Current insight on irinotecan dose adjustment in advanced colorectal cancers based on pharmacogenetic studies: an updated review
Bibliographic record
Abstract
Despite advancements in colorectal cancer screening and treatment, the occurrence, severity, and mortality rates have consistently risen among younger patients. Precision medicine aims to personalize cytotoxic drug dosages, such as irinotecan, by considering the pharmacogenetic specificity of glucuronidation backgrounds. Our search, focused on recent developments (2020-2024) in categorizing Uridine 5'-diphosphate-glucuronosyltransferase (UGT)1A1 variants related to irinotecan's safety, effectiveness, and cost-benefit in metastatic colorectal cancer patients identified 32 relevant clinical studies and recent reviews from 296 abstracts in PubMed and PubMed Central databases. This updated review emphasizes racial disparities in the incidence and essential variants influencing irinotecan's activated metabolite (SN-38). While UGT1A1*28 homozygosity is the primary cause of toxicity in North America, Europe, and a Middle Asian country, UGT1A1*6 is the prominent variant responsible in East Asian countries. Despite various methods employed for dose adjustment based on pharmacogenomic findings, individualization of the dose has been associated with reduced toxicity, improved response, and enhanced patient survival. The recommended irinotecan dose in the FOLFIRI regimen can be variable between 120mg/m2 to 350 mg/m2 based on the UGT1A1 genotype variant. Moreover, this approach appears to be cost-effective, as suggested by European and Chinese studies.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".