Serotonin Reuptake Inhibitors: The Corner Stone in Treatment of Depression for Half a Century – A Medicinal Chemistry Survey
Bibliographic record
Abstract
Inhibition of serotonin (5-HT) reuptake has been a central theme in the therapy of depression for half a century. Through the years these therapies have improved, particularly with regard to side effects, and today's selective serotonin reuptake inhibitors (SSRIs) constitute a reasonably effective offer for the patients. However, there is still room for major improvement and considering that almost 20% of the population in the western world will experience a depressive period in their lifetime, there is a large need for improved therapies. A large spectrum of targets and strategies are currently being pursued, but so far none of these new approaches have been successful, mainly due to lack of a deeper understanding of the disease biology. Since inhibition of 5-HT reuptake ensures a certain degree of antidepressant efficacy, there has been a large interest in various combinations with serotonin reuptake inhibitors (SRIs) in order to improve on the shortcomings of treatment with SSRIs. Some of these approaches have resulted in marketed antidepressants, eg combinations of SRI with norepinephrine (NE) reuptake inhibition, whereas other approaches are still at an experimental stage. This review attempts to present the current status of these add-on/combination approaches with particular focus on the medicinal chemistry aspects.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".