“Breakthrough” Dopamine Supersensitivity during Ongoing Antipsychotic Treatment Leads to Treatment Failure over Time
Bibliographic record
Abstract
Antipsychotics often lose efficacy in patients despite chronic continuous treatment. Why this occurs is not known. It is known, however, that withdrawal from chronic antipsychotic treatment induces behavioral dopaminergic supersensitivity in animals. How this emerging supersensitivity might interact with ongoing treatment has never been assessed. Therefore, we asked whether dopamine supersensitivity could overcome the behavioral and neurochemical effects of antipsychotics while they are still in use. Using two models of antipsychotic-like effects in rats, we show that during ongoing treatment with clinically relevant doses, haloperidol and olanzapine progressively lose their efficacy in suppressing amphetamine-induced locomotion and conditioned avoidance responding. Treatment failure occurred despite high levels of dopamine D2 receptor occupancy by the antipsychotic and was at least temporarily reversible by an additional increase in antipsychotic dose. To explore potential mechanisms, we studied presynaptic and postsynaptic elements of the dopamine system and observed that antipsychotic failure was accompanied by opposing changes across the synapse: tolerance to the ability of haloperidol to increase basal dopamine and dopamine turnover on one side, and 20-40% increases in D2 receptor number and 100-160% increases in the proportion of D2 receptors in the high-affinity state for dopamine (D2(High)) on the other. Thus, the loss of antipsychotic efficacy is linked to an increase in D2 receptor number and sensitivity. These results are the first to demonstrate that "breakthrough" supersensitivity during ongoing antipsychotic treatment undermines treatment efficacy. These findings provide a model and a mechanism for antipsychotic treatment failure and suggest new directions for the development of more effective antipsychotics.
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 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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".