Therapeutic drug monitoring in inflammatory bowel disease: The dawn of reactive monitoring
Bibliographic record
Abstract
Inflammatory bowel disease (IBD) is a chronic condition that significantly affects the quality of life of its patients. Biologic drugs have been the mainstay treatment in the management of IBD patients but despite their significant contribution, there remains a proportion of patients that do not respond or lose response to treatment. Therapeutic drug monitoring (TDM) involves measuring levels of serum drug concentrations and anti-drug antibodies. TDM of biologic drugs initially emerged to understand treatment failure in other immune mediated inflammatory diseases. This was then introduced in IBD to rationalize primary non-response or secondary loss of response, given that low serum drug concentrations or the formation of anti-drug antibodies are variably associated with treatment failure. The aim of this narrative review is to provide an overview regarding the current use of TDM in clinical practice and to present the evidence available regