Opioid antagonism mitigates antipsychotic-associated weight gain: focus on olanzapine
Bibliographic record
Abstract
BACKGROUND: The endogenous opioid system affects metabolism, including weight regulation. Evidence from preclinical and clinical studies provides a rationale for targeting this system to mitigate weight-related side effects of antipsychotics. This review describes the role of the opioid system in regulating weight and metabolism, examines the effects of opioid receptor antagonism on those functions, and explores the use of opioid antagonists to mitigate antipsychotic-associated weight gain and/or metabolic effects. METHODS: A PubMed literature search was conducted to identify representative opioid antagonists and associated preclinical and clinical studies examining their potential for the regulation of weight and metabolism. RESULTS: The mu opioid receptor (MOR), delta opioid receptor (DOR), and kappa opioid receptor (KOR) types have overlapping but distinct patterns of central and peripheral expression, and each contributes to the regulation of body weight and metabolism. Three representative opioid antagonists (eg, naltrexone, samidorphan, and LY255582) were identified for illustration. These opioid antagonists differed in their receptor binding and pharmacokinetic profiles, including oral bioavailability, systemic clearance, and half-life, and were associated with varying effects on food intake, energy utilization, and metabolic dysregulation. CONCLUSIONS: Preclinical and clinical data suggest that antagonism of the endogenous opioid system is a mechanism to address antipsychotic-associated weight gain and metabolic dysregulation. However, evidence suggests that the differing roles of MOR, DOR, and KOR in metabolism, together with the differences in receptor binding, pharmacokinetic, and functional activity profiles of the opioid receptor antagonists discussed in this review, likely contribute to their differential pharmacodynamic effects and clinical outcomes observed regarding antipsychotic-associated weight gain.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".