Case report: asenapine and anticholinergic toxicity
Bibliographic record
Abstract
While antipsychotic medications have long been associated with anticholinergic effects, asenapine has been purported to have no capacity for muscarinic cholinergic antagonism based on in vitro studies. Research in rat brain tissue has yielded different results, with one study finding more cholinergic M1-5 binding in the medial prefrontal cortex, dorsolateral frontal cortex and hippocampal CA1 and CA3 areas than would be predicted from in vitro findings. Moreover, it is structurally similar to other anticholinergic antipsychotics such as loxapine and, to a lesser degree, quetiapine, olanzapine and clozapine. This case report describes the anticholinergic toxidrome in a patient treated with benztropine and paroxetine at stable doses, with the emergence of the toxidrome after upward titration of asenapine. A broad differential was considered. With further consideration of the history, time-course, clinical features and physical examination, the presentation is most indicative of the anticholinergic toxidrome. Although not employed, physostigmine, the antidote for anticholinergic delirium, could help to differentiate this toxidrome and serve as a diagnostic and therapeutic intervention. We have presented this case to highlight the importance for clinicians to integrate history and bedside examination data with principles of pharmacology. In particular, asenapine should be added to the list of compounds with recognized anticholinergic potential.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.012 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".