Methadone: a review of drug-drug and pathophysiological interactions
Bibliographic record
Abstract
Numerous established and potential drug interactions with methadone are clinically important in people treated with methadone either for addiction or for chronic pain. Methadone users often have comorbidities and are prescribed drugs that may interact with methadone. Methadone is extensively metabolized by cytochrome P450 (CYP) 3A4 and to a lesser extent by CYP 1A2, 2D6, 2D8, 2C9/2C8, 2C19, and 2B6. Eighty-six percent of methadone is protein bound, predominately to α1-acid glycoprotein (AGP). Polymorphisms in or interactions with CYPs that metabolize methadone, changes in protein binding, and other pathophysiological conditions affect the pharmacokinetic properties of methadone. It is critical for health care providers who treat patients on methadone to have adequate information on the interactions of methadone with other drugs of abuse and other medications. We set out to describe drug-drug interactions as well as physiological and pathophysiological factors that may impact the pharmacokinetics of methadone. Using MEDLINE, we conducted a systematic search for papers and related abstracts published between 1966 and June 2010. Keywords that included methadone, drug-drug interactions, CYP P450 and AGP identified a total of 7709 papers. Other databases, including the Cochrane Database of Systematic Reviews and Scopus, were also searched; an additional 929 papers were found. Final selection of 286 publications was based on the relevance of each paper to the topic. Over 50 such interactions were found. Interactions of methadone with other drugs can lead to increased or decreased methadone drug levels in patients and result in potential overdose or withdrawal, respectively. The former can contribute to methadone's fatality. Prescribers of methadone and pharmacists should enquire about any new medications (including natural products and over-the-counter medications) periodically, and especially when an otherwise stable patient suddenly experiences drug craving, withdrawal or intoxication.
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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 | 0.020 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".