A hormetic approach to understanding antidepressant effectiveness and the development of antidepressant tolerance – A conceptual view
Bibliographic record
Abstract
Antidepressants such as the selective serotonin reuptake inhibitors (SSRIs) have complex temporal effects. They may worsen symptoms during early treatment, they may reduce depressive symptoms over several weeks of treatment, and they may lose effectiveness over more prolonged treatment or after repeated treatment trials. Conceptually, these effects fall within the domain of hormesis, which refers to a biphasic or multiphasic response to a drug or toxin. Hormetic effects are commonly triggered when a drug interacts with homeostatic mechanisms. We develop and evaluate a theoretical framework for understanding how adaptations to SSRIs that restore synaptic homeostasis may partially contribute to their hormetic effects. Specifically, the serotonin system adapts to SSRIs by suppressing the firing of serotonergic neurons, inhibiting the synthesis of serotonin, and reducing the overall content of serotonin in the brain. Moreover, rodent models such as inescapable shock show that serotonin neurotransmission to specific forebrain regions is a necessary, but insufficient cause of depressive symptoms. Our review suggests: (1) early worsening of symptoms may be related to the direct effects of SSRIs on synaptic serotonin; (2) the symptom-reducing effects could be related to the loss of serotonin content in the brain during SSRI exposure; (3) the loss of efficacy over prolonged exposure could be related to the central nervous system equilibrating to the SSRIs. The serotonin system's adaptations to SSRIs may play a clinically meaningful role in their hormetic effects on depressive symptoms. A complete understanding of SSRIs' hormetic effects will require exploring temporal dynamics in other neurotransmitter systems.
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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 | 0.003 | 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 | 0.001 | 0.016 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| 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".