Vesicular monoamine transporters: Structure‐function, pharmacology, and medicinal chemistry
Bibliographic record
Abstract
Vesicular monoamine transporters (VMAT) are responsible for the uptake of cytosolic monoamines into synaptic vesicles in monoaminergic neurons. Two closely related VMATs with distinct pharmacological properties and tissue distributions have been characterized. VMAT1 is preferentially expressed in neuroendocrine cells and VMAT2 is primarily expressed in the CNS. The neurotoxicity and addictive properties of various psychostimulants have been attributed, at least partly, to their interference with VMAT2 functions. The quantitative assessment of the VMAT2 density by PET scanning has been clinically useful for early diagnosis and monitoring of the progression of Parkinson's and Alzheimer's diseases and drug addiction. The classical VMAT2 inhibitor, tetrabenazine, has long been used for the treatment of chorea associated with Huntington's disease in the United Kingdom, Canada, and Australia, and recently approved in the United States. The VMAT2 imaging may also be useful for exploiting the onset of diabetes mellitus, as VMAT2 is also expressed in the β-cells of the pancreas. VMAT1 gene SLC18A1 is a locus with strong evidence of linkage with schizophrenia and, thus, the polymorphic forms of the VMAT1 gene may confer susceptibility to schizophrenia. This review summarizes the current understanding of the structure-function relationships of VMAT2, and the role of VMAT2 on addiction and psychostimulant-induced neurotoxicity, and the therapeutic and diagnostic applications of specific VMAT2 ligands. The evidence for the linkage of VMAT1 gene with schizophrenia and bipolar disorder I is also discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".