Nicotinic acid and DP1 blockade: studies in mouse models of atherosclerosis.

The use of nicotinic acid to treat dyslipidemia is limited by induction of a "flushing" response, mediated in part by the interaction of prostaglandin D(2) (PGD(2)) with its G-protein coupled receptor, DP1 (Ptgdr). The impact of DP1 blockade (genetic or pharmacologic) was assessed in experimental murine models of atherosclerosis. In Ptgdr(-/-)ApoE(-/-) mice versus ApoE(-/-) mice, both fed a high-fat diet, aortic cholesterol content was modestly higher (1.3- to 1.5-fold, P < 0.05) in Ptgdr(-/-)ApoE(-/-) mice at 16 and 24 weeks of age, but not at 32 weeks. In multiple ApoE(-/-) mouse studies, a DP1-specific antagonist, L-655, generally had a neutral to beneficial effect on aortic lipids in the presence or absence of nicotinic acid treatment. In a separate study, a modest increase in some atherosclerotic measures was observed with L-655 treatment in Ldlr(-/-) mice fed a high-fat diet for 8 weeks; however, this effect was not sustained for 16 or 24 weeks. In the same study, treatment with nicotinic acid alone generally decreased plasma and/or aortic lipids, and addition of L-655 did not negate those beneficial effects. These studies demonstrate that inhibition of DP1, with or without nicotinic acid treatment, does not lead to consistent or sustained effects on plaque burden in mouse atherosclerotic models.

Plasma lipid profiling across species for the identification of optimal animal models of human dyslipidemia.

In an attempt to understand the applicability of various animal models to dyslipidemia in humans and to identify improved preclinical models for target discovery and validation for dyslipidemia, we measured comprehensive plasma lipid profiles in 24 models. These included five mouse strains, six other nonprimate species, and four nonhuman primate (NHP) species, and both healthy animals and animals with metabolic disorders. Dyslipidemic humans were assessed by the same measures. Plasma lipoprotein profiles, eight major plasma lipid fractions, and FA compositions within these lipid fractions were compared both qualitatively and quantitatively across the species. Given the importance of statins in decreasing plasma low-density lipoprotein cholesterol for treatment of dyslipidemia in humans, the responses of these measures to simvastatin treatment were also assessed for each species and compared with dyslipidemic humans. NHPs, followed by dog, were the models that demonstrated closest overall match to dyslipidemic humans. For the subset of the dyslipidemic population with high plasma triglyceride levels, the data also pointed to hamster and db/db mouse as representative models for practical use in target validation. Most traditional models, including rabbit, Zucker diabetic fatty rat, and the majority of mouse models, did not demonstrate overall similarity to dyslipidemic humans in this study.

Novel transcriptome profiling analyses demonstrate that selective peroxisome proliferator-activated receptor γ (PPARγ) modulators display attenuated and selective gene regulatory activity in comparison with PPARγ full agonists.

Selective peroxisome proliferator-activated receptor γ (PPARγ) modulators (SPPARγMs) have been actively pursued as the next generation of insulin-sensitizing antidiabetic drugs, because the currently marketed PPARγ full agonists, pioglitazone and rosiglitazone, have been reported to produce serious adverse effects among patients with type 2 diabetes mellitus. We conducted extensive transcriptome profiling studies to characterize and to contrast the activities of 70 SPPARγMs and seven PPARγ full agonists. In both 3T3-L1 adipocytes and adipose tissue from db/db mice, the SPPARγMs generated attenuated and selective gene-regulatory responses, in comparison with full agonists. More importantly, SPPARγMs regulated the expression of antidiabetic efficacy-associated genes to a greater extent than that of adverse effect-associated genes, whereas PPARγ full agonists regulated both gene sets proportionally. Such SPPARγM selectivity demonstrates that PPARγ ligand regulation of gene expression can be fine-tuned, and not just turned on and off, to achieve precise control of complex cellular and physiological functions. It also provides a potential molecular basis for the superior therapeutic window previously observed with SPPARγMs versus full agonists. On the basis of our profiling results, we introduce two novel, gene expression-based scores, the γ activation index and the selectivity index, to aid in the detection and characterization of novel SPPARγMs. These studies provide new insights into the gene-regulatory activity of SPPARγMs as well as novel quantitative indices to facilitate the identification of PPARγ ligands with robust insulin-sensitizing activity and improved tolerance among patients with type 2 diabetes, compared with presently available PPARγ agonist drugs.

Gene expression profiling in patients with chronic obstructive pulmonary disease and lung cancer.

