A medicinal chemistry investigation of 3,4-Methylenedioxymethamphetamine (MDMA)
Bibliographic record
Abstract
3,4-Memylenedioxymethamphetamine (MDMA) 1, the active chemical constituent of the illicit drug ecstasy, is a psychotropic agent whose effects are primarily modulated through the serotonergic system. In recent years numerous researchers have identified MDMA as possessing therapeutic activity towards a variety of disease states. Parkinson's disease (PD) is a common and disabling neurodegenerative disorder. The primary symptomatic treatment of PD utilises the dopamine precursor levodopa. Long-term levodopa therapy typically elicits deleterious side-effects, the most significant being levodopa-induced dyskinesia (LED), the severity of which may negate the therapeutic benefit of levodopa. MDMA has been demonstrated in primate models to possess both anti-parkinsonian and anti-LED activity. Burkitt's lymphoma (BL) is a malignant disease of the lymphatic system, affecting B-cell lymphocytes in particular. Recent characterisation of a functioning immunoreactive serotonin reuptake transporter (SERT) in B-cell lines has been impetus for the investigation of SERT as a target for drug therapy in BL. MDMA, a known SERT substrate, exhibitss an anti-proliferative and pro-apoptotic response in a BL cell line (L3055). This thesis, prompted by recent reports of the therapeutic activity of MDMA in various disease states, details a medicinal chemistry investigation of MDMA, Chapters One, Two and Three document the analogues synthesised. These analogues were intended for evaluation as anti-neoplasties for Burkitt's lymphoma and for evaluation as therapeutics for the treatment of PD. This work was conducted in collaboration with Prof. John Gordon and coworkers (Centre for Immune Regulation at The University of Birmingham), and Dr Jonathan Brotchie and coworkers (Toronto Western Research Institute). Additionally a series of putative monoamine oxidase (MAO; EC 1.4.3.4) inhibitors was conceived, synthesised and assayed (Chapter Four). These compounds were structurally analogous to selegiline 10, a clinically useful MAO-B inhibitor.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".