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Pharmakokinetik von Mycophenolatmofetil nach primärer orthotoper Herztransplantation – Entwicklung von Kurzalgorithmen zur therapeutischen Arzneimittelüberwachung–. Eine prospektive Studie zur Untersuchung der Pharmakokinetik von Mycophenolatmofetil nach primärer orthotoper Herztransplantation mit Tacrolimus als Begleitmedikation
Pharmakokinetik von Mycophenolatmofetil nach primärer orthotoper Herztransplantation – Entwicklung von Kurzalgorithmen zur therapeutischen Arzneimittelüberwachung–. Eine prospektive Studie zur Untersuchung der Pharmakokinetik von Mycophenolatmofetil nach primärer orthotoper Herztransplantation mit Tacrolimus als Begleitmedikation
Pharmacokinetics of mycophenolate mofetil (MMF) show large interindividual variability. Concentration-controlled dosing of MMF based on routine therapeutic drug monitoring, which requires area under the concentration-time curve (mycophenolic acid [MPA]-AUC0-12h) determinations, is uncommon. Dose adjustments are based on predose concentrations (C0h) or side effects. The aim of this study was to compare C0h with postdose concentrations (C0.5h-C12h) and to develop practical methods for estimation of MPA-AUCs on the basis of a limited sampling strategy (LSS) in heart transplant recipients under MMF and tacrolimus maintenance immunosuppression. Full MPA-AUC0-12h profiles were generated by high-performance liquid chromatography in 28 patients. Statistical analysis for MPA-AUC0-12h was performed by a case resampling bootstrap method. Bland and Altmann analysis was performed to test agreement between "predicted AUC" and "measured AUC." C1h provided the highest coefficient of determination (r2 = 0.57) among the concentrations determined during the 12-hour interval, which were correlated with AUC. All other MPA levels were better surrogates of the MPA-AUC0-12h when compared with C0h (r2 = 0.14). The best estimation of MPA-AUC0-12h was achieved with four sampling points with the algorithm AUC = 1.25*C1h + 5.29*C4h + 2.90*C8h + 3.61*C10h (r2 = 0.95). Since LSS with four time points appeared unpractical, the authors prefer models with three or two points. To optimize practicability, LSS with sample points within the first 2 hours were evaluated resulting in the algorithms: AUC = 1.09*C0.5h + 1.19*C1h + 3.60*C2h (r2 = 0.84) and AUC = 1.65*C0.5h + 4.74*C2h (r2 = 0.75) for three and two sample points, respectively. The results provide strong evidence for the use of either LSS or the use of time points other than C0h for therapeutic drug monitoring of MMF. Using the algorithms for the estimation of MPA-AUC0-12h based on LSS within the first 2 hours after MMF dosing may help to optimize treatment with MMF by individualization of dosing.
Mycophenolate mofetil, MMF, MPA-AUC0-12h, heart transplantation, HTx
Bigdeli, Amir Khosrow
2010
Deutsch
Universitätsbibliothek der Ludwig-Maximilians-Universität München
Bigdeli, Amir Khosrow (2010): Pharmakokinetik von Mycophenolatmofetil nach primärer orthotoper Herztransplantation – Entwicklung von Kurzalgorithmen zur therapeutischen Arzneimittelüberwachung–: Eine prospektive Studie zur Untersuchung der Pharmakokinetik von Mycophenolatmofetil nach primärer orthotoper Herztransplantation mit Tacrolimus als Begleitmedikation. Dissertation, LMU München: Medizinische Fakultät
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Abstract

Pharmacokinetics of mycophenolate mofetil (MMF) show large interindividual variability. Concentration-controlled dosing of MMF based on routine therapeutic drug monitoring, which requires area under the concentration-time curve (mycophenolic acid [MPA]-AUC0-12h) determinations, is uncommon. Dose adjustments are based on predose concentrations (C0h) or side effects. The aim of this study was to compare C0h with postdose concentrations (C0.5h-C12h) and to develop practical methods for estimation of MPA-AUCs on the basis of a limited sampling strategy (LSS) in heart transplant recipients under MMF and tacrolimus maintenance immunosuppression. Full MPA-AUC0-12h profiles were generated by high-performance liquid chromatography in 28 patients. Statistical analysis for MPA-AUC0-12h was performed by a case resampling bootstrap method. Bland and Altmann analysis was performed to test agreement between "predicted AUC" and "measured AUC." C1h provided the highest coefficient of determination (r2 = 0.57) among the concentrations determined during the 12-hour interval, which were correlated with AUC. All other MPA levels were better surrogates of the MPA-AUC0-12h when compared with C0h (r2 = 0.14). The best estimation of MPA-AUC0-12h was achieved with four sampling points with the algorithm AUC = 1.25*C1h + 5.29*C4h + 2.90*C8h + 3.61*C10h (r2 = 0.95). Since LSS with four time points appeared unpractical, the authors prefer models with three or two points. To optimize practicability, LSS with sample points within the first 2 hours were evaluated resulting in the algorithms: AUC = 1.09*C0.5h + 1.19*C1h + 3.60*C2h (r2 = 0.84) and AUC = 1.65*C0.5h + 4.74*C2h (r2 = 0.75) for three and two sample points, respectively. The results provide strong evidence for the use of either LSS or the use of time points other than C0h for therapeutic drug monitoring of MMF. Using the algorithms for the estimation of MPA-AUC0-12h based on LSS within the first 2 hours after MMF dosing may help to optimize treatment with MMF by individualization of dosing.