RATIONALE: Chronic obstructive lung disease (COPD) is a common and disabling lung disease for which there are few therapeutic options. OBJECTIVES: We reasoned that gene expression profiling of COPD lungs could reveal previously unidentified disease pathways. METHODS: Forty-eight human lung samples were obtained from tissue resected from five nonsmokers, 21 GOLD (Global Initiative for Chronic Obstructive Lung Disease) stage 0, 9 GOLD stage 1, 10 GOLD stage 2, and 3 GOLD stage 3 patients. mRNA from the specimens was profiled using Agilent's Functional ID v2.0 array (Agilent, Santa Clara, CA) containing 23,720 sequences. MEASUREMENTS AND MAIN RESULTS: The gene expression pattern was influenced by the percentage of the sample made up of parenchyma. Gene expression was related to forced expiratory flow between 25 and 75% of forced expiratory volume (FEF(25-75%) % predicted) revealing a signature gene set of 203 transcripts. Genes involved in extracellular matrix synthesis/degradation and apoptosis were among the up-regulated genes, whereas genes that participate in antiinflammatory responses were down-regulated. Immunohistochemistry confirmed expression of urokinase plasminogen activator (PLAU), urokinase plasminogen activator receptor (PLAUR), and thrombospondin (THBS1) by alveolar macrophages and airway epithelial cells. Genes in this pathway have been shown to be involved in the activation of transforming growth factor (TGF)-beta1 and matrix metalloproteinases and are subject to inhibition by SERPINE2. Interestingly, both TGF-beta1 and SERPINE2 have been identified as candidate genes in COPD genetic linkage and association studies. CONCLUSIONS: The results provide evidence that genes involved in tissue remodeling and repair are differentially regulated in the lungs of obstructed smokers and suggest that they are potential therapeutic targets. Data deposited in GEO at http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE8500.

Decreased complexity of glucose dynamics in diabetes in rhesus monkeys.

Until recently, preclinical and clinical work on diabetes has focused on the understanding of blood glucose elevation and its detrimental metabolic sequelae. The advent of continuous glucose monitoring (CGM) technology now allows real time monitoring of blood glucose levels as a time series, and thus the exploration of glucose dynamics at short time scales. Previous work has shown decreases in the complexity of glucose dynamics, as measured by multiscale entropy (MSE) analysis, in diabetes in humans, mice, and rats. Analyses for non-human primates (NHP) have not been reported, nor is it known if anti-diabetes compounds affect complexity of glucose dynamics. We instrumented four healthy and six diabetic rhesus monkeys with CGM probes in the carotid artery and collected glucose values at a frequency of one data point per second for the duration of the sensors' life span. Sensors lasted between 45 and 78 days. Five of the diabetic rhesus monkeys were also administered the anti-diabetic drug liraglutide daily beginning at day 39 of the CGM monitoring period. Glucose levels fluctuated during the day in both healthy and diabetic rhesus monkeys, peaking between 12 noon - 6 pm. MSE analysis showed reduced complexity of glucose dynamics in diabetic monkeys compared to healthy animals. Although liraglutide decreased glucose levels, it did not restore complexity in diabetic monkeys consistently. Complexity varied by time of day, more strongly for healthy animals than for diabetic animals. And by dividing the monitoring period into 3-day or 1-week subperiods, we were able to estimate within-animal variability of MSE curves. Our data reveal that decreased complexity of glucose dynamics is a conserved feature of diabetes from rodents to NHPs to man.

Differential effects of paroxetine on fatigue and depression: a randomized, double-blind trial from the University of Rochester Cancer Center Community Clinical Oncology Program.

PURPOSE: Fatigue and depression typically occur together in cancer patients, suggesting a common etiology, perhaps based on serotonin. This randomized clinical trial tested whether paroxetine, a selective serotonin reuptake inhibitor antidepressant known to modulate brain serotonin, would reduce fatigue in cancer patients and whether any reduction was related to depression. PATIENTS AND METHODS: Cancer patients undergoing chemotherapy for the first time were assessed for fatigue. Of 704 patients who reported fatigue at their second chemotherapy cycle, 549 patients were randomly assigned to receive either 20 mg of oral paroxetine hydrochloride daily or placebo for 8 weeks. The assessments of fatigue and depression were performed at cycles 3 and 4 of chemotherapy. RESULTS: A total of 244 patients treated with paroxetine and 235 patients treated with placebo provided assessable data. No difference was detected in fatigue between patient groups. At the end of the study, there was a difference between groups in the mean level of depression (Center for Epidemiologic Studies Depression scores, 12.0 v 14.8, respectively; P <.01). CONCLUSION: Paroxetine had no influence on fatigue in patients receiving chemotherapy. A possible explanation is that cancer-related fatigue does not involve a reduction in brain 5-HT levels.

