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Buprenorphine is a semisynthetic opiate synthesized from the precursor thebaine. In contrast to earlier reports in the literature, the findings of this study show that the rate-limiting step in the onset and offset of buprenorphine's antinociceptive effect is distribution to the brain. Fentanyl binds instantaneously to the OP3 receptor because no reasonable values for k on and k off were obtained with the dynamical receptor model. On the other hand, part of the hysteresis in the buprenorphine pharmacodynamics could be explained by slow receptor association/dissociation kinetics. The k eo was 0.024 min Ϫ1 and 0.123 min Ϫ1 (95% CI: 0.095-0.151 min Ϫ1 ) for buprenorphine and fentanyl, respectively. The results show that onset and offset of the antinociceptive effect of both buprenorphine and fentanyl is mainly determined by biophase distribution. The model converged, yielding precise estimates of the parameters characterizing hysteresis. A log logistic probability distribution model is proposed to account for censored tail-flick latencies. To explain time dependencies in pharmacodynamics of buprenorphine and fentanyl, a combined effect compartment/receptor binding model was applied. For buprenorphine, a three-compartment pharmacokinetic model best described the concentration time course. For fentanyl, the pharmacokinetics was described on the basis of a two-compartment pharmacokinetic model. Data on the time course of the antinociceptive effect following intravenous administration of buprenorphine or fentanyl was analyzed in conjunction with plasma concentrations by nonlinear mixedeffects analysis. Index Terms-collaborative filtering multi-task learning mixed effects model kernel methods regularization Gaussian processes Kalman filtering pharmacokinetic data I.ĪBSTRACT The objective of this investigation was to characterize the pharmacokinetic/pharmacodynamic correlation of buprenorphine and fentanyl for the antinociceptive effect in rats. The algorithm is tested on two simulated problems and a real dataset relative to xenobiotics administration in human patients. Within a Bayesian setting, a recursive on-line algorithm is obtained, that updates both estimates and confidence intervals as new data become available. More precisely, a quadratic loss is assumed and each task consists of the sum of a common term and a task-specific one. The aim of this paper is to derive an efficient computational scheme for an important class of multi-task kernels. For instance, when regularization networks are used, complexity scales as the cube of the overall number of training data, which may be large when several tasks are involved. However, a possible drawback is computational complexity. There are experimental results, especially in biomedicine, showing the benefit of the multi-task approach compared to the single-task one. The method is evaluated on simulations from some famous diffusion processes and on real datasets.Ībstract-Standard single-task kernel methods have been re-cently extended to the case of multi-task learning in the context of regularization theory.
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Here we propose a computationally fast approximated maximum likelihood procedure for the estimation of the non-random parameters and the random effects. In most cases the likelihood function is not available, and thus maximum likelihood esti-mation of the unknown parameters is not possible. This class of models enables the simul-taneous representation of randomness in the dynamics of the phenomena being considered and variability between experimental units, thus providing a powerful modeling tool with immediate applications in biomedicine and pharmacokinetic/pharmacodynamic studies. When both system noise and random effects are considered, stochastic differential mixed-effects models ensue.
Nonmem greater than series#
Biomedical experiments often imply repeated measurements on a series of experimental units and differences between units can be represented by incorporating ran-dom effects into the model. Stochastic differential equations have shown useful to describe random continuous time processes.