15 research outputs found
34 PROOF OF CONCEPT: FUNCTIONAL MODELS FOR DRUG DEVELOPMENT IN HUMANS
A drug developed for human use classically goes through a number of steps, including discovery, extensive preclinical studies for safety in experimental animals, and then human safety and efficacy studies. The Food and Drug Administration (FDA) requires successful completion of all the above tasks before approval for therapeutic use in human beings. The exciting advances in human genomics and combinatorial chemistry promise substantial applications to new drugs for human diseases; however, the time and expense associated with the processes necessary to bring a new drug to market are rising exponentially. Thus, there is a great need for functional models of disease progression, including animal models of human disease and biomarkers in human clinical trials. BIOMARKERS AND SURROGATE MARKERS The monitoring of biologic disease processes increasingly employs biomarkers (Table 34.1). At a recent National Institutes of Health (NIH) and FDA conference (1) biomarkers for clinical efficacy were divided into several groups including natural history markers, biological activity markers, and surrogate markers. A relevant issue in clinical trials is the selection of an appropriate endpoint. Endpoints that are less deleterious than death or onset of a disease are desirable. Surrogate markers or endpoints are events of a more intermediate nature. These typically replace the final endpoints such as mortality. A surrogate marker is defined statistically as a ‘‘response variable for which a test of the null hypothesis of no relationship to the treatment groups under comparison is also a valid test of the corresponding null hypothesis base
Spectral analysis with a minimal basis functions approach for quantification of ligand-receptor dynamic PET study
Advances in the quantification of [11C]raclopride dynamic PET with amphetamine challenges
Dopamine release and serotonin interactions in Tourette’s syndrome:new relations to obsessive compulsive disorder
Mu Opioid Receptor Binding Correlates with Nicotine Dependence and Reward in Smokers.
The rewarding effects of nicotine are associated with activation of nicotine receptors. However, there is increasing evidence that the endogenous opioid system is involved in nicotine's rewarding effects. We employed PET imaging with [11C]carfentanil to test the hypotheses that acute cigarette smoking increases release of endogenous opioids in the human brain and that smokers have an upregulation of mu opioid receptors (MORs) when compared to nonsmokers. We found no significant changes in binding potential (BPND) of [11C]carfentanil between the placebo and the active cigarette sessions, nor did we observe differences in MOR binding between smokers and nonsmokers. Interestingly, we showed that in smokers MOR availability in bilateral superior temporal cortices during the placebo condition was negatively correlated with scores on the Fagerström Test for Nicotine Dependence (FTND). Also in smokers, smoking-induced decreases in [11C]carfentanil binding in frontal cortical regions were associated with self-reports of cigarette liking and wanting. Although we did not show differences between smokers and nonsmokers, the negative correlation with FTND corroborates the role of MORs in superior temporal cortices in nicotine addiction and provides preliminary evidence of a role of endogenous opioid signaling in frontal cortex in nicotine reward
Characterization of dose dependent norepinephrine transporter blockade by atomoxetine in human brain using 11
GM1 ganglioside in Parkinson's disease: Pilot study of effects on dopamine transporter binding
Concentrations of nicotine and metabolites in plasma during active and placebo cigarette scans.
<p>Mean ± standard deviation (ng/mL) of individual subjects' means across 2–75 min, except for the second nicotine row representing nicotine concentrations only from 2–10 min.</p><p>Limits of quantification were 1 ng/mL for cotinine, OH-cotinine and norcotinine, and 2.5 ng/mL for nicotine at individual time point.</p><p>* Active cigarette scan values > placebo cigarette scan value at p<0.01; paired t-test.</p>#<p>Smoker > nonsmoker at p<0.00001; t-test.</p><p>Concentrations of nicotine and metabolites in plasma during active and placebo cigarette scans.</p
Positive correlation clusters of Δ[<sup>11</sup>C]carfentanil binding potential (BP<sub>ND</sub>) (placebo - active) versus ΔVAS of feel the effect category in smokers, displayed on trans-axial images of a gray-matter probability maps.
<p>Scatter plots of cluster Δ[<sup>11</sup>C]carfentanil BP<sub>ND</sub> values to ΔVAS are shown together with regression lines. VAS stands for the visual analog scale of smoking effects, and <i>R<sup>2</sup></i> stands for the coefficient of determination of linear regression.</p
Correlation clusters of [<sup>11</sup>C]carfentanil binding potential (BP<sub>ND</sub>) of placebo-cigarette scans versus the Fagerström Test for Nicotine Dependence (FTND) in smokers, displayed on trans-axial images of a gray-matter probability maps.
<p>Right panels show scatter plots using cluster [<sup>11</sup>C]carfentanil BP<sub>ND</sub>, together with regression line. In regression equations, <i>R<sup>2</sup></i> stands for the coefficient of determination.</p