Platelet transfusion reverses bleeding evoked by triple anti-platelet therapy including vorapaxar, a novel platelet thrombin receptor antagonist.

Vorapaxar is a novel protease-activated receptor-1 (PAR-1) antagonist recently approved for the reduction of thrombotic cardiovascular events in patients with a history of myocardial infarction or with peripheral arterial disease. Patients who received vorapaxar in addition to standard of care antiplatelet therapy had an increased incidence of major bleeding events compared with placebo. To assess whether platelet transfusion can restore hemostasis in primates on triple antiplatelet therapy, template bleeding times were assessed concurrently in the buccal mucosa, finger pad, and distolateral tail of anesthetized cynomolgus macaques to evaluate bleeding with vorapaxar as either monotherapy or in combination with aspirin or aspirin and clopidogrel. Aspirin (5mg/kg, IV) or vorapaxar (1mg/kg, PO) alone had no significant effect on bleeding times in the three vascular beds examined. A modest (<2-fold) increase in bleeding time was achieved in the three beds with the dual combination of aspirin and vorapaxar. Major increases in bleeding time were achieved in the three beds with the triple combination of aspirin (5mg/kg, IV), vorapaxar (1mg/kg, PO), and clopidogrel (1mg/kg, PO). Transfusion of fresh human platelet rich plasma, but not platelet poor plasma, reversed the increase in bleeding time in the triple therapy group. Transfusion of human platelets may be a viable approach in situations requiring a rapid reversal of platelet function in individuals treated with triple anti-platelet therapy that includes vorapaxar.

Comparison of lipoprotein separation and lipid analysis methodologies for human and cynomolgus monkey plasma samples.

To assess cardiovascular risk in both clinical and basic research settings, it is imperative to be able to accurately measure plasma lipid levels. Here, methods commonly used to measure lipoproteins and lipids: ultracentrifugation (UC), fast protein liquid chromatography (FPLC), Roche auto-analyzer, and enzymatic assays were tested and compared. Plasma samples from 20 healthy humans and 22 cynomolgus monkeys were analyzed for their total cholesterol (TC), cholesterol in low density lipoproteins (LDL) and high density lipoproteins (HDL), and triglycerides (TG). Major lipid classes from UC and FPLC separated lipoprotein fractions from human plasma were further characterized by liquid chromatography-mass spectrometry analysis. All the tested methods showed acceptable performance with Roche analyzer among the best in approximate dilution linearity and recovery for most lipids as well as in repeatability between measurements of the same samples. TC, LDL, HDL, and TG values measured in human vs. monkey were-183.9 ± 35.5 (mean ± SD) vs. 105.6 ± 24.6 mg/dl, 106.0 ± 30.1 vs. 42.8 ± 13.0 mg/dl, 50.0 ± 11.4 vs. 53.4 ± 14.8 mg/dl, and 107.6 ± 50.7 vs. 58.0 ± 52.3 mg/dl. While no single method was uniformly the best, we recommend the Roche analyzer for routine measurements. UC or FPLC separation is needed for further functional characterization for specific lipid fraction. We have shown athero-protective profile in cynomolgus monkey compared with humans.

Statistical methods to estimate treatment effects from multichannel electroencephalography (EEG) data in clinical trials.

With the increasing popularity of using electroencephalography (EEG) to reveal the treatment effect in drug development clinical trials, the vast volume and complex nature of EEG data compose an intriguing, but challenging, topic. In this paper the statistical analysis methods recommended by the EEG community, along with methods frequently used in the published literature, are first reviewed. A straightforward adjustment of the existing methods to handle multichannel EEG data is then introduced. In addition, based on the spatial smoothness property of EEG data, a new category of statistical methods is proposed. The new methods use a linear combination of low-degree spherical harmonic (SPHARM) basis functions to represent a spatially smoothed version of the EEG data on the scalp, which is close to a sphere in shape. In total, seven statistical methods, including both the existing and the newly proposed methods, are applied to two clinical datasets to compare their power to detect a drug effect. Contrary to the EEG community's recommendation, our results suggest that (1) the nonparametric method does not outperform its parametric counterpart; and (2) including baseline data in the analysis does not always improve the statistical power. In addition, our results recommend that (3) simple paired statistical tests should be avoided due to their poor power; and (4) the proposed spatially smoothed methods perform better than their unsmoothed versions.