Easy-to-use, accurate and flexible individualized Bayesian limited sampling method without fixed time points for ciclosporin monitoring after liver transplantation.
BACKGROUND: New methods to estimate the systemic exposure to ciclosporin such as the level 2 h after dosing and limited sampling formulas may lead to improved clinical outcome after orthotopic liver transplantation. However, most strategies are characterized by rigid sampling times. AIM: To develop and validate a flexible individualized population-pharmacokinetic model for ciclosporin monitoring in orthotopic liver transplantation. METHODS: A total of 62 curves obtained from 31 patients at least 0.5 year after orthotopic liver transplantation were divided into two equal groups. From 31 curves, relatively simple limited sampling formulas were derived using multiple regression analysis, while using pharmacokinetic software a two-compartment population-pharmacokinetic model was derived from these same data. We then tested the ability to estimate the AUC by the limited sampling formulas and a different approach using several limited sampling strategies on the other 31 curves. The new approach consists of individualizing the mean a priori population-pharmacokinetic parameters of the two-compartment population-pharmacokinetic model by means of maximum a posteriori Bayesian fitting with individual data leading to an individualized population-pharmacokinetic limited sampling model. From the individualized pharmacokinetic parameters, AUC(0-12h) was calculated for each combination of measured blood concentrations. The calculated AUC(0-12h) both from the limited-sampling formulas and the limited-sampling model were compared with the gold standard AUC(0-12h) (trapezoidal rule) by Pearson's correlation coefficient and prediction precision and bias were calculated. RESULTS: The AUC(0-12h) value calculated by individualizing the population-pharmacokinetic model using several combinations of measured blood concentrations: 0 + 2 h (r(2) = 0.94), 0 + 1 + 2 h (r(2) = 0.94), 0 + 1 + 3 h (r(2) = 0.92), 0 + 2 + 3 h (r(2) = 0.92) and 0 + 1 + 2 + 3 h (r(2) = 0.96) had excellent correlation with AUC(0-12h), better than limited sampling formulas with less than three sampling time points. Even trough level with limited sampling method (r(2) = 0.86) correlated better than the level after 2 h of dosing (r(2) = 0.75) or trough level (r(2) = 0.64) as single values without limited sampling method. Moreover, the individualized population-pharmacokinetic model had a low prediction bias and excellent precision. CONCLUSION: Multiple rigid sampling time points limit the use of limited sampling formulas. The major advantage of the Bayesian estimation approach presented here, is that blood sampling time points are not fixed, as long as sampling time is known. The predictive performance of this new approach is superior to trough level and that after 2 h of dosing and at least as good as limited sampling formulas. It is of clear advantage in busy out-patient clinics.