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.
Keywords: chronic pain service; pediatric chronic pain; pharmacogenetics (PGx); precision medicine; qualitative analysis.