Pharmacogenomics and Drug-Induced Phenoconversion Informed Medication Safety Review in the Management of Pain Control and Quality of Life: A Case Report
Bibliographic record
Abstract
Utilizing pharmacogenomics (PGx) and integrating drug-induced phenoconversion to guide opioid therapies could improve the treatment response and decrease the occurrence of adverse drug events. Genetics contribute to the interindividual differences in opioid response. The purpose of this case report highlights the impact of a PGx-informed medication safety review, assisted by a clinical decision support system, in mitigating the drug-gene and drug-drug-gene interactions (DGI and DDGI, respectively) that increase the risk of an inadequate drug response and adverse drug events (ADEs). This case describes a 69-year-old female who was referred for PGx testing for uncontrolled chronic pain caused by osteoarthritis and neuropathy. The clinical pharmacist reviewed the PGx test results and medication regimen and identified several (DGIs and DDGIs, respectively) at Cytochrome P450 (CYP) 2C19 and CYP2D6. The recommendations were to: (1) switch tramadol to buprenorphine transdermal patch, an opioid with lower potential for ADEs, to mitigate a CYP2D6 DDGI; (2) gradually discontinue amitriptyline to alleviate the risk of anticholinergic side effects, ADEs, and multiple DDGIs; and (3) optimize the pregabalin. The provider and the patient agreed to implement these recommendations. Upon follow-up one month later, the patient reported an improved quality of life and pain control. Following the amitriptyline taper, the patient experienced tremors in the upper and lower extremities. When the perpetrator drug, omeprazole, was stopped, the metabolic capacity was no longer impeded; the patient experienced possible amitriptyline withdrawal symptoms due to the rapid withdrawal of amitriptyline, which was reinitiated and tapered off more slowly. This case report demonstrates a successful PGx-informed medication safety review that considered drug-induced phenoconversion and mitigated the risks of pharmacotherapy failure, ADEs, and opioid misuse.
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 | 0.020 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".