Recent developments in the psychobiology and pharmacotherapy of depression: optimising existing treatments and novel approaches for the future
Bibliographic record
Abstract
Effective antidepressants include monoamine oxidase inhibitors and tricyclic antidepressants, selective serotonin re-uptake inhibitors and novel agents, including serotonin and noradrenaline re-uptake inhibitors. Although effective, current treatments most often produce partial symptomatic improvement (response) rather than symptom resolution and optimal functioning (remission). While current pharmacotherapies target monoaminergic systems, different symptoms of major depressive disorder (MDD) may have distinct neurobiological underpinnings and other neurobiological systems are likely involved in the pathogenesis of MDD. In this article a review of current pharmacotherapeutic options for MDD, current understanding of the neurobiology and pathogenesis of MDD and a review of new and promising directions in pharmacological research will be provided. It is generally accepted that no single neurotransmitter or system is responsible for the dysregulation found in MDD. While agents that affect monoaminergic systems will likely continue to be first-line treatments for MDD for the foreseeable future, a number of new and novel agents, including corticotropin-releasing factor antagonists, substance P antagonists and antiglucocorticoids show considerable promise for refining treatment options. In order to better understand the neurobiology and treatment response of MDD, it is probable that more sophisticated theory-driven typologies of MDD will have to be developed.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".