Physiologically Based Pharmacokinetic Modeling to Assess the Impact of CYP2D6‐Mediated Drug‐Drug Interactions on Tramadol and O‐Desmethyltramadol Exposures via Allosteric and Competitive Inhibition
Bibliographic record
Abstract
Tramadol is an opioid medication used to treat moderately severe pain. Cytochrome P450 (CYP) 2D6 inhibition could be important for tramadol, as it decreases the formation of its pharmacologically active metabolite, O-desmethyltramadol, potentially resulting in increased opioid use and misuse. The objective of this study was to evaluate the impact of allosteric and competitive CYP2D6 inhibition on tramadol and O-desmethyltramadol pharmacokinetics using quinidine and metoprolol as prototypical perpetrator drugs. A physiologically based pharmacokinetic model for tramadol and O-desmethyltramadol was developed and verified in PK-Sim version 8 and linked to respective models of quinidine and metoprolol to evaluate the impact of allosteric and competitive CYP2D6 inhibition on tramadol and O-desmethyltramadol exposure. Our results show that there is a differentiated impact of CYP2D6 inhibitors on tramadol and O-desmethyltramadol based on their mechanisms of inhibition. Following allosteric inhibition by a single dose of quinidine, the exposure of both tramadol (51% increase) and O-desmethyltramadol (52% decrease) was predicted to be significantly altered after concomitant administration of a single dose of tramadol. Following multiple-dose administration of tramadol and a single-dose or multiple-dose administration of quinidine, the inhibitory effect of quinidine was predicted to be long (≈42 hours) and to alter exposure of tramadol and O-desmethyltramadol by up to 60%, suggesting that coadministration of quinidine and tramadol should be avoided clinically. In comparison, there is no predicted significant impact of metoprolol on tramadol and O-desmethyltramadol exposure. In fact, tramadol is predicted to act as a CYP2D6 perpetrator and increase metoprolol exposure, which may necessitate the need for dose separation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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 teacher head, 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".