DPYD and Fluorouracil-Based Chemotherapy: Mini Review and Case Report
Bibliographic record
Abstract
5-Fluorouracil remains a foundational component of chemotherapy for solid tumour malignancies. While considered a generally safe and effective chemotherapeutic, 5-fluorouracil has demonstrated severe adverse event rates of up to 30%. Understanding the pharmacokinetics of 5-fluorouracil can improve the precision medicine approaches to this therapy. A single enzyme, dihydropyrimidine dehydrogenase (DPD), mediates 80% of 5-fluorouracil elimination, through hepatic metabolism. Importantly, it has been known for over 30-years that adverse events during 5-fluorouracil therapy are linked to high systemic exposure, and to those patients who exhibit DPD deficiency. To date, pre-treatment screening for DPD deficiency in patients with planned 5-fluorouracil-based therapy is not a standard of care. Here we provide a focused review of 5-fluorouracil metabolism, and the efforts to improve predictive dosing through screening for DPD deficiency. We also outline the history of key discoveries relating to DPD deficiency and include relevant information on the potential benefit of therapeutic drug monitoring of 5-fluorouracil. Finally, we present a brief case report that highlights a limitation of pharmacogenetics, where we carried out therapeutic drug monitoring of 5-fluorouracil in an orthotopic liver transplant recipient. This case supports the development of robust multimodality precision medicine services, capable of accommodating complex clinical dilemmas.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".