OC63 Does checking thioguanine levels add to the treatment of paediatric IBD patients?
Bibliographic record
Abstract
Background Azathioprine is widely used in the maintenance treatment of inflammatory bowel disease (IBD) in both children and adults. The use of thiopurines is linked to dose-dependent adverse drug reactions, which frequently result in dose adjustments or discontinuation of the medication.1 Despite the increasing use of thiopurines in paediatric IBD management and the potential benefits of monitoring 6-TGN and 6-MMP metabolites for assessing compliance and optimizing therapy, routine monitoring of these metabolites is not standard practice.2 Methods Paediatric patients with IBD who have been started on Azathioprine between 01.04.23 and 31.10.24 are enrolled in the study. Diagnosis date, dose and start date of Azathioprine were recorded. Thiopurine methyl transferase levels (TMPT) levels were checked. The levels of azathioprine metabolites, Thioguanine and MMP levels were checked approximately in 3 months. Results In total, 42 patients were enrolled in the study. Mean age of the patients at the diagnosis were 12.81±2.97 years old (range 4–16) 42% (18) were female. Out of 42 patients, 64.3% were Crohn’s, 28.6% were Ulcerative Colitis and 7.1% were IBD-U (table 1). TPMT levels of 35 out of 42 patients were available. 4 (11.4%) of them had low levels of TPMT, 26 (74.3%) were in normal range (26–50) and 5 (14.3%) had high levels (table 3). The thioguanine levels were available in 27 out of 42 patients. The average time for checking 6-TGN were 88.68±45.3 days (min:36 max 248). In 33.3% (9) of the patients, the levels were below the normal range,%26.9 (8) were above the range and 37% (10) were within range (table 2). Conclusion Azathioprine metabolite monitoring is more common in adult IBD management, as specific metabolite ranges are linked to improved response and reduced risk of adverse effects.3 4 In paediatrics, it is not an established practice, however there are publications implying the usefulness and importance of metabolite monitoring in IBD maintenance treatment.5 6 In our cohort, the thioguanine levels in 33.3% (n=9) of the patients were below the therapeutic range and 3 of them had dose increase based on these results and the rest of them were reviewed from compliance point of view. Also 26.8% of the patients were found to have TGN levels above the therapeutic range. The monitoring of TGN metabolites can be useful to optimise the IBD treatment in paediatric patients, not only in terms of avoiding toxicity and optimising the dose but also would be effective to assess the compliance which is a common obstacle in the treatment of teenage patients. References Nguyen TV, Vu DH, Nguyen TM, et al. Relationship between azathioprine dosage and thiopurine metabolites in pediatric IBD patients: identification of covariables using multilevel analysis. Ther Drug Monit. 2013;35:251–257. Chevaux JB, Peyrin-Biroulet L, Sparrow MP. Optimizing thiopurine therapy in inflammatory bowel disease. Inflamm Bowel Dis. 2011;17:1428–1435. Goel RM, Blaker P, Mentzer A, et al. Optimizing the use of thiopurines in inflammatory bowel disease. Therapeutic advances in chronic disease 2015;6(3):138–146. Wilson L, Tuson S, Yang L, et al. Real-world use of azathioprine metabolites changes clinical management of inflammatory bowel disease. Journal of the Canadian Association of Gastroenterology 2021;(3):101–109. Tresman R, Mutalib M, Kammermeier J, et al. (, February). Azathioprine dosing and metabolite measurement in paediatric inflammatory bowel disease—does one size fit all? Journal of Crohns & Colitis 2018;12:S454-S454. Bąk-Drabik K, Adamczyk P, Duda-Wrońska J, et al. Usefulness of measuring thiopurine metabolites in children with inflammatory bowel disease and autoimmunological hepatitis, treated with azathioprine. Gastroenterology Research and Practice 2021;(1):9970019.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".