Prospect of a Dopamine Contribution in the Next Generation of Antidepressant Drugs: The Triple Reuptake Inhibitors
Bibliographic record
Abstract
Biogenic amine transporters for serotonin and norepinephrine (5-HT and NE respectively), are major targets for currently available antidepressant drugs, particularly those inhibiting the reuptake of 5-HT and/or NE. Compelling evidence suggest that dopamine (DA) is also involved in the pathophysiology and treatment of depression, leading to the development of a new class of antidepressants: the triple reuptake inhibitors that simultaneously inhibit 5-HT, NE and DA reuptake thereby prolonging their duration of action at postsynaptic levels. Although preclinical studies strongly suggest that triple reuptake inhibitors display antidepressant-like activity in various behavioural paradigms, including the forced swimming and the tail suspension tests, it has yet to be demonstrated that the addition of the dopaminergic component produces more robust effects than single- or dual-acting compounds. Several arguments favour this hypothesis and particularly the observation that DA may promote neurotrophic processes in the adult hippocampus, as 5-HT and NE do. It is thus possible that the stimulation of multiple signalling pathways resulting from the elevation of all three monoamines may account, in part, for an accelerated and/or greater antidepressant response. To predict the efficacy of triple reuptake inhibitors, it is important to take into consideration the existence of dense connections between monoaminergic neurons. Indeed, it is well established that the increase in central dopaminergic transmission regulates the neuronal activity of 5-HT and NE in the dorsal raphe (DR) and locus coeruleus (LC), respectively, while in turn, the ventral tegmental area (VTA), is sensitive to changes in 5-HT and NE release. This review synthetizes the pertinent litterature, focusing on the contribution of DA, to illustrate the rationale for designing improved antidepressants.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".