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- Hamrén, Bengt, et al.
(författare)
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Mechanistic modelling of tesaglitazar pharmacokinetic data in subjects with various degrees of renal function : evidence of interconversion
- 2008
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Ingår i: British Journal of Clinical Pharmacology. - : Wiley. - 0306-5251 .- 1365-2125. ; 65:6, s. 855-863
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Tidskriftsartikel (refereegranskat)abstract
- AIMS To develop a mechanistic pharmacokinetic (PK) model for tesaglitazar and its metabolite (an acyl glucuronide) following oral administration of tesaglitazar to subjects with varying renal function, and derive an explanation for the increased plasma exposure of tesaglitazar in subjects with impaired renal function. METHODS Data were from a 6-week study in subjects with renal insufficiency and matched controls undergoing repeated oral dosing with tesaglitazar (n = 41). Compartmental population PK modelling was employed to describe the PK of tesaglitazar and its metabolite, in plasma and urine, simultaneously. Two hypotheses were tested to investigate the increased exposure of tesaglitazar in subjects with renal functional impairment: tesaglitazar metabolism is correlated with renal function, or metabolite elimination is reduced in renal insufficiency, leading to increased hydrolysis (interconversion) to the parent compound via biliary circulation. RESULTS The hypothesis for interconversion was best supported by the data. The population PK model included first-order absorption, two-compartment disposition and separate renal (0.027 l h(-1)) and metabolic (1.9 l h(-1)) clearances for tesaglitazar. The model for the metabolite; one-compartment disposition with renal (saturable, V-max = 0.19 mu mol l(-1) and Km = 0.04 mmol l(-1)) and nonrenal clearances (1.2 l h(-1)), biliary secretion (12 h(-1)) to the gut, where interconversion and reabsorption (0.8 h(-1)) of tesaglitazar occurred. CONCLUSION A mechanistic population PK model for tesaglitazar and its metabolite was developed in subjects with varying degrees of renal insufficiency. The model and data give insight into the likely mechanism (interconversion) of the increased tesaglitazar exposure in renally impaired subjects, and separate elimination and interconversion processes without dosing of the metabolite.
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| 3. |
- Parkinson, Joanna, et al.
(författare)
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Application of the integrated glucose–insulin model for cross-study characterization of T2DM patients on metformin background treatment
- 2016
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Ingår i: British Journal of Clinical Pharmacology. - : Wiley. - 0306-5251 .- 1365-2125. ; 82, s. 1613-1624
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Tidskriftsartikel (refereegranskat)abstract
- © 2016 The British Pharmacological Society Aim: The integrated glucose–insulin (IGI) model is a semi-mechanistic physiological model which can describe the glucose–insulin homeostasis system following various glucose challenge settings. The aim of the present work was to apply the model to a large and diverse population of metformin-only-treated type 2 diabetes mellitus (T2DM) patients and identify patient-specific covariates. Methods: Data from four clinical studies were pooled, including glucose and insulin concentration–time profiles from T2DM patients on stable treatment with metformin alone following mixed-meal tolerance tests. The data were collected from a wide range of patients with respect to the duration of diabetes and level of glycaemic control. Results: The IGI model was expanded by four patient-specific covariates. The level of glycaemic control, represented by baseline glycosylated haemoglobin was identified as a significant covariate for steady-state glucose, insulin-dependent glucose clearance and the magnitude of the incretin effect, while baseline body mass index was a significant covariate for steady-state insulin levels. In addition, glucose dose was found to have an impact on glucose absorption rate. The developed model was used to simulate glucose and insulin profiles in different groups of T2DM patients, across a range of glycaemic control, and it was found accurately to characterize their response to the standard oral glucose challenge. Conclusions: The IGI model was successfully applied to characterize differences between T2DM patients across a wide range of glycaemic control. The addition of patient-specific covariates in the IGI model might be valuable for the future development of antidiabetic treatment and for the design and simulation of clinical studies.
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