Reviewing pharmacogenetics to advance precision medicine for opioids
Bibliographic record
Abstract
BACKGROUND: Adequate opioid prescribing is critical for therapeutic success of pain management. Despite the widespread use of opioids, optimized opioid therapy remains unresolved with risk of accidental lethal overdosing. With the emergence of accumulating evidence linking genetic variation to opioid response, pharmacogenetic based treatment recommendations have been proposed. OBJECTIVE: The aim of this review is to evaluate pharmacogenetic evidence and provide an overview on genes involved in the pharmacokinetics and pharmacodynamics of opioids. METHODS: For this review, a systematic literature search of published articles was used in PubMed®, with no language restriction and between the time period of January 2000 to December 2020. We reviewed randomized clinical studies, study cohorts and case reports that investigated the influence of genetic variants on selected opioid pharmacokinetics and pharmacodynamics. In addition, we reviewed current CPIC clinical recommendations for pharmacogenetic testing. RESULTS: Results of this review indicate consistent evidence supporting the association between selected genetic variants of CYP2D6 for opioid metabolism. CPIC guidelines include recommendations that indicate the avoidance of tramadol use, in addition to codeine, in CYP2D6 poor metabolizers and ultrarapid metabolizers, and to monitor intermediate metabolizers for less-than-optimal response. While there is consistent evidence for OPRM1 suggesting increased postoperative morphine dosing requirements in A118G G-allele carriers, the clinical relevance remains limited. CONCLUSION: There is emerging evidence of clinical relevance of CYP2D6 and, to a lesser extent, OPRM1 polymorphism in personalized opioid drug dosing. As a result, first clinics have started to implement pharmacogenetic guidelines for CYP2D6 and codeine.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".