Retrospective Analysis of the Utility of Genetic Testing in Predicting Drug Response in Chronic Pain
Bibliographic record
Abstract
Abstract Introduction Chronic pain is often unrecognized and/or undertreated, and as a result has significant impact on functional abilities, quality of life, societal participation and health care utilization. Medications remain a mainstay of treatment, but selection for any given patient remains a challenge when trying to predict efficacy and/or side effects. There is interest to see whether genetic analysis of how a given drug is processed for a patient can help with rational drug choices. This appears to have some early support in cardiac, psychiatric and acute pain studies. We studied whether genetic analysis of drug processing using the Pillcheck program could have helped in choosing the appropriate medications in a cohort of patients suffering from chronic pain. To our knowledge this type of study has not been completed in this environment and/or patient population. Methods We retrospectively studied a 31 patient cohort seen in the Canadian Forces Health Services Unit (Ottawa) Physiatry clinic. All patients suffered from a diagnosed chronic pain condition, completed the Pillcheck genetic drug processing analysis and filled-in questionnaires looking at efficacy and side effects of the drugs. We analyzed the correlation between the Pillcheck predictions and participants’ self-reported treatment efficacy and tolerability. The goal was to explore the clinical utility of Pillcheck results in guiding prescriptions for chronic pain patients. Results 31 patients returned completed questionnaires and had samples taken. Forty eight percent of the participants were actively treated with one of the study pain medications, and 84% had been taking at least one of these medications and discontinued. Pillcheck scores did not correlate with self-reported efficacy of any of the medications, nor did it correlate with self-reported side effects. Furthermore, active medications were more likely to receive a score indicating caution should be exerted, than were medications which had been discontinued. Discussion In a small cohort of pain patients with comorbid psychiatric disorders, genetic profiling using Pillcheck did not seem to correlate with reported benefit or side effect profile of commonly prescribed pain medications. Furthermore, discontinued medications were no more likely to be marked as warranting caution than did actively used medications. Conclusion Retrospectively using pharmacogenetics to guide medication selection in Canadian Forces members with chronic pain did not correlate with response or side effects. A larger prospective controlled study, measuring numerous clinical and non- clinical outcomes would be worthwhile in the future before widespread adoption for patients living with chronic pain.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".