Identifying Opioid-Related Pharmacogenomic Variants in High-Risk Hospitalized Infants: A Pilot Study
Bibliographic record
Abstract
Abstract Introduction Pharmacogenomic (PGx) variants impact the pharmacodynamics and pharmacokinetics of opioids and the brain’s reward, cognition, stress, and pain pathways. This study examines the prevalence of PGx variants impacting opioid response among a cohort of high-risk infants. Methods This retrospective study was conducted at a quaternary children’s hospital from 2009-2020. Infants <1y with ≥1 high-risk condition (congenital heart disease (CHD), medical or surgical necrotizing enterocolitis (NEC), thoracoabdominal surgery, very or extremely low birthweight, hypoxic ischemic encephalopathy, or extracorporeal membrane oxygenation) with exome sequencing were included. Prevalence of opioid-related variants ( COMT, DRD2/ANKK1, ABCB1, OPRM1, CYP2B6, CYP2D6 ) were compared to Ensembl, a database of published genomic cohorts. Results Overall, 111 high-risk infants were identified (62.2% male, 47.7% Hispanic/Latino, 18.0% premature, and 82.0% CHD). Most underwent surgery (68.2%), with 37.8% undergoing CHD surgery. Overall, 81.1% of infants were homozygous for ≥1 opioid-related variant(s). Compared to Ensembl, high-risk infants had a significantly higher frequency of homozygosity for ABCB1: rs1045642 (43.6% vs. 18.7%, p<0.001) and rs2032582 (42.7% vs. 15.9%, p<0.001). Conversely, high-risk infants had a lower frequency of homozygosity for COMT: rs4818 (2.7% vs. 10.3%, p<0.001) and OPRM1: rs1799971 (2.7% vs. 7.1%, p<0.001). All infants with surgical NEC (N=5) were homozygous for ≥1 opioid-related variant. The most common metabolizer phenotypes were intermediate (CYP2B6: 35.5%, CYP2D6: 20.0%) and normal (CYP2B6: 53.9%, CYP2D6: 70.9%). Conclusion Most high-risk infants carried at least one opioid-related variant, with frequencies that are significantly different from broader genetic cohorts. Larger studies inclusive of intronic PGx variants using ancestrally comparable controls are needed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".