The norepinephrine transporter inhibitor reboxetine reduces stimulant effects of MDMA ("ecstasy") in humans

This study assessed the pharmacodynamic and pharmacokinetic effects of the interaction between the selective norepinephrine (NE) transporter inhibitor reboxetine and 3,4-methylenedioxymethamphetamine (MDMA, "ecstasy") in 16 healthy subjects. The study used a double-blind, placebo-controlled crossover design. Reboxetine reduced the effects of MDMA including elevations in plasma levels of NE, increases in blood pressure and heart rate, subjective drug high, stimulation, and emotional excitation. These effects were evident despite an increase in the concentrations of MDMA and its active metabolite 3,4-methylenedioxyamphetamine (MDA) in plasma. The results demonstrate that transporter-mediated NE release has a critical role in the cardiovascular and stimulant-like effects of MDMA in humans.

Duloxetine inhibits effects of MDMA ("ecstasy") in vitro and in humans in a randomized placebo-controlled laboratory study

This study assessed the effects of the serotonin (5-HT) and norepinephrine (NE) transporter inhibitor duloxetine on the effects of 3,4-methylenedioxy-methamphetamine (MDMA, ecstasy) in vitro and in 16 healthy subjects. The clinical study used a double-blind, randomized, placebo-controlled, four-session, crossover design. In vitro, duloxetine blocked the release of both 5-HT and NE by MDMA or by its metabolite 3,4-methylenedioxyamphetamine from transmitter-loaded human cells expressing the 5-HT or NE transporter. In humans, duloxetine inhibited the effects of MDMA including elevations in circulating NE, increases in blood pressure and heart rate, and the subjective drug effects. Duloxetine inhibited the pharmacodynamic response to MDMA despite an increase in duloxetine-associated elevations in plasma MDMA levels. The findings confirm the important role of MDMA-induced 5-HT and NE release in the psychotropic effects of MDMA. Duloxetine may be useful in the treatment of psychostimulant dependence.

Non-Contact Documentation of Physical Characteristics of Ecstasy Tablets, Hemp Coins, and Imprint Punches by using 3D Optical Surface Scanning

The description of seized illicit ecstasy tablets and other pressed drug products is an important step in casework. The physical and visual analysis and the description of the characteristics can be employed for intelligence purposes. Besides photography and manual measurements of dimensions, some optical instruments are employed for detailed measurements of physical characteristics. In this work, the method of 3D surface digitizing is introduced as a suitable tool for highly accurate documentation of small objects, especially for pressed drug products. The resulting detailed information about the geometry, and the results of an automatic comparison of apparently uniform tablets and coins with punches, can support drug intelligence.

Tabulation of multiple responses

Although multiple-response questions are quite common in survey research, Stata’s official release does not provide much capability for an effective analysis of multiple-response variables. For example, in a study on drug addiction an interview question might be, “Which substances did you consume during the last four weeks?” The respondents just list all the drugs they took, if any; e.g., an answer could be “cannabis, cocaine, heroin” or “ecstasy, cannabis” or “none”, etc. Usually, the responses to such questions are stored as a set of variables and, therefore, cannot be easily tabulated. I will address this issue here and present a new module to compute one- and two-way tables of multiple responses. The module supports several types of data structure, provides significance tests, and offers various options to control the computation and display of the results. In addition, tools to create graphs of multiple-response distributions are presented.

Tabulation of multiple responses

Although multiple response questions are quite common in survey research, Stata's official release does not provide much possibility for an effective analysis of multiple response variables. For example, in a study on drug addiction an interview question might be, "Which substances did you consume during the last four weeks?" The respondents just list all the drugs they took if any, e.g., an answer could be "cannabis, cocaine, heroin" or "ecstasy, cannabis" or "none", etc. Usually, the responses to such questions are held as a set of variables and, therefore, cannot be easily tabulated. I will address this issue and present a new module to compute one- and two-way tables of multiple responses. The module supports several types of data structure, provides significance tests, and offers various options to control the computation and display of the results.