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Record W2921295719 · doi:10.1093/jcag/gwz006.025

A26 USE OF DRUG-TOLERANT ASSAYS IN INFLAMMATORY BOWEL DISEASE; WHAT DOES IT TELL US?

2019· article· en· W2921295719 on OpenAlexaff
Margaret Walshe, Krzysztof Borowski, Joanne M. Stempak, Mark S. Silverberg

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2019
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsMount Sinai Hospital
Fundersnot available
KeywordsDrugMedicineAdalimumabInfliximabAntibodyInflammatory bowel diseaseInternal medicinePharmacologyClinical significanceImmunologyGastroenterologyTumor necrosis factor alphaDisease

Abstract

fetched live from OpenAlex

Therapeutic drug monitoring helps guide decision making in the treatment of IBD with anti-TNF therapies. Unlike older assays, newer drug-tolerant assays (such as the Prometheus assay) enable detection of drug antibodies in the presence of drug. The clinical relevance of drug antibodies detected in the presence of drug however is poorly understood. To assess the clinical relevance of antibodies to infliximab and adalimumab detected in the presence of drug. In particular, to assess the association of such antibodies with drug levels and CRP. The Prometheus assay was used to measure infliximab(IFX)/adalimumab(ADA) drug concentration and antibody levels in IBD patients treated with anti-TNF. Drug level was compared between samples whereby both drug and antibody were detectable (Drug+AB+), vs. samples with detectable drug and negative antibodies (Drug+AB-). Where CRP was assessed contemporaneously, CRP was compared between samples with detectable drug and antibody (Drug+AB+) vs. Drug+AB- samples. CRP was also compared between Drug+AB+ samples and Drug-AB+ samples. Where patients with detectable antibodies (either Drug+AB+ or Drug-AB+) had a subsequent test performed, we compared persistence of drug antibodies; Drug+AB+ vs. Drug-AB+. For each analysis, where patients had >1 relevant sample, only the earliest sample was used for analysis. 913 samples (511 IFX, 402 ADA) from 564 patients were analysed. Median IFX level in Drug+AB+ samples was 3.8μg/mL (IQR 1.7–8.6) vs. median IFX level of 13.2μg/mL (IQR 6.5–23.1) in Drug+AB- samples; p<0.0001. Median ADA level in Drug+AB+ samples was 7.5μg/mL (IQR 3.4–10.5) vs. median ADA level of 14.7μg/mL (IQR 10.1–21.0) in Drug+AB- samples; p<0.0001 (Mann-Whitney U test). CRP results were available for 345 samples. Median CRP for Drug+AB+, Drug+AB-, and Drug-AB+ samples was 3.2mg/L (IQR1.8–14.1), 2.5mg/L (IQR 0.8–8.3), and 4.6mg/L (IQR 2.4- 19.3) respectively. CRP in the Drug+AB+ group did not differ significantly from Drug+AB- or Drug-AB+ groups, p=0.10 and p=0.39 (Mann-Whitney U test). 32 patients who had antibodies detected (22 Drug+AB+ and 10 Drug-AB+) had a subsequent assay. 12(54.5%) Drug+AB+ and 9(90%) Drug-AB+ patients had persistent drug antibodies on their subsequent test; p=0.11 (Fisher’s exact test). IFX and ADA antibodies detected in the presence of drug are associated with lower drug levels, suggesting that such antibodies significantly impact drug pharmacokinetics. Antibodies detected in the presence of drug however are not associated with differences in CRP. Persistence of antibodies over time was evident in a lesser proportion of Drug+AB+ patients vs. Drug-AB+ patients. Whilst this finding failed to reach statistical significance, this may have been due to insufficient patient numbers to adequately power this aspect of our study. Drug assays funded by Prometheus

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)0.051
metaresearch head score (Gemma)0.098
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.051
Threshold uncertainty score0.268

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0510.098
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0040.004
Science and technology studies0.0010.006
Scholarly communication0.0060.011
Open science0.0030.002
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0040.003

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 head0.010
GPT teacher head0.242
Teacher spread0.232 · 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 designNot applicable
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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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