Biomedical subjects

Luc Bijnens

Publications and source records attributed to Luc Bijnens.

11 recordsLinked to original sources

PubMed2006

Teaching statistical thinking to life scientists a case-based approach.

We describe a workshop on statistical thinking for scientists involved in pharmaceutical discovery research. The objectives were 1) to improve the quality of research data by developing a structured approach to bias and variability and 2) to establish a collaborative and informed relationship between scientists and statisticians by broadening their common basis. The corner stone was the introduction of statistical thinking and the didactical route to achieve this goal.

Bias↗

PubMed2006

A modeling approach to the analysis of nerve regenerative experiments.

Many experiments aiming at the investigation of nerve repair involve elaborate testing over a certain time period. Data arising from such experiments are often analyzed by time-point. Such a cross-sectional approach is often very inefficient. In this paper, we consider a case study in which repeated measurements of two response variables assumed to be Poisson distributed are obtained. We show how a repeated measures modeling approach, based on generalized linear models, can handle both responses in one model and improve the inference in the nerve repair experiments. The benefits of the model as well as problems that can occur are illustrated and discussed.

Algorithms↗

PubMed2006

Real-time gene expression analysis in human xenografts for evaluation of histone deacetylase inhibitors.

Real-time analysis of gene expression in experimental tumor models represents a major tool to document disease biology and evaluate disease treatment. However, monitoring gene regulation in vivo still is an emerging field, and thus far it has not been linked to long-term tumor growth and disease outcome. In this report, we describe the development and validation of a fluorescence-based gene expression model driven by the promoter of the cyclin-dependent kinase inhibitor p21waf1,cip1. The latter is a key regulator of tumor cell proliferation and a major determinant in the response to many anticancer agents such as histone deacetylase inhibitors. In response to histone deacetylase inhibitors, induction of fluorescence in A2780 ovarian tumors could be monitored in living mice in a noninvasive real-time manner using whole-body imaging. Single p.o. administration of the histone deacetylase inhibitor MS-275 significantly induces tumor fluorescence in a time- and dose-dependent manner, which accurately predicted long-term antitumoral efficacy in individual mice following extended treatment. These findings illustrate that this technology allows monitoring of the biological response induced by treatment with histone deacetylase inhibitors. In addition to providing experimental pharmacokinetic/pharmacodynamic markers for investigational drugs, this model provides insight into the kinetics of in vivo regulation of transcription, which plays a key role in causing and maintaining the uncontrolled proliferation of tumor tissue.

Animals↗

PubMed2005

Design and analysis of drug combination experiments.

In this paper we present and discuss a novel, simple and easy to implement parametric modeling approach to assess synergy. An extended three parameter log-logistic model is used to analyse the data and calculate confidence intervals of the interaction indices. In addition the model corrects for the bias due to plate-location effects. The analysis is performed with PROC NLMIXED and SAS-code is provided. The approach is illustrated using data coming from an oncology study in which the inhibition effect of a combination of two compounds is studied using 96-well plates and a fixed-ratio design.

Algorithms↗

PubMed2005

Behavioral testing of antidepressant compounds: an analysis of crossover design for correlated binary data.

The differential reinforcement of low-rate 72 seconds schedule (DRL-72) is a standard behavioral test procedure for screening potential antidepressant compounds. The protocol for the DRL-72 experiment, proposed by Evenden et al. (1993), consists of using a crossover design for the experiment and one-way ANOVA for the statistical analysis. In this paper we discuss the choice of several crossover designs for the DRL-72 experiment and propose to estimate the treatment effects using either generalized linear mixed models (GLMM) or generalized estimating equation (GEE) models for clustered binary data.

Algorithms↗

PubMed2005

Modeling anti-KLH ELISA data using two-stage and mixed effects models in support of immunotoxicological studies.

During preclinical drug development, the immune system is specifically evaluated after prolonged treatment with drug candidates, because the immune system may be an important target system. The response of antibodies against a T-cell-dependent antigen is recommenced by the FDA and EMEA for the evaluation of immunosuppression/enhancement. For that reason, we developed a semiquantitative enzyme-linked immunosorbent assay to measure antibodies against keyhole limpet hemocyanin. To our knowledge, the analysis of this kind of data is at this moment not yet fully explored. In this article, we describe two approaches for modeling immunotoxic data using nonlinear models. The first is a two-stage model in which we fit an individual nonlinear model for each animal in the first stage, and the second stage consists of testing possible treatment effects using the individual maximum likelihood estimates obtained in the first stage. In the second approach, the inference about treatment effects is based on a nonlinear mixed model, which accounts for heterogeneity between animals. In both approaches, we use a three-parameter logistic model for the mean structure.

Analysis of Variance↗

PubMed2005

A hierarchical Binomial-Poisson model for the analysis of a crossover design for correlated binary data when the number of trials is dose-dependent.

