Thiopurine S-methyltransferase polymorphisms in acute lymphoblastic leukemia, inflammatory bowel disease and autoimmune disorders: influence on treatment response
Bibliographic record
Abstract
Abstract: The thiopurine S-methyltransferase ( TPMT ) gene encodes for the TPMT enzyme that plays a crucial role in the metabolism of thiopurine drugs. Genetic polymorphisms in this gene can affect the activity of the TPMT enzyme and have been correlated with variability in response to treatment with thiopurines. Advances in the pharmacogenetics of TPMT allowed the development of dosing recommendations and treatment strategies to optimize and individualize prescribing thiopurine in an attempt to enhance treatment efficacy while minimizing toxicity. The influence of genetic polymorphisms in the TPMT gene on clinical outcome has been well-documented and replicated in many studies. In this review, we provide an overview of the evolution, results, conclusions and recommendations of selected studies that investigated the influence of TPMT pharmacogenetics on thiopurine treatment in acute lymphoblastic leukemia, inflammatory bowel disease and autoimmune disorders. We focus mainly on prospective studies that explored the impact of individualized TPMT-based dosing of thiopurines on clinical response. Together, these studies demonstrate the importance of preemptive TPMT genetic screening and subsequent dose adjustment in mitigating the toxicity associated with thiopurine treatment while maintaining treatment efficacy and favorable long-term outcomes. In addition, we briefly address the cost-effectiveness of this pharmacogenetics approach and its impact on clinical practice as well as the importance of recent breakthrough advances in sequencing and genotyping techniques in refining the TPMT genetic screening process. Keywords: TPMT, pharmacogenetics, thiopurine, 6-mercaptopurine, azathioprine, ADRs
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".