Antipsychotic‐induced DRD2 upregulation and its prevention by α‐lipoic acid in SH‐SY5Y Neuroblastoma cells
Bibliographic record
Abstract
Most antipsychotic (AP) drugs are dopamine (DA) D2 receptor (DRD2) antagonists and remain the main pharmacological treatment of schizophrenia. Long-term AP use can give rise to tardive dyskinesia. It has been reported that chronic treatment with APs induces DRD2 upregulation and oxidative stress, which have been associated with tardive dyskinesia. We showed previously that H₂O₂-induced oxidative stress increased DRD2 expression in human SH-SY5Y neuroblastoma cells. We report here the effects of AP drugs on DRD2 expression levels in the same cell line and the effects of the inhibition of oxidative phenomena by (±)-α-lipoic acid treatment. Haloperidol, a first-generation AP, induced an increase in DRD2 protein and mRNA levels, whereas amisulpride, a second-generation AP, had no significant effect. (±)-α-Lipoic acid pretreatment reversed the haloperidol-induced DRD2 upregulation in mRNA and protein levels. Furthermore, haloperidol induced a larger increase of oxidative stress biomarkers (protein carbonylation, lipid peroxidation, and superoxide anion production) than amisulpride. (±)-α-Lipoic acid also attenuated AP-induced oxidative stress. Inhibition of catecholamine synthesis by α-methyl-DL-tyrosine (AMPT) increased DRD2 expression and prevented further increase by APs. Our results suggest that haloperidol-induced DRD2 upregulation is linked to oxidative stress and provide potential mechanisms by which (±)-α-lipoic acid can be considered as a therapeutic agent to prevent and treat side effects related to the use of first-generation APs.
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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) | 0.000 | 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 | 0.001 |
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".