Effects of different sampling strategies on predictions of blood cyclosporine concentrations in haematological patients with multidrug resistance by Bayesian and non-linear least squares methods

The Bayesian method (BM) can use previous information for the optimization of dosage regimen. However, Bayes' law remains true when the parameters are obtained from the infinite population. Therefore a bias might exist in the previous information and affect BM predictive performance. To overcome this shortcoming, the blood drug concentration of a patient can be used to individualize his pharmacokinetic parameters. Until now, at least two sampling strategies, i.e. steady-state and non-steady-state sampling strategies, have been developed to individualize and predict blood drug concentration. In the present study we used five sampling strategies: (1) all samples; (2) post-infusion samples; (3) during-infusion samples; (4) samples within 95% confidence interval/interquartile range of a steady-state concentration; (5) the sample of the mean/median at the mid-time-point of a steady-state to individualize and predict blood cyclosporine concentrations in haematological patients with multidrug resistance. We investigated the effects of different sampling strategies on BM and the nonlinear least squared method (NLLSM) predictive performances. The results showed that BM predictive performance was better than NLLSM. But the results did not prove that the steady-state sampling strategies were superior to the non-steady-state ones.