In contrast, the systemic elimination of M6G decreased by 22% (p<0.05) upon probenecid co-administration. The half-life of M6G was longer in the brain than in blood on both experimental days (p<0.05). In conclusion, probenecid decreased the systemic elimination of M6G, but had no effect on the BBB transport of M6G.
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| 2. |
- Tunblad, Karin, et al.
(författare)
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The use of clinical irrelevance criteria in covariate model building with application to dofetilide pharmacokinetic data
- 2008
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 35:5, s. 503-526
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Tidskriftsartikel (refereegranskat)abstract
- To characterise the pharmacokinetics of dofetilide in patients and to identify clinically relevant parameter-covariate relationships. To investigate three different modelling strategies in covariate model building using dofetilide as an example: (1) using statistical criteria only or in combination with clinical irrelevance criteria for covariate selection, (2) applying covariate effects on total clearance or separately on non-renal and renal clearances and (3) using separate data sets for covariate selection and parameter estimation. Pooled concentration-time data (1,445 patients, 10,133 observations) from phase III clinical trials was used. A population pharmacokinetic model was developed using NONMEM. Stepwise covariate model building was applied to identify important covariates using the strategies described above. Inclusion and exclusion of covariates using clinical irrelevance was based on reduction in interindividual variability and changes in parameters at the extremes of the covariate distribution. Parametric separation of the elimination pathways was accomplished using creatinine clearance as an indicator of renal function. The pooled data was split in three parts which were used for covariate selection, parameter estimation and evaluation of predictive performance. Parameter estimations were done using the first-order (FO) and the first-order conditional estimation (FOCE) methods. A one-compartment model with first order absorption adequately described the data. Using clinical irrelevance criteria resulted in models containing less parameter-covariate relationships with a minor loss in predictive power. A larger number of covariates were found significant when the elimination was divided into a renal part and a non-renal part, but no gain in predictive power could be seen with this data set. The FO and FOCE estimation methods gave almost identical final covariate model structures with similar predictive performance. Clinical irrelevance criteria may be valuable for practical reasons since stricter inclusion/exclusion criteria shortens the run times of the covariate model building procedure and because only the covariates important for the predictive performance are included in the model.
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