Therapeutic drug monitoring of biologics in inflammatory bowel disease
Bibliographic record
Abstract

 
 
 Biologics have revolutionized the management of patients with inflammatory bowel disease (IBD), in both ulcerative colitis (UC) and Crohn’s disease (CD). There are several classes of biologics used to treat IBD, including monoclonal antibodies directed against TNF, integrin, IL12/23, and IL-23 monoclonal antibodies. Despite the effectiveness of anti-TNF medications, approximately 30% of patients are primary non-responders (PNR), and another 50% lose response over time (secondary loss of response [SLR]). Therapeutic drug monitoring (TDM) provides a tool for biologic dose optimization by measuring drug trough concentrations and anti-drug antibodies (ADA). Drug concentrations are positively correlated to therapeutic benefits, but questions remain on how, when and for whom to perform TDM. Successful implementation is challenged by several factors such as variations in optimal drug targets, different types of drug detection assays, individual pharmacokinetics, and disease severity. Over recent years, various expert groups have provided guidelines on reactive TDM of anti-TNF therapies; however, a knowledge gap still exists on the role of proactive TDM, as well as reactive TDM for non-anti-TNF biologics. The most recent and comprehensive expert consensus statement published in the American Journal of Gastroenterology (AJG), attempted to fill this gap by advocating for the use of reactive TDM for anti-TNF medications, as well as for proactive TDM in certain scenarios.
 
 
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".