An Evaluation of the Effects of the Novel Antipsychotic Drug Lurasidone on Glucose Tolerance and Insulin Resistance: A Comparison with Olanzapine
Bibliographic record
Abstract
Over the past two decades, there has been a notable rise in the use of antipsychotic drugs, as they are used to treat an increasing number of neuropsychiatric disorders. This rise has been led predominantly by greater use of the second generation antipsychotic (SGA) drugs, which have a low incidence of neurological side-effects. However, many SGAs cause metabolic dysregulation, including glucose intolerance and insulin resistance, thus increasing the risk of cardiometabolic disorders. The metabolic effects of the novel SGA lurasidone, which was approved by the Food and Drug Administration in 2010, remain largely unknown. As rodent models accurately predict the metabolic effects of SGAs in humans, the aim of the present study was to use sophisticated animal models of glucose tolerance and insulin resistance to measure the metabolic effects of lurasidone. In parallel, we compared the SGA olanzapine, which has established metabolic effects. Adult female rats were treated with vehicle, lurasidone (0.2, 0.8 or 2.0 mg/kg, s.c.) or olanzapine (10.0 mg/kg, s.c.) and subjected to the glucose tolerance test (GTT). Separate groups of rats were treated with vehicle, lurasidone (0.2, 0.8 or 2.0 mg/kg, s.c.) or olanzapine (1.5 and 15 mg/kg, s.c.) and tested for insulin resistance with the hyperinsulinemic-euglycemic clamp (HIEC). Compared to vehicle treated animals, lurasidone caused mild glucose intolerance in the GTT with a single dose, but there was no effect on insulin resistance in the GTT, measured by HOMA-IR. The HIEC also confirmed no effect of lurasidone on insulin resistance. In contrast, olanzapine demonstrated dose-dependent and potent glucose intolerance, and insulin resistance in both tests. Thus, in preclinical models, lurasidone demonstrates mild metabolic liability compared to existing SGAs such as olanzapine. However, confirmation of these effects in humans with equivalent tests should be confirmed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".