Rat brain<scp>CYP2D</scp>activity alters<i>in vivo</i>central oxycodone metabolism, levels and resulting analgesia
Bibliographic record
Abstract
Abstract Oxycodone is metabolized by CYP2D to oxymorphone. Despite oxymorphone being a more potent opioid‐receptor agonist, its contribution to oxycodone analgesia may be minor because of low peripheral production, low blood–brain barrier permeability and central nervous system efflux. CYP2D metabolism within the brain may contribute to variation in central oxycodone and oxymorphone levels, thereby affecting analgesia. Brain CYP2D expression and activity are subject to exogenous regulation; nicotine induces rat brain, but not liver, CYP2D consistent with higher brain CYP2D in smokers. We assessed the role of rat brain CYP2D in orally administered oxycodone metabolism (in vivobrain microdialysis) and analgesia (tail‐flick test) by inhibiting brain CYP2D selectively with intracerebroventricular propranolol (mechanism‐based inhibitor) and inducing brain CYP2D with nicotine. Inhibiting brain CYP2D increased brain oxycodone levels (1.8‐fold;P < 0.03) and analgesia (1.5‐fold AUC0–60;P < 0.001) after oxycodone, while inducing brain CYP2D increased brain oxymorphone levels (4.6‐fold;P < 0.001) and decreased analgesia (0.8‐fold;P < 0.02). Inhibiting the induced brain CYP2D reversed the change in oxycodone levels (1.2‐fold;P > 0.1) and analgesia (1.1‐fold;P > 0.3). Brain, but not plasma, metabolic ratios were affected by pre‐treatments. Peak analgesia was inversely correlated withex vivobrain (P < 0.003), but not hepatic (P > 0.9), CYP2D activity. Altering brain CYP2D did not affect analgesia from oral oxymorphone (P > 0.9 for AUC0–60across all groups), which is not a CYP2D substrate. Thus, brain CYP2D metabolism alters local oxycodone levels and response, suggesting that people with increased brain CYP2D activity may have reduced oxycodone response. Factors that alter individual oxycodone response may be useful for optimizing treatment and minimizing abuse liability.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; 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".