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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".