Next-Generation MDMA Analogue SDMA: Pharmacological and Metabolic Insights
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide 3,4-Methylenedioxymethamphetamine (MDMA), commonly known as ecstasy, shows promise in treating depression and post-traumatic stress disorder (PTSD), resulting in breakthrough status. However, concerns regarding MDMA’s abuse potential and cytotoxicity have sparked interest in developing safer analogues with similar therapeutic benefits. This study investigated the pharmacological properties of MDMA analogues in which the 1,3-benzodioxole group is replaced by a 1,3-benzoxathiole, termed SDA and SDMA, compared to MDA and MDMA through in silico, in vitro, and in vivo assays. In vitro experiments using human embryonic kidney (HEK293) cells examined the interactions with monoamine transporters. SDA and SDMA showed similar profiles to MDMA at the serotonin transporter (SERT), while both inhibited dopamine (DAT) and norepinephrine (NET) transporters more potently, in line with in silico molecular docking fitness scores of binding. SDA and SDMA also showed increased potency in evoking efflux through SERT and DAT acting as partial releasers. SDA and SDMA exhibited a similar interaction profile with 5-HT 2 receptors compared with their respective analogues. Metabolism studies revealed faster clearance rates for SDA and SDMA, in contrast to MDA and MDMA, which exhibited only weak degradation. In contrast to MDMA’s rewarding effects, SDMA did not induce significant effects in mice, while SDA only produced a significant preference for the drug-paired compartment at the lowest dose tested. Moreover, while SDMA shares similar locomotor and hyperthermic profiles as MDMA in mice, SDA induced increased hyperlocomotion and more sustained hyperthermia. In conclusion, these findings suggest that SDMA, with enhanced metabolic profiles and reduced abuse potential, is a promising candidate for further studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".