Therapeutic Drug Monitoring of Tumor Necrosis Factor Antagonists in Crohn Disease: A Theoretical Construct to Apply Pharmacokinetics and Guidelines to Clinical Practice
Bibliographic record
Abstract
Therapeutic drug monitoring (TDM) is the measurement of drug and antidrug antibody concentrations in individuals to guide treatment decisions. In patients with Crohn disease (CD), TDM, used either reactively or proactively, is emerging as a valuable tool for optimization of tumor necrosis factor (TNF) antagonist therapy. Reactive TDM is carried out in response to treatment failure, whereas proactive TDM involves the periodic monitoring of patients responding to TNF antagonist therapy to allow treatment optimization. In patients with CD, most of the available data for TDM relate to the first-to-market TNF antagonist infliximab and, to a lesser extent, to adalimumab and certolizumab pegol. Several gastroenterology associations, including the American Gastroenterology Association, have endorsed the use of reactive TDM in patients with active CD. However, fewer recommendations currently exist for the use of proactive TDM, although several new prospective randomized controlled trials evaluating proactive TDM strategies have been published. In this review, the current evidence for reactive and proactive TDM is discussed, and a proactive treatment algorithm for certolizumab pegol based on previously published threshold concentrations is proposed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".