Overcoming Resistance to Selective Serotonin Reuptake Inhibitors: Targeting Serotonin, Serotonin-1A Receptors and Adult Neuroplasticity
Bibliographic record
Abstract
Major depressive disorder (MDD) is the most prevalent mental illness contributing to global disease burden. Selective serotonin (5-HT) reuptake inhibitors (SSRIs) are the first-line treatment for MDD, but are only fully effective in 30% of patients and require weeks before improvement may be seen. About 30% of SSRI-resistant patients may respond to augmentation or switching to another antidepressant, often selected by trial and error. Hence a better understanding of the causes of SSRI resistance is needed to provide models for optimizing treatment. Since SSRIs enhance 5-HT, in this review we discuss new findings on the circuitry, development and function of the 5-HT system in modulating behavior, and on how 5-HT neuronal activity is regulated. We focus on the 5-HT1A autoreceptor, which controls 5-HT activity, and the 5-HT1A heteroreceptor that mediates 5-HT actions. A series of mice models now implicate increased levels of 5-HT1A autoreceptors in SSRI resistance, and the requirement of hippocampal 5-HT1A heteroreceptor for neurogenic and behavioral response to SSRIs. We also present clinical data that show promise for identifying biomarkers of 5-HT activity, 5-HT1A regulation and regional changes in brain activity in MDD patients that may provide biomarkers for tailored interventions to overcome or bypass resistance to SSRI treatment. We identify a series of potential strategies including inhibiting 5-HT auto-inhibition, stimulating 5-HT1A heteroreceptors, other monoamine systems, or cortical stimulation to overcome SSRI resistance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".