Thiopurine methyltransferase: should it be measured before commencing thiopurine drug therapy?
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Bibliographic record
Abstract
Thiopurines [azathioprine (AZA), 6-mercaptopurine (6-MP) and thioguanine (6-TG)] have a well-established role as immunosuppressive agents in a variety of chronic inflammatory conditions, haematological neoplasia and in transplant rejection. Despite good overall clinical response rates, particularly when used as steroid sparing agents, adverse effects are a limiting problem leading to withdrawal in up to a quarter of patients. Severe myelosuppression is the most serious toxicity occurring early or occasionally later during treatment. An understanding of the competing pathways involved in the metabolism of thiopurines has important implications for predicting some of the more severe toxicity seen with these drugs. Thiopurine methyl transferase (TPMT) is an enzyme catalysing the methylation of 6-MP, competing with xanthine oxidase (XO) and hypoxanthine guanine phosphoribosyl transferase (HGPRT) to determine the amount of 6-MP metabolised to cytotoxic thioguanine nucleotides. Allelic polymorphisms in the TPMT gene predict the activity of the enzyme such that 1 in 10 of the population are heterozygous and have approximately 50% of normal activity, whilst 1 in 300 are completely deficient. As a result, these individuals are at high risk of severe myelosuppression. Conversely, individuals with very high levels of TPMT activity are hyper-methylators in whom clinical response is less likely. Prior knowledge of TPMT status avoids exposure of individuals with zero TPMT to potentially fatal treatment with AZA or 6-MP and provides one of the best examples of predictive pharmacogenetics in therapeutics. This article reviews literature on the role of TPMT measurement prior to treatment with thiopurines and provides some guidance to the use of TPMT as a guide to tailoring thiopurine therapy.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| 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 it