Prospective evaluation of the pharmacogenetics of azathioprine in the treatment of inflammatory bowel disease
Bibliographic record
Abstract
AIM: To investigate whether pharmacogenetic loci or metabolite concentrations explain clinical response or side effects to AZA. METHODS: Patients with IBD were given 2 mg/kg of AZA without dose escalation or adjustment. Serial clinical response, thiopurine methyl transferase (TPMT) activity and thioguanine nucleotide (TGN) concentrations were measured over 6 months. All patients were genotyped for inosine triphosphatase (ITPase) and TPMT. Clinical response and side effects were compared to these variables. RESULTS: Two hundred and seven patients were analysed. Thirty-nine per cent withdrew due to adverse effects. Heterozygous TPMT genotype strongly predicted adverse effects (79% heterozygous vs. 35% wild-type TPMT, P < 0.001). The ITPA 94C>A mutation was associated with withdrawal due to flu-like symptoms (P = 0.014). A baseline TPMT activity below 35 pmol/h/mg/Hb was associated with a greater chance of clinical response compared with a TPMT above 35 pmo/h/mg/Hb (81% vs. 43% respectively, P < 0.001). Patients achieving a mean TGN level above 100 were significantly more likely to respond (P = 0.0017). CONCLUSIONS: TPMT testing predicts adverse effects and reduced chance of clinical response (TPMT >35 pmol/h/mg/Hb). ITPase deficiency is a predictor of adverse effects and TGN concentrations above 100 correlate with clinical response.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".