Moving Away from One-Size-Fits-All: Assessing the Use of Pharmacogenetic-Guided Medication Therapy in Pediatric Patients with Chronic Pain
Bibliographic record
Abstract
Background/Objectives: Pharmacogenetic (PGx) testing can predict drug efficacy, toxicity, and risk of adverse drug reactions (ADRs). However, PGx-guided prescribing for pediatric chronic pain is underutilized. Methods: We evaluated the rate of deviance from standard drug dosing regimens in children and adolescents with chronic pain based on PGx testing of drug-metabolizing genes. We also assessed the acceptability and feasibility of PGx testing and implementation of PGx-guided recommendations from patient, caregiver, and prescriber perspectives. Finally, we explored whether PGx results could predict self-reported therapeutic responses and/or ADRs to medications. Results: Forty-eight participants aged 8–17 years with chronic pain provided DNA via buccal swab. Genetic variant data for CYP2D6, CYP2C9, and CYP2C19 metabolism genes and associated metabolizer status were analyzed with respect to clinical PGx guidelines for dosing recommendations of analgesics and psychotropic medications. Participants, their caregivers, and their prescribers also completed quantitative questionnaires evaluating their experience with PGx testing. Twenty-three (50%) participants were predicted to benefit from non-standard dosing for medications with clinical PGx guidelines. Participants expressed satisfaction with the PGx testing process and felt it was safe and worthwhile. Prescribers also reported that PGx results were relevant for medication choices in 42 (91%) participants. Seven (15%) participants had genotyping results which may have predicted their self-reported therapeutic responses and/or ADRs to specific medications. Conclusions: Though further research on pharmacodynamic associations is required to sufficiently address the complexity of interpatient responses to medications for the treatment of pediatric pain and mental health conditions, PGx testing may be used to inform individualized medication choices based on genetic make-up.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".