Utilizing Therapeutic Drug Monitoring of Biological Therapy in Inflammatory Bowel Disease
Bibliographic record
Abstract
Introduction: Reactive therapeutic drug monitoring (TDM) is currently considered as the new standard of care for optimizing anti-tumor necrosis factor (anti-TNF) therapy in inflammatory bowel disease (IBD). Nevertheless, there is still no consensus on proactive TDM, other than anti-TNF biologics and the drug thresholds to target. We aimed to determine the clinical utility of TDM for biological therapy in IBD. Methods: We used a modified Delphi method to establish consensus. A comprehensive literature review was performed regarding the use of TDM of biological therapy in IBD and presented to a panel of 13 international IBD experts. Subsequently, 28 statements were formulated describing when and how to apply TDM in clinical practice. These were rated on a scale of 1 to 10 and agreement was set if score ≥7. Upon disagreement, statements were discussed and revised based on the available evidence followed by a second round of voting. Statements were accepted if 80% or more of the participants agreed, or refused if lower than 80% panel agreement. Results: The panel agreed on 24 statements. For anti-TNF therapies, proactive TDM was found to be appropriate after induction and at least once during maintenance therapy, but this was not the case for the other biologics. Reactive TDM was appropriate for all agents for both primary non-response and secondary loss of response (Table 1). The panellists also agreed on several statements regarding TDM and appropriate drug and anti-drug antibody concentration thresholds for biologics in specific clinical scenarios (Tables 2 and 3). Conclusion: Despite limited data from randomised controlled trials, there was significant agreement about the utility of TDM of biological therapy in IBD. More data are also needed to identify optimal drug concentration and anti-drug antibodies thresholds as these can vary depending on the therapeutic outcome to target.687_A Figure 1 No Caption available.687_B Figure 2 No Caption available.687_C Figure 3 No Caption available.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".