EFFECTS OF DIFFERENT SAMPLING STRATEGIES ON PREDICTIONS OF BLOOD CYCLOSPORINE CONCENTRATIONS IN HEMATOLOGICAL PATIENTS WITH MULTIDRUG-RESISTANCE BY BAYESIAN AND NONLINEAR LEAST-SQUARES METHODS

The Bayesian method (BM) can use previous information for the optimiza tion of dosage regimen. However, Bayes' law remains true when the para meters are obtained from the infinite population. Therefore a bias mig ht exist in the previous information and affect BM predictive performa nce. To overcome this shortcoming, the blood drug concentration of a p atient can be used to individualize his pharmacokinetic parameters. Un til now, at least two sampling strategies, i.e. steady-state and non-s teady-state sampling strategies, have been developed to individualize and predict blood drug concentration. In the present study we used fiv e sampling strategies: (1) all samples; (2) post-infuson samples; (3) during-infusion samples; (4) samples within 95% confidence interval/in terquartile range of a steady-state concentration; (5) the sample of t he mean/median at the mid time-point of a steady-state to individualiz e and predict blood cyclosporine concentrations in haematological pati ents with multidrug resistance. We investigated the effects of differe nt sampling strategies on BM and the nonlinear least squared method (N LLSM) predictive performances. The results showed that BM predictive p erformance was better than NLLSM. But the results did not prove that t he steady-state sampling strategies were superior to the non-steady-st ate ones. (C) 1995 The Italian Pharmacological Society.