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Therapeutic drug monitoring with vedolizumab in inflammatory bowel disease

2020· review· en· W2990546292 on OpenAlexaff
Daniela Pugliese, Giuseppe Privitera, Fabrizio Pizzolante, Antonio Gasbarrini, Luisa Guidi, Alessandro Armuzzi

Bibliographic record

VenueMinerva Gastroenterologica e Dietologica · 2020
Typereview
Languageen
FieldImmunology and Microbiology
TopicBiosimilars and Bioanalytical Methods
Canadian institutionsUniversity Hospital Foundation
Fundersnot available
KeywordsVedolizumabMedicineTherapeutic drug monitoringInflammatory bowel diseaseIntensive care medicineDrugClinical trialClinical PracticeDiseaseInternal medicinePharmacologyPhysical therapy

Abstract

fetched live from OpenAlex

Therapeutic drug monitoring (TDM) is a useful tool for decision-making process in patients with inflammatory bowel disease (IBD) treated with anti TNF-α drugs, especially when experiencing loss of response. Growing evidences support the existence of exposure-response relationship with vedolizumab, but the utility and the appropriate use of TDM in clinical practice is still under debate. In this review, we summarize all evidences supporting a TDM-guided approach for patients treated with vedolizumab, suggesting three potential scenarios: 1) early prediction of long-term outcomes; 2) verifying the best strategy in case of loss of response; 3) maximizing therapeutic efficacy during maintenance treatment. Vedolizumab through concentrations <20 µg/mL at week 6 and >12 µg/mL seem to be associated with more favorable outcomes. No comparative studies have been conducted so far to demonstrate the advantage of adopting a TDM-guided versus an empirical approach for managing primary or secondary nonresponses. The frequency of antibodies to vedolizumab detection is quite low (up to 4% in pivotal trials), suggesting, unlike of anti TNF-α agents, a low probability of experiencing an immune-mediated pharmacokinetic failure in clinical practice. Future prospective and controlled studies are warranted to establish the guidance on the use of a TDM-guided approach with vedolizumab in clinical practice.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Other designhigh
models splitAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.971
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.044
GPT teacher head0.303
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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Other design
Domainnot available
GenreReview

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

Citations5
Published2020
Admission routes1
Has abstractyes

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