Evaluation of AUC(0-4) predictive methods for cyclosporine in kidney transplant patients.
ABSTRACT Cyclosporine (CyA) is the most commonly used immunosuppressive agent in patients who undergo kidney transplantation. Dosage adjustment of CyA is usually based on trough levels. Recently, trough levels have been replacing the area under the concentration-time curve during the first 4 h after CyA administration (AUC(0-4)). The aim of this study was to compare the predictive values obtained using three different methods of AUC(0-4) monitoring. AUC(0-4) was calculated from 0 to 4 h in early and stable renal transplant patients using the trapezoidal rule. The predicted AUC(0-4) was calculated using three different methods: the multiple regression equation reported by Uchida et al.; Bayesian estimation for modified population pharmacokinetic parameters reported by Yoshida et al.; and modified population pharmacokinetic parameters reported by Cremers et al. The predicted AUC(0-4) was assessed on the basis of predictive bias, precision, and correlation coefficient. The predicted AUC(0-4) values obtained using three methods through measurement of three blood samples showed small differences in predictive bias, precision, and correlation coefficient. In the prediction of AUC(0-4) measurement of one blood sample from stable renal transplant patients, the performance of the regression equation reported by Uchida depended on sampling time. On the other hand, the performance of Bayesian estimation with modified pharmacokinetic parameters reported by Yoshida through measurement of one blood sample, which is not dependent on sampling time, showed a small difference in the correlation coefficient. The prediction of AUC(0-4) using a regression equation required accurate sampling time. In this study, the prediction of AUC(0-4) using Bayesian estimation did not require accurate sampling time in the AUC(0-4) monitoring of CyA. Thus Bayesian estimation is assumed to be clinically useful in the dosage adjustment of CyA.
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ABSTRACT: Cyclosporine (CsA) dosing is traditionally based on trough blood levels (C0) rather than area under the concentration-time curve (AUC), although AUC correlates better with posttransplantation clinical events. For Neoral, AUC based on limited sampling correlates closely with full 12-hr AUC. The purpose of our study was to correlate C0 with AUC based on CsA levels at 0, 1, 2, 3, and 4 hr after dose (PK0-4) and to compare this AUC with C0 in predicting acute rejection (AR) and acute cyclosporine nephrotoxicity (CsANT) in de novo first kidney transplant patients. PK0-4 was done 2-4 days after starting Neoral for 156 patients. All received CsA-based triple-drug immunosuppression without antibody induction. AUC was calculated as projected 12-hr (AUC0-12) and actual 4-hr (AUC0-4) from the PK0-4 using the parallel trapezoid rule. Neoral dosing was based on C0 not AUC. AUC was retrospectively compared with C0 as a predictor of AR and CsANT during the first 90 days. C0 correlated poorly with AUC0-12 and AUC0-4 (r=0.61 and r=0.42). C0 (mean+/-SEM) levels were not significantly different in 34 patients with and 109 without AR (293+/-21 vs. 294+/-11 microg/L, P=0.95). AUC0-12 and AUC0-4 were significantly lower in patients with than without AR (AUC0-12 9090+/-598 vs. 10608+/-336 microg x h/L, P=0.01; AUC0-4 3934+/-306 vs. 4802+/-166 microg.h/L, P=0.006). In stepwise regression analysis only AUC0-12 or AUC0-4 (P=0.03/P=0.02) and delayed graft function (P=0.007) predicted AR. AUC0-12, AUC0-4, and C0 were all significantly higher in patients with CsANT than without CsANT (AUC0-12 11746+/-650 vs. 10023+/-301 microg x h/L, P=0.01; AUC0-4 5270+/-358 vs. 4474+/-150 microg x h/L, P=0.01; C0 343+/-18 vs. 287+/-10 microg/L, P=0.01), but in stepwise regression analysis C0 was not an independent predictor of CsANT. Patients with AUC0-12 in the range of 9500 to 11500 microg x h/L or AUC0-4 between 4400 and 5500 microg x h/L had the lowest incidence of AR (13% and 7%, respectively) without significantly higher risk for CsANT. C0 correlates poorly with AUC based on PK0-4. Early AUC based on PK0-4 is more closely associated with AR and CsANT than is C0. Our data suggest that a target AUC0-12 of 9500-11500 or AUC0-4 of 4400-5500 microg x h/L may provide optimal Neoral immunosuppression.Transplantation 08/1999; 68(1):55-62. DOI:10.1097/00007890-199907150-00011 · 3.83 Impact Factor
