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Record W2587274443 · doi:10.1093/ecco-jcc/jjx002.477

P352 Assessment of the use of therapeutic drug monitoring of Adalimumab during maintenance therapy in children with inflammatory bowel disease

2017· article· en· W2587274443 on OpenAlexaff
Mallory Chavannes, Florence Gervais, Alexandra Verreault, Colette Deslandres, Prévost Jantchou

Bibliographic record

VenueJournal of Crohn s and Colitis · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsAdalimumabMedicineUlcerative colitisInternal medicineInflammatory bowel diseaseTherapeutic drug monitoringRetrospective cohort studyDiseaseCohortPopulationMaintenance therapyDrugPharmacokineticsChemotherapyPharmacology

Abstract

fetched live from OpenAlex

Background: Adalimumab (ADA) is effective in the induction and maintenance of remission in pediatric Crohn's disease (CD). Pharmacokinetics analysis of the IMAgINE-1 study demonstrated that a higher ADA concentration is associated with greater rates of remission. Data remains scarce in terms of which level is predictive of clinical remission in pediatric patients. Moreover, few study, have established the rate of anti-adalimumab antibody (AAA) in this population. Methods: Aims: To review our experience with therapeutic drug monitoring (TDM) in children with CD and Ulcerative colitis (UC) treated with ADA. The primary objective was to assess the serum levels of ADA in children aged 5–18 years during maintenance. We also aim to 1) establish the rate of AAA in our population and 2) correlate the levels of ADA with clinical response. Methods: This was a single-center retrospective cohort study. Patients were included if they had at least one serum level of ADA/AAA drawn between June 2014 and November 2016. ADA and AAA analysis were performed in a central lab using a classic ELISA technique. We collected biochemical inflammatory markers drawn the same day or, if missing, at a maximum of two weeks prior to the ADA level. The physician global assessment (PGA) was also recorded if it was done within 4 weeks of the ADA level. Results: In our database, we identified 95 patients treated with ADA during the study period. Fifty-eight (61%) had at least 1 ADA level drawn (47 with CD, 9 patients with UC and 2 with Indeterminate colitis (IBD-U)). The 2 patients with IBD-U were excluded. Eleven CD patients (19%) had not received infliximab prior to initiating ADA. We collected 97 ADA levels (84% in CD). The overall median ADA level was 12 ug/mL, with a median dose of 0.74 mg/Kg/ injection and at a median frequency of every 14 days. Four patients, all with CD, developed AAA (7.14%). A weekly administration was associated with a higher drug level (14.8 vs 10.1ug/mL, p-value 0.0011). A PGA of remission was linked to 47 (55.3%) levels. An ADA level above 10 correlated with remission according to PGA with a sensitivity of 76% and a specificity of 47.4% (p-value 0.0205). AAA and previous infliximab exposure correlated with a lower in ADA level (p=0.0016 and 0.0448 respectively). Combination therapy improved ADA levels in all patients, but more significantly in patients previously exposed to infliximab (p=0.0369). Conclusions: ADA measurement is a useful tool in monitoring therapy in children with CD or UC. Aiming for levels above 10 ug/mL could be a target, as it correlates with clinical remission. Combination therapy helps to improve ADA levels. Prospective studies are required to better assess the role of TDM with ADA therapy.

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)0.007
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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.011
GPT teacher head0.252
Teacher spread0.241 · 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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Citations1
Published2017
Admission routes1
Has abstractyes

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