In Vitro-In Vivo Extrapolation and Hepatic Clearance-Dependent Underprediction

Accurately predicting the hepatic clearance of compounds using in vitro to in vivo extrapolation (IVIVE) is crucial within the pharmaceutical industry. However, several groups have recently highlighted the serious error in the process. Although empirical or regression-based scaling factors may be used to mitigate the common underprediction, they provide unsatisfying solutions because the reasoning behind the underlying error has yet to be determined. One previously noted trend was intrinsic clearance-dependent underprediction, highlighting the limitations of current in vitro systems. When applying these generated in vitro intrinsic clearance values during drug development and making first-in-human dose predictions for new chemical entities though, hepatic clearance is the parameter that must be estimated using a model of hepatic disposition, such as the well-stirred model. Here, we examine error across hepatic clearance ranges and find a similar hepatic clearance-dependent trend, with high clearance compounds not predicted to be so, demonstrating another gap in the field.     

ITERATIVE DYNAMIC PROGRAMMING FOR MINIMUM ENERGY CONTROL PROBLEMS WITH TIME DELAY

This paper presents the use of iterative dynamic programming employing exact penalty functions for minimum energy control problems. We show that exact continuously non-differentiable penalty functions are superior to continuously differentiable penalty functions in terms of satisfying final state constraints. We also demonstrate that the choice of an appropriate penalty function factor depends on the relative size of the time delay with respect to the final time and on the expected value of the energy consumption. A quadratic approximation (QA) of the delayed variables is much better than a linear approximation (LA) of the same for relatively large time delays. The QA improves the rate of convergence and avoids the formation of ‘kinks‘. A more general way of selecting appropriate penalty function factors is given and the results obtained using four illustrative examples of varying complexity corroborate the efficacy of the method.     

不同记忆负荷对事件相关电位—P300的影响

我们研究了不同记忆负荷条件下,ERP-P_(300)变化的规律性和特点及ERP-P_(300)与脑力负荷难度之间的关系。在三种脑力作业时,要求受试者记忆2、4、6位随机数字。结果表明,1、随着记忆数字增加,P_(300)波幅相应增大,三种记忆作业P_(300)波幅之间均有显著性差异。2、记忆错误率和记忆难度主观评价值,亦随着记忆数字增加而增大,且在三种记忆作业之间均有显著性差异。3、P_(300)波幅和记忆难度主观评价值之间相关分析表明呈正相关(P<0.01)。我们建议,P_(300)波幅测量可以作为评价脑力负荷的一项客观指标。     

Indoor air pollution and pulmonary performance: investigating errors in exposure assessment

We use pulmonary function measurements on pre-adolescent children and indoor air pollution measurements in the homes of these children to illustrate estimation techniques for linear regression models containing independent variables measured with error. In our data set, replicate measures of indoor air pollutant concentrations provide one method of estimating measurement error variances. Surrogate information in the form of cigarettes smoked is also available for the pollutant of interest. Several estimation procedures are presented, and we combine two estimators, one based on surrogate information and one based on replication information, using generalized least squares.     

Introduction of “international syringe labelling” in the Republic of Ireland

Introduction  Medication errors are a preventable cause of patient injury. In May 2003, as a result of a joint initiative by the Royal College of Anaesthetists, the Association of Anaesthetists of Great Britain and Ireland, the Intercollegiate Faculty of Accident and Emergency Medicine and the Intensive Care Society, a new colour code chart for syringe labelling was introduced. The introduction of the new system has not been uniform in the Irish Republic with no national guidelines or time scale in place. Methods  A questionnaire was administered to doctors working in Anaesthesia in two Dublin teaching hospitals. Results  As much as 23% had administered an incorrect medication and 53% admitted to a near miss as a result of the introduction of the new label. Discussion  Future action should focus on practical, common sense interventions including techniques such as those that reduce reliance on memory, standardization, the use of protocols and checklists, and the elimination of look-alike products.     

A recycling framework for the construction of Bonferroni‐based multiple tests

In this paper we describe Bonferroni‐based multiple testing procedures (MTPs) as strategies to split and recycle test mass. Here, ‘test mass’ refers to (parts of) the nominal level α at which the family‐wise error rate is controlled. Briefly, test mass is split between different null hypotheses, and whenever a null hypothesis is rejected, the part of α allocated to it may be recycled to the testing of other hypotheses. These recycling MTPs are closed testing procedures based on raw p‐values associated with testing the individual null hypotheses, and the class of such MTPs includes, for example, serial and parallel gatekeeping, fallback and Holm procedures. Graphical displays and a concise algebraic notation are provided for such MTPs. This recycling approach has pedagogical advantages and may facilitate the tailoring of MTPs for different purposes. Copyright © 2009 John Wiley & Sons, Ltd.     

Neural classifier construction using regularization, pruning and test error estimation

In this paper we propose a method for construction of feed-forward neural classifiers based on regularization and adaptive architectures. Using a penalized maximum likelihood scheme, we derive a modified form of the entropic error measure and an algebraic estimate of the test error. In conjunction with optimal brain damage pruning, a test error estimate is used to select the network architecture. The scheme is evaluated on four classification problems.