Pharmacological fingerprint of antipsychotic drugs at the serotonin 5-HT2A receptor
Bibliographic record
Abstract
Abstract The intricate involvement of the serotonin 5-HT 2A receptor (5-HT 2A R) both in schizophrenia and in the activity of antipsychotic drugs is widely acknowledged. The currently marketed antipsychotic drugs, although effective in managing the symptoms of schizophrenia to a certain extent, are not without their repertoire of serious side effects. There is a need for better therapeutics to treat schizophrenia for which understanding the mechanism of action of the current antipsychotic drugs is imperative. With bioluminescence resonance energy transfer (BRET) assays, we trace the signaling signature of six antipsychotic drugs belonging to three generations at the 5-HT 2A R for the entire spectrum of signaling pathways activated by serotonin (5-HT). The antipsychotic drugs display previously unidentified pathway preference at the level of the individual Gα subunits and β-arrestins. In particular, risperidone, clozapine, olanzapine and haloperidol showed G protein-selective inverse agonist activity. In addition, G protein-selective partial agonism was found for aripiprazole and cariprazine. Pathway-specific apparent dissociation constants determined from functional analyses revealed distinct coupling-modulating capacities of the tested antipsychotics at the different 5-HT-activated pathways. Computational analyses of the pharmacological and structural fingerprints support a mechanistically based clustering that recapitulate the clinical classification (typical/first generation, atypical/second generation, third generation) of the antipsychotic drugs. The study provides a new framework to functionally classify antipsychotics that should represent a useful tool for the identification of better and safer neuropsychiatric drugs and allows formulating hypotheses on the links between specific signaling cascades and in the clinical outcomes of the existing drugs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".