Optimal design for model discrimination and parameter estimation for itraconazole population pharmacokinetics in cystic fibrosis patients

Optimal sampling times are found for a study in which one of the primary purposes is to develop a model of the pharmacokinetics of itraconazole in patients with cystic fibrosis for both capsule and solution doses. The optimal design is expected to produce reliable estimates of population parameters for two different structural PK models. Data collected at these sampling times are also expected to provide the researchers with sufficient information to reasonably discriminate between the two competing structural models.

Design strategies for controlling the molecular weight and rate using reversible addition-fragmentation chain transfer mediated living radical polymerization

Living radical polymerization has allowed complex polymer architectures to be synthesized in bulk, solution, and water. The most versatile of these techniques is reversible addition-fragmentation chain transfer (RAFT), which allows a wide range of functional and nonfunctional polymers to be made with predictable molecular weight distributions (MWDs), ranging from very narrow to quite broad. The great complexity of the RAFT mechanism and how the kinetic parameters affect the rate of polymerization and MWD are not obvious. Therefore, the aim of this article is to provide useful insights into the important kinetic parameters that control the rate of polymerization and the evolution of the MWD with conversion. We discuss how a change in the chain-transfer constant can affect the evolution of the MWD. It is shown how we can, in principle, use only one RAFT agent to obtain a poly-mer with any MWD. Retardation and inhibition are discussed in terms of (1) the leaving R group reactivity and (2) the intermediate radical termination model versus the slow fragmentation model. (c) 2005 Wiley Periodicals, Inc.

Bayesian population pharmacokinetic analysis of sirolimus

Objective: It is usual that data collected from routine clinical care is sparse and unable to support the more complex pharmacokinetic (PK) models that may have been reported in previous rich data studies. Informative priors may be a pre-requisite for model development. The aim of this study was to estimate the population PK parameters of sirolimus using a fully Bayesian approach with informative priors. Methods: Informative priors including prior mean and precision of the prior mean were elicited from previous published studies using a meta-analytic technique. Precision of between-subject variability was determined by simulations from a Wishart distribution using MATLAB (version 6.5). Concentration-time data of sirolimus retrospectively collected from kidney transplant patients were analysed using WinBUGS (version 1.3). The candidate models were either one- or two-compartment with first order absorption and first order elimination. Model discrimination was based on computation of the posterior odds supporting the model. Results: A total of 315 concentration-time points were obtained from 25 patients. Most data were clustered at trough concentrations with range of 1.6 to 77 hours post-dose. Using informative priors, either a one- or two-compartment model could be used to describe the data. When a one-compartment model was applied, information was gained from the data for the value of apparent clearance (CL/F = 18.5 L/h), and apparent volume of distribution (V/F = 1406 L) but no information was gained about the absorption rate constant (ka). When a two-compartment model was fitted to the data, the data were informative about CL/F, apparent inter-compartmental clearance, and apparent volume of distribution of the peripheral compartment (13.2 L/h, 20.8 L/h, and 579 L, respectively). The posterior distribution of the volume distribution of central compartment and ka were the same as priors. The posterior odds for the two-compartment model was 8.1, indicating the data supported the two-compartment model. Conclusion: The use of informative priors supported the choice of a more complex and informative model that would otherwise have not been supported by the sparse data.

Repelling class discrimination: ephrin-A5 binds to and activates EphB2 receptor signaling

The interactions between Eph receptor tyrosine kinases and their ephrin ligands regulate cell migration and axon pathfinding. The EphA receptors are generally thought to become activated by ephrin-A ligands, whereas the EphB receptors interact with ephrin-B ligands. Here we show that two of the most widely studied of these molecules, EphB2 and ephrin-A5, which have never been described to interact with each other, do in fact bind one another with high affinity. Exposure of EphB2-expressing cells to ephrin-A5 leads to receptor clustering, autophosphorylation and initiation of downstream signaling. Ephrin-A5 induces EphB2-mediated growth cone collapse and neurite retraction in a model system. We further show, using X-ray crystallography, that the ephrin-A5-EphB2 complex is a heterodimer and is architecturally distinct from the tetrameric EphB2-ephrin-B2 structure. The structural data reveal the molecular basis for EphB2-ephrin-A5 signaling and provide a framework for understanding the complexities of functional interactions and crosstalk between A- and B-subclass Eph receptors and ephrins.