Metabolic Side-Effects of the Novel Second-Generation Antipsychotic Drugs Asenapine and Iloperidone: A Comparison with Olanzapine
Bibliographic record
Abstract
BACKGROUND: The second generation antipsychotic (SGA) drugs are widely used in psychiatry due to their clinical efficacy and low incidence of neurological side-effects. However, many drugs in this class cause deleterious metabolic side-effects. Animal models accurately predict metabolic side-effects for SGAs with known clinical metabolic liability. We therefore used preclinical models to evaluate the metabolic side-effects of glucose intolerance and insulin resistance with the novel SGAs asenapine and iloperidone for the first time. Olanzapine was used as a comparator. METHODS: Adults female rats were treated with asenapine (0.01, 0.05, 0.1, 0.5, 1.0 mg/kg), iloperidone (0.03, 0.5, 1.0, 5.0, 10.0 mg/kg) or olanzapine (0.1, 0.5, 1.5, 5.0, 10.0 mg/kg) and subjected to the glucose tolerance test (GTT). Separate groups of rats were treated with asenapine (0.1 and 1.0 mg/kg), iloperidone (1.0 and 10 mg/kg) or olanzapine (1.5 and 15 mg/kg) and tested for insulin resistance with the hyperinsulinemic-euglycemic clamp (HIEC). RESULTS: Asenapine showed no metabolic effects at any dose in either test. Iloperidone caused large and significant glucose intolerance with the three highest doses in the GTT, and insulin resistance with both doses in the HIEC. Olanzapine caused significant glucose intolerance with the three highest doses in the GTT, and insulin resistance with the higher dose in the HIEC. CONCLUSIONS: In preclinical models, asenapine shows negligible metabolic liability. By contrast, iloperidone exhibits substantial metabolic liability, comparable to olanzapine. These results emphasize the need for appropriate metabolic testing in patients treated with novel SGAs where current clinical data do not exist.
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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 | 0.000 |
| 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".