Finding unrecognized information in overactive bladder clinical trial data: a new approach to understanding placebo and treatment effects.

AIMS: To identify combinations of variables among overactive bladder (OAB) clinical trial subjects that allow prediction of those who are more--or less--likely to respond strongly to placebo, or to medication. METHODS: Data from two Phase IIIb clinical trials of solifenacin in OAB were combined. Predictive models for placebo and treatment responses were constructed using baseline variables including individual items from the OAB questionnaire. These models were reduced to an essential subset of predictor variables. Two outcome measures are reported: urgency and incontinence. RESULTS: In placebo subjects, 14 selected variables permitted distinction between those who responded with significant reductions in urgency and those who did not. A subset of nine variables in treated subjects permitted distinction between those more--or less--likely to respond to medication. Data for urgency were combined from both placebo and actively treated subjects to identify those who had one of the previously identified clusters of variables. It was possible to predict, among all subjects, who would be likely to experience a strong placebo or active treatment response and who would not. This process was also applied to incontinence data. CONCLUSIONS: We have developed a new process to help understand placebo and treatment responses and their relationships to baseline conditions. The effectiveness of these methods was indicated using data from two solifenacin clinical trials and would benefit from future validation using other data sets. Methods used here are suitable for predicting the placebo effect in other clinical trials.

Cluster analysis for the impact of sickle cell disease on the human erythrocyte protein interactome.

Protein interaction networks provide useful information to assess impacts of disease on cell functions. Statistical clustering methods applied to these networks can reveal impacts on particular functional groups of proteins. In addition, clustering methods can identify subsets of proteins that require additional study to provide updated information regarding their position within an interaction network, and hence increase our understanding of their relationships with other proteins in the network. These ideas are illustrated here by considering the impacts of sickle cell disease on the human erythrocyte interaction network. Statistical cluster analyses are performed based on a measure of similarity for nodes within a network called the Generalized Topological Overlap Measure. These analyses identify clusters of proteins that are similar in terms of shared interaction partners. Identification of clusters that contain proteins whose relative abundances have been significantly altered in sickle cell patients provides specific information about the impact of these proteins on erythrocyte functions.

Monocyte protein signatures of disease severity in sickle cell anemia.

Using two-dimensional difference gel electrophoresis (2D DIGE) we have analyzed monocytes derived from 10 sickle cell disease patients (5 males and 5 females ages 12-18) to generate hypotheses regarding signature proteins that appear most positively and negatively correlated with vasoocclusive event rate. Signature proteins have been identified by tandem mass spectrometry. Based on the limited number of samples analyzed, the most negatively correlated proteins related to crises rate were transketolase and coronin in the membrane fraction and heat shock 70 kDa protein cognate 4, and adenylate kinase isoenzyme 2, mitochondrial found in the cytosolic fraction. The protein spots that were most positively correlated with crisis rate in the cytoplasmic fraction were far upstream element-binding protein and Alpha actinin 1 or Alpha actinin 4. Utilizing StepSIM analysis, vinculin was able to classify all samples from the combined set and the membrane-only set, and cytosolic leukotriene A-4 hydrolase and phosphoglycerate kinase were also identified as important indicators for differentiating between low and high vasoocclusive event rates.

The human red blood cell proteome and interactome.

The red blood cell or erythrocyte is easily purified, readily available, and has a relatively simple structure. Therefore, it has become a very well studied cell in terms of protein composition and function. RBC proteomic studies performed over the last five years, by several laboratories, have identified 751 proteins within the human erythrocyte. As RBCs contain few internal structures, the proteome will contain far fewer proteins than nucleated cells. In this minireview, we summarize the current knowledge of the RBC proteome, discuss alterations in this partial proteome in varied human disease states, and demonstrate how in silico studies of the RBC interactome can lead to considerable insight into disease diagnosis, severity, and drug or gene therapy response. To make these latter points we focus on what is known concerning changes in the RBC proteome in Sickle Cell Disease.

Solid-phase microextraction and the human fecal VOC metabolome.

The diagnostic potential and health implications of volatile organic compounds (VOCs) present in human feces has begun to receive considerable attention. Headspace solid-phase microextraction (SPME) has greatly facilitated the isolation and analysis of VOCs from human feces. Pioneering human fecal VOC metabolomic investigations have utilized a single SPME fiber type for analyte extraction and analysis. However, we hypothesized that the multifarious nature of metabolites present in human feces dictates the use of several diverse SPME fiber coatings for more comprehensive metabolomic coverage. We report here an evaluation of eight different commercially available SPME fibers, in combination with both GC-MS and GC-FID, and identify the 50/30 µm CAR-DVB-PDMS, 85 µm CAR-PDMS, 65 µm DVB-PDMS, 7 µm PDMS, and 60 µm PEG SPME fibers as a minimal set of fibers appropriate for human fecal VOC metabolomics, collectively isolating approximately 90% of the total metabolites obtained when using all eight fibers. We also evaluate the effect of extraction duration on metabolite isolation and illustrate that ex vivo enteric microbial fermentation has no effect on metabolite composition during prolonged extractions if the SPME is performed as described herein.