A Comprehensive Literature Review and Expert Consensus Statement on Therapeutic Drug Monitoring of Biologics in Inflammatory Bowel Disease
Bibliographic record
Abstract
Therapeutic drug monitoring (TDM) of biologics is a rapidly evolving field. We aimed to provide a consensus statement regarding the clinical utility of TDM for biologics in inflammatory bowel disease (IBD). A modified Delphi method was applied to develop consensus statements. A comprehensive literature review was performed regarding TDM of biologic therapies in IBD, and 45 statements were subsequently formulated on the potential application of TDM in IBD. The statements, along with literature, were then presented to a panel of 10 gastroenterologists with expertise in IBD and TDM who anonymously rated them on a scale of 1-10 (1 = strongly disagree and 10 = strongly agree). An expert consensus development meeting was held virtually to review, discuss, refine, and reformulate statements that did not meet criteria for agreement or that were ambiguous. During the meeting, additional statements were proposed. Panelists then confidentially revoted, and statements rated ≥7 by 80% or more of the participants were accepted. During the virtual meeting, 8 statements were reworded, 7 new statements were proposed, and 19 statements were rerated. Consensus was finally reached in 48/49 statements. The panel agreed that reactive TDM should be used for all biologics for both primary nonresponse and secondary loss of response. It was recommended that treatment discontinuation should not be considered for infliximab or adalimumab until a drug concentration of at least 10-15 μg/mL was achieved. Consensus was also achieved regarding the utility of proactive TDM for anti-tumor necrosis factor therapy. It was recommended to perform proactive TDM after induction and at least once during maintenance. Consensus was achieved in most cases regarding the utility of TDM of biologics in IBD, specifically for reactive and proactive TDM of anti-tumor necrosis factors.
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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 | 0.076 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.035 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".