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Record W2794232784 · doi:10.1093/jcag/gwy008.103

A102 THIOPURINE METABOLITE LEVEL MONITORING LEADS TO INDIVIDUALIZED AND OPTIMIZED THIOPURINE THERAPY IN ADULT INFLAMMATORY BOWEL DISEASE (IBD)

2018· article· en· W2794232784 on OpenAlexaff
Julie Zhu, Juan G. Abraldeṣ, Levinus A. Dieleman, Vivian Huang, Karen I. Kroeker, Farhad Peerani, Karen Wong, Donald F. LeGatt, Richard N. Fedorak, Brendan P. Halloran

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2018
Typearticle
Languageen
FieldMedicine
TopicAcute Lymphoblastic Leukemia research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsThiopurine methyltransferaseMedicineAllopurinolMetaboliteTherapeutic drug monitoringInternal medicineGastroenterologyAzathioprineDosingInflammatory bowel diseaseXanthine oxidaseMercaptopurineToxicityPharmacologyPharmacokineticsDiseaseChemistry

Abstract

fetched live from OpenAlex

The thiopurine drugs (6-MP, azathioprine (AZA)) are purine anti-metabolites commonly used in IBD. About 55% of active IBD fail to respond to thiopurine weight based dosing. Studies suggest 6-thioguanine (6-TG) and 6-methylmercaptopurine (6-MMP) levels are better therapeutic targets than weight-based regimens. The therapeutic range of 6-TG is 400–750*. 6-TG levels >750*, 6-MMP levels >6600* increase risk of bone marrow, hepatic toxicity respectively. Xanthine oxidase inhibitor allopurinol (ALL) can decrease the shunting to 6-MMP and increase 6-TG levels in “shunters” (6-MMP/6-TG ratios >20). To assess thiopurine metabolite levels in adult IBD patients, physician response to levels, how they altered therapy and patient outcomes. Metabolite levels were obtained retrospectively from a chart review of 159 adult IBD patients between 2014 and 2015. All had levels measured for non-response or toxicity. Clinical outcomes were examined. Steady state metabolite levels were analyzed using the Dervieux-Boulieu method. Sub-therapeutic (subT) clinical response was the main indication for assessing levels (69.2%). Mean 6-TG and 6-MMP levels were 443.4*, 3690.5* respectively. SubT 6-TG and supra-target 6-MMP levels occurred in 95 (60%), 27 (17%) patients. Mean 6-MMP/6-TG ratio was 10.9 (95% CI 8.8–13.0). There were 25 shunters with mean ratio 35.1 (range 21.2–68.1). Overall physician responses to abnormal levels were dose alternation or initiating new therapy (89%), and to diagnosis of shunting was dose reduction with initiation of ALL (76% (19/25)). Highest concordance was seen in subT 6-TG levels and sub-clinical response (59%). AZA dose escalation did not lead to incremental change in 6-TG level; 6-MMP level was nonlinearly increased by higher AZA dose (Figure 1). Highest 6-TG levels (586.6*) were seen in AZA+5-ASA+anti-TNFα group (95% CI 429.8–734.4*, AZA 137.5mg) compared to thiopurine monotherapy group (474.1*,95% CI 361.7–586.6*, AZA 160.7mg). Lowest 6-TG level 392.4*(95% CI 303.5–481.3*, AZA 141.2mg) was seen in AZA+anti-TNFα group (NS ANOVA p= 0.32). There was no difference in AZA dose in these three groups (p=0.19). AZA dose was lowest in AZA+ALL group, and achieved comparable 6-TG levels (AZA 71.4mg, p<0.0002, 6-TG 453.3* p=0.66). In 5 patients, metabolite levels were re-measured; there were reduction of 6-MMP and increased 6-TG levels post ALL use and none developed side effects (Table 1). This data suggests that weight based dosing is a suboptimal way to achieve therapeutic levels of 6-TG. The addition of ALL may be a safe alternative for those who do not response to standard dose escalation. The optimal interval of serial metabolite monitoring remains to be determined. *pmol/8x108 RBC None

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.016
GPT teacher head0.275
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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