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S0652 Real World Exposure-Response Relationship of Vedolizumab in Inflammatory Bowel Disease: A Pooled Multicenter Observational Cohort Analysis of Clinical and Modeled Pharmacological Data

2020· article· en· W3094481828 on OpenAlexaff
Niels Vande Casteele, William J. Sandborn, Brian G. Feagan, Séverine Vermeire, Parambir S. Dulai, Julián Panés, Andrés Yarur, Xavier Roblin, Shomron Ben‐Horin, Iris Dotan, Mark T. Osterman, Dirk Lindner, Christian Agboton, Maria Rosario, Teresa Osborn

Bibliographic record

VenueThe American Journal of Gastroenterology · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsRobarts Clinical TrialsWestern University
Fundersnot available
KeywordsVedolizumabMedicineInternal medicineInflammatory bowel diseaseUlcerative colitisTherapeutic drug monitoringDosingPopulationGastroenterologyDiseasePharmacokinetics

Abstract

fetched live from OpenAlex

INTRODUCTION: The role of vedolizumab (VDZ) therapeutic drug monitoring in patients with ulcerative colitis (UC) or Crohn's disease (CD) is under investigation. However, a thorough understanding of the relationship between VDZ serum concentrations and clinical outcomes is lacking. METHODS: ERELATE was a retrospective cohort study conducted at 9 centers in 6 countries. Data were pooled from real-world clinical cohorts of patients with UC or CD treated with VDZ. Clinically important treatment outcomes based on definitions commonly used across centers were collected at Weeks 14, 26, and 52. Using a population pharmacokinetic model,1 VDZ dosing, baseline body weight, and albumin concentration were used to predict VDZ concentrations. A Bayesian approach incorporated observed VDZ concentrations to refine individual predicted concentrations. Relationships between predicted concentrations and observed clinically important outcomes were investigated. VDZ concentration thresholds were determined using the optimal Youden index point along the receiver operating characteristic curve. Correlation and agreement between observed and predicted concentrations were evaluated using Spearman (ρ) and intra-class correlation (ICC) coefficients, respectively. RESULTS: In total 695 patients (304 UC, 391 CD) were included; 47.9% were male. Median age was 39 years, median disease duration was 9 years, and 86.3% had prior anti-tumor necrosis factor exposure. Clinically important outcome rates for UC and CD are shown in Table 1. Significant correlation ([ρ = 0.879; P < 0.01] across all timepoints) and agreement (ICC = 0.722; P < 0.01) were noted between observed and predicted concentrations, indicating adequate Bayesian model prediction of individual VDZ concentrations. Observed exposure-response relationships were more pronounced in UC than CD. Differences in VDZ concentration and drug concentration area under the curve from Weeks 0–6 were observed between remitters and non-remitters for UC and CD combined (Table 2). Predicted VDZ concentrations of 30.8 and 33.8 µg/mL at Week 6 and 16.6 and 14.4 µg/mL at Week 14 were associated with clinical remission and deep remission, respectively, at Week 52. CONCLUSION: These real-world data confirm GEMINI trial findings of significant relationships between VDZ serum concentration and clinical outcomes. Adequate drug concentrations during induction therapy may be important predictors of short- and long-term treatment outcomes.Table 1.: Clinically Important Outcomes in Patients With UC or CDTable 2.: Predicted Vedolizumab Trough Concentrations Associated With Clinical and Deep Remission in Patients With UC or CD

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)0.017
metaresearch head score (Gemma)0.015
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 score0.017
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.053
GPT teacher head0.343
Teacher spread0.290 · 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".

Quick stats

Citations3
Published2020
Admission routes1
Has abstractyes

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