Isolation of lactoperoxidase using different cation exchange resins by batch and column procedures

Lactoperoxidase (LP) was isolated from whey protein by cation-exchange using Carboxymethyl resin (CM-25C) and Sulphopropyl Toyopearl resin (SP-650C). Both batch and column procedures were employed and the adsorption capacities and extraction efficiencies were compared. The resin bed volume to whey volume ratios were 0.96:1.0 for CM-25C and ≤ 0.64:1.0 for SP-650 indicating higher adsorption capacity of SP-650 compared to CM-25C. The effluent LP activity depended on both the enzyme activity in the whey and the amount of whey loaded on the column within the saturation limits of the resin. The percentage recovery was high below the saturation point and fell off rapidly with over-saturation. While effective recovery was achieved with column extraction procedures, the recovery was poor in batch procedures. The whey-resin contact time had little impact on the enzyme adsorption. SDS PAGE and HPLC analyses were also carried out, the purity was examined and the proteins characterised in terms of molecular weights. Reversed phase HPLC provided clear distinction of the LP and lactoferrin (LF) peaks. The enzyme purity was higher in column effluents compared to batch effluents, judged on the basis of the clarity of the gel bands and the resolved peaks in HPLC chromatograms.

Bayesian decision procedures for dose-escalation based on evidence of undesirable events and therapeutic benefit.

In this paper, Bayesian decision procedures are developed for dose-escalation studies based on bivariate observations of undesirable events and signs of therapeutic benefit. The methods generalize earlier approaches taking into account only the undesirable outcomes. Logistic regression models are used to model the two responses, which are both assumed to take a binary form. A prior distribution for the unknown model parameters is suggested and an optional safety constraint can be included. Gain functions to be maximized are formulated in terms of accurate estimation of the limits of a therapeutic window or optimal treatment of the next cohort of subjects, although the approach could be applied to achieve any of a wide variety of objectives. The designs introduced are illustrated through simulation and retrospective implementation to a completed dose-escalation study. Copyright © 2006 John Wiley & Sons, Ltd.

Bayesian decision procedures for binary and continuous bivariate dose-escalation studies

In this paper, Bayesian decision procedures are developed for dose-escalation studies based on binary measures of undesirable events and continuous measures of therapeutic benefit. The methods generalize earlier approaches where undesirable events and therapeutic benefit are both binary. A logistic regression model is used to model the binary responses, while a linear regression model is used to model the continuous responses. Prior distributions for the unknown model parameters are suggested. A gain function is discussed and an optional safety constraint is included. Copyright (C) 2006 John Wiley & Sons, Ltd.