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ABSTRACT: The aim of this study was to develop routinely applicable limited sampling strategies for assessing cyclosporin (CsA) AUC(0-12 h), and possibly other exposure indices such as AUC(0-4 h) and C(max), in heart transplant patients over the first year post-transplantation. First, the individual pharmacokinetics (PKs) of 14 adult heart-transplant patients receiving Neoral were assessed at three post-transplantation periods, at the end of the first week (W1), the third month (M3) and the first year (Y1). To fit blood concentrations, a PK model specially developed for oral CsA was applied. Second, two statistical methods were compared for AUC(0-12 h) estimation using a limited sampling strategy (maximum of three blood samples): multiple regression analysis (MR) and Bayesian estimation (BE). No significant difference was observed between the individual PK parameters at M3 and Y1, so population modelling was performed taking as a whole the concentration data collected at M3 and Y1. On the contrary, a significant difference ( P<0.05) was found for the C2/dose ratio between W1 and M3 and between W1 and Y1 (mean+/-SD =5.47+/-2.33; 7.78+/-1.05; 6.98+/-2.17 ml(-1 )for W1, M3 and Y1, respectively). Also, C(max)/dose and A were found significantly lower at W1 than at M3 ( P<0.01 and P<0.005, respectively), while lambda(1) was significantly higher at W1 than at both M3 and Y1 ( P<0.01). Using three sampling times (t0 h, t1 h and t3 h), BE allowed an accurate prediction of AUC(0-12 h) (mean bias =3.06+/-12.16%; +1.50+/-1.61%; and -0.20+/-11.42% at W1, M3 and Y1, respectively), AUC(0-4 h )and C(max). MR led to satisfactory estimation of AUC(0-12 h) using only two blood samples collected 2 h and 6 h post-dose (R=0.956-0.993; bias =-5.22 to +4.41; precision =6.38 to 9.90%), but this method is unable to estimate any other exposure index and requires strict respect of sampling times, contrary to BE. Neoral monitoring based on full or abbreviated AUC is possible using BE or MR in heart transplant patients over the first year post-transplantation. BE provides a good description of the individual PK profiles and thus might be useful not only in case of potential discrepancies between C2 and clinical findings, but also for clinical trials aimed at finding optimum PK monitoring in heart recipients.European Journal of Clinical Pharmacology 04/2003; 58(12):813-20. DOI:10.1007/s00228-003-0559-5 · 2.97 Impact Factor
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ABSTRACT: Dosage adjustments of cyclosporine are confounded with an unexpected degree of variability, thus invalidating a direct proportionality between the oral dose rate and the steady-state concentration. In 1033 observations of dose rate and average steady-state concentration collected during therapeutic monitoring (area under the curve method) in 134 adult kidney transplant patients, a population pharmacokinetic analysis showed that a Michaelis-Menten model fitted the data better than a linear clearance model. It was further shown that the Michaelis-Menten constant (Km) parameter of the Michaelis-Menten model (the average steady-state concentration at half-maximal dose rate) increased during the first 4 months after transplantation whereas the maximal dose rate of the Michaelis-Menten model (Vmax) remained constant. The following parameters with interindividual variation in parenthesis were estimated: Vmax = 852 mg/24 hr (43%) and Km at 114 days after transplantation = 349 ng/ml (117%). An algorithm was derived from this population model that guides the clinician during the adjustment of oral cyclosporine dose rates.Clinical Pharmacology & Therapeutics 07/1993; 53(6):651-60. DOI:10.1038/clpt.1993.86 · 7.90 Impact Factor