Innovations in CNS drug discovery: differentiating strategies to treat depression
Bibliographic record
Abstract
Over the past two decades, the clinical management of depression has been revolutionized by the introduction of selective serotonin re-uptake inhibitors and serotonin/noradrenaline re-uptake inhibitors. However, despite this progress, several unmet medical needs remain. These challenges, which collectively represent the next frontier for antidepressant drug discovery, range from improving efficacy in treatment-resistant patients, to accelerating onset of therapeutic activity, to reducing deleterious side effects such as emesis or sexual dysfunction. The present review addresses some of the innovative approaches designed to create novel therapies that improve in one or more of these areas. Additionally, the authors propose that to discover truly novel disease-modifying agents we must improve our appreciation of disease etiology, pathophysiology and genetics. Therefore, while it is still very early in the characterization of these strategies - as well as our general understanding of disease progression - the next several years should allow sufficient time for one (or more) of these approaches to differentiate themselves from current therapies.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".