Nicotine Increases Codeine Analgesia Through the Induction of Brain CYP2D Central Activation of Codeine to Morphine
Bibliographic record
Abstract
Cytochrome P450 2D (CYP2D) metabolizes codeine to morphine, a step required for codeine analgesia. However lower morphine permeability across the blood‐brain barrier, as well as exportation of morphine, suggest that after codeine the initial morphine in the brain, and resulting analgesia, may be due to brain CYP2D activation. Higher brain, but not liver, CYP2D6 is found in smokers, and 7‐day nicotine treatment induces rat brain, but not liver, CYP2D. We hypothesized that inducing rat‐brain CYP2D would increase the CNS CYP2D‐mediated activation of codeine to morphine resulting in increased analgesia, despite first‐pass metabolism , which could be blocked by inhibition of brain CYP2D. Codeine (20mg/kg ip) analgesia was tested: compared to pretreatment with vehicle, 7‐day nicotine (1mg/kg sc, brain CYP2D inducer) increased analgesia (1.59, AUC 0‐30min fold change vs. vehicle; p<0.001), propranolol (20 μg in 4 μl icv, brain CYP2D inhibitor) decreased analgesia (0.56; p<0.05), and co‐pretreatment was similar to vehicle (1.23; p>0.1). 7‐day nicotine increased and propranolol decreased brain, but not plasma, morphine levels; analgesia correlated with brain (p<0.02), but not plasma (p>0.4), morphine levels at 15 min after codeine. Pretreatments (alone or together) had no effect on baseline nociception or morphine (3.5mg/kg ip) analgesia. Thus, variation in brain CYP2D activity may contribute to clinically relevant differences in individual response to centrally acting CYP2D substrates. Individuals exposed to nicotine may have increased brain metabolism contributing to altered drug efficacy and abuse liability. Funding: CIHR and the CAMH Foundation
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".