In vitro drug-drug interactions in rat liver between oxycodone and commonly co-consumed drugs
Bibliographic record
Abstract
Background and Purpose: The co-use of opioids and other drugs is prevalent, both in a clinical setting and during opioid misuse. We investigated whether drug-drug interactions occur between oxycodone and xylazine, diazepam, etizolam, and methamphetamine using rat liver microsomes. Experimental Approach: Michaelis–Menten parameters (Km, Vmax), and intrinsic clearance (CLint) of oxymorphone and noroxycodone formation were determined. Inhibition Kis for xylazine, diazepam, and etizolam were assessed. Inhibition of oxycodone and dextromethorphan (probe substrate) metabolite formation by selective P450- and test-inhibitors was investigated. Key Results: Km values of oxymorphone (75 μM) and noroxycodone (74 μM) formation were similar. The Vmax of noroxycodone formation (1235 pmol/min/mg) was higher than oxymorphone (354 pmol/min/mg), leading to three times higher CLint of noroxycodone. Xylazine and etizolam were competitive inhibitors of oxymorphone (Ki = 0.5 and 9.5 μM, respectively) and noroxycodone (Ki = 2.6 and 14.6 μM, respectively) formation, while diazepam was a competitive inhibitor of oxymorphone formation (Ki = 3.7 μM) and a mixed inhibitor of noroxycodone formation (Ki = 8.3 μM). Xylazine, diazepam, and etizolam, as well selective P450-inhibitors, were confirmed to inhibit both CYP2D- and CYP3A-mediated pathways in rat liver microsomes. Methamphetamine was a moderate inhibitor of oxymorphone formation, and weak inhibitor of noroxycodone formation. Conclusion and Implications: Xylazine, diazepam, and etizolam are potent inhibitors of the formation of both primary oxycodone metabolites and leading to potential drug-drug interactions. Further, xylazine, diazepam, and etizolam could inhibit both CYP2D- and CYP3A-mediated pathways, leading to potentially altered drug metabolism of other opioids and substrates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".