The differential reinforcement of a low-rate 72-seconds schedule (DRL-72) is a standard behavioral test procedure for screening a potential antidepressant compound. The data analyzed in the article are binary outcomes from a crossover design for such an experiment. Recently, Shkedy et al. (2004) proposed to estimate the treatments effect using either generalized linear mixed models (GLMM) or generalized estimating equations (GEE) for clustered binary data. The models proposed by Shkedy et al. (2004) assumed the number of responses at each binomial observation is fixed. This might be an unrealistic assumption for a behavioral experiment such as the DRL-72 because the number of responses (the number of trials in each binomial observation) is expected to be influenced by the administered dose level. In this article, we extend the model proposed by Shkedy et al. (2004) and propose a hierarchical Bayesian binomial-Poisson model, which assumes the number of responses to be a Poisson random variable. The results obtained from the GLMM and the binomial-Poisson models are comparable. However, the latter model allows estimating the correlation between the number of successes and number of trials.

Algorithms↗

PubMed2004

Transcriptional response to corticotropin-releasing factor in AtT-20 cells.

Corticotropin-releasing factor (CRF) plays a central role in the regulation of the hypothalamic-pituitary-adrenal axis, mediating endocrine and behavioral responses to various stressors. Two high-affinity receptors for CRF have been described. Although many of the intracellular signaling pathways activated by CRF have been studied extensively, our knowledge of transcriptional responses downstream of the CRF receptor 1 (CRFR1) is still limited. To elucidate gene networks regulated by CRF and CRFR1, we applied microarray technology to explore transcriptional response to CRF stimulation. Therefore, mouse pituitary-derived AtT-20 cells were exposed continuously to CRF either in the presence or absence of the specific CRFR1 antagonist R121919. Transcriptional responses to different treatments were studied in a time course ranging from 0.5 to 24 h. Microarray data were analyzed using classic microarray data analysis tools such as correspondence factor analysis, cluster analysis, and fold-change filtering. Furthermore, spectral map analysis was applied, a recently introduced unsupervised multivariate analysis method. A broad and transient transcriptional response to CRF was identified that could be blocked by the antagonist. This way, several known CRF-induced target genes and novel CRF responsive genes were identified. These include transcription factors such as cAMP-responsive element modulator (7x increased), secreted peptides such as cholecystokinin (1.5x), and proteins involved in modulating intracellular signaling, such as regulator of G-protein signaling 2 (11x). Up-regulation of many of these genes can be explained as negative feedback, attenuating CRF-activated pathways. In addition, spectral map analysis proved to be a promising new tool for microarray data analysis.

Animals↗

PubMed2003

Controlling type I error rate for fast track drug development programmes.

The U.S. Food and Drug Administration (FDA) Modernization Act of 1997 has a Section (No. 112) entitled 'Expediting Study and Approval of Fast Track Drugs' (the Act). In 1998, the FDA issued a 'Guidance for Industry: the Fast Track Drug Development Programs' (the FTDD programmes) to meet the requirement of the Act. The purpose of FTDD programmes is to 'facilitate the development and expedite the review of new drugs that are intended to treat serious or life-threatening conditions and that demonstrate the potential to address unmet medical needs'. Since then many health products have reached patients who suffered from AIDS, cancer, osteoporosis, and many other diseases, sooner by utilizing the Fast Track Act and the FTDD programmes. In the meantime several scientific issues have also surfaced when following the FTDD programmes. In this paper we will discuss the concept of two kinds of type I errors, namely, the 'conditional approval' and the 'final approval' type I errors, and propose statistical methods for controlling them in a new drug submission process.

Clinical Trials, Phase III as Topic↗

PubMed2003

Graphical exploration of gene expression data: a comparative study of three multivariate methods.

This article describes three multivariate projection methods and compares them for their ability to identify clusters of biological samples and genes using real-life data on gene expression levels of leukemia patients. It is shown that principal component analysis (PCA) has the disadvantage that the resulting principal factors are not very informative, while correspondence factor analysis (CFA) has difficulties interpreting distances between objects. Spectral map analysis (SMA) is introduced as an alternative approach to the analysis of microarray data. Weighted SMA outperforms PCA, and is at least as powerful as CFA, in finding clusters in the samples, as well as identifying genes related to these clusters. SMA addresses the problem of data analysis in microarray experiments in a more appropriate manner than CFA, and allows more flexible weighting to the genes and samples. Proper weighting is important, since it enables less reliable data to be down-weighted and more reliable information to be emphasized.

Biometry↗

PubMed2002

Selection models and pattern-mixture models to analyse longitudinal quality of life data subject to drop-out.

Longitudinally observed quality of life data with large amounts of drop-out are analysed. First we used the selection modelling framework, frequently used with incomplete studies. An alternative method consists of using pattern-mixture models. These are also straightforward to implement, but result in a different set of parameters for the measurement and drop-out mechanisms. Since selection models and pattern-mixture models are based upon different factorizations of the joint distribution of measurement and drop-out mechanisms, comparing both models concerning, for example, treatment effect, is a useful form of a sensitivity analysis.

Aged↗