Pharmacogenetics of Biochemically Verified Abstinence in an Opioid Agonist Therapy Randomized Clinical Trial of Methadone and Buprenorphine/Naloxone
Bibliographic record
Abstract
Methadone and buprenorphine/naloxone are opioid agonist therapies for opioid use disorder treatment. Genetic factors contribute to individual differences in opioid response; however, little is known regarding genetic associations with clinical outcomes in people receiving opioid agonist therapies. Participants diagnosed with opioid use disorder, principally consisting of prescription opioids (licit or illicit), were randomized to methadone or buprenorphine/naloxone for 24 weeks of daily treatment (NCT03033732). Urine was collected at 12 biweekly study visits and analyzed for non-treatment opioids. Variants in genes involved in methadone metabolism (CYP2B6, CYP2C19, and CYP3A4), buprenorphine metabolism (CYP3A4 and UGT2B7), and μ-opioid receptor function (OPRM1) were genotyped and analyzed for their association with the number of non-treatment opioid-free urine screens. Primary analyses focused on the last 12 weeks (6 study visits, post-titration) of treatment among those reporting White ethnicity. Additional sensitivity and exploratory analyses were performed. Among methadone-treated participants (n = 52), the OPRM1 rs1799971 AA genotype (vs. G-genotypes, i.e., having one or two G alleles) was associated with greater opioid-free urine screens (incidence rate ratio = 5.24, 95% confidence interval (CI) = 2.43-11.26, P = 0.000023); longitudinal analyses showed a significant genotype-by-time interaction over the full 24 weeks (12 study visits, β = -0.28, 95% CI = -0.45 to -0.11, P = 0.0015). Exploratory analyses suggest an OPRM1 rs1799971 genotype effect on retention. No evidence of association was found between other genetic variants, including in metabolic variants, and non-treatment opioid-free urine screens in the methadone or buprenorphine/naloxone arms. Those with the OPRM1 rs1799971 G-genotypes may have a poorer response to methadone maintenance treatment, an effect that persisted through 24 weeks of treatment.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".