AIM: To explore relationships between sirolimus dosing, concentration and clinical outcomes. METHODS: Data were collected from 25 kidney transplant recipients (14 M/11 F), median 278 days after transplantation. Outcomes of interest were white blood cell (WBC) count, platelet (PLT) count, and haematocrit (HCT). A naive pooled data analysis was performed with outcomes dichotomized (Mann-Whitney U-tests). RESULTS: Several patients experienced at least one episode when WBC (n = 9), PLT (n = 12), or HCT (n = 21) fell below the lower limits of the normal range. WBC and HCT were significantly lower (P < 0.05) when sirolimus dose was greater than 10 mg day(-1), and sirolimus concentration greater than 12 microg l(-1). No relationship was shown for PLT and dichotomized sirolimus dose or concentration. CONCLUSIONS: Given this relationship between sirolimus concentration and effect, linked population pharmacokinetic-pharmacodynamic modelling using data from more renal transplant recipients should now be used to quantify the time course of these relationships to optimize dosing and minimize risk of these adverse outcomes.
AIM: To explore relationships between sirolimus dosing, concentration and clinical outcomes. METHODS: Data were collected from 25 kidney transplant recipients (14 M/11 F), median 278 days after transplantation. Outcomes of interest were white blood cell (WBC) count, platelet (PLT) count, and haematocrit (HCT). A naive pooled data analysis was performed with outcomes dichotomized (Mann-Whitney U-tests). RESULTS: Several patients experienced at least one episode when WBC (n = 9), PLT (n = 12), or HCT (n = 21) fell below the lower limits of the normal range. WBC and HCT were significantly lower (P < 0.05) when sirolimus dose was greater than 10 mg day(-1), and sirolimus concentration greater than 12 microg l(-1). No relationship was shown for PLT and dichotomized sirolimus dose or concentration. CONCLUSIONS: Given this relationship between sirolimus concentration and effect, linked population pharmacokinetic-pharmacodynamic modelling using data from more renal transplant recipients should now be used to quantify the time course of these relationships to optimize dosing and minimize risk of these adverse outcomes.
Authors: W C Zamboni; D Z D'Argenio; C F Stewart; T MacVittie; B J Delauter; A M Farese; D M Potter; N M Kubat; D Tubergen; M J Egorin Journal: Clin Cancer Res Date: 2001-08 Impact factor: 12.531
Authors: Joshua J Augustine; Thomas C Knauss; James A Schulak; Kenneth A Bodziak; Christopher Siegel; Donald E Hricik Journal: Am J Transplant Date: 2004-12 Impact factor: 8.086
Authors: Johannes P van Hooff; Jean-Paul Squifflet; Zbigniew Wlodarczyk; Yves Vanrenterghem; Leszek Paczek Journal: Transplantation Date: 2003-06-27 Impact factor: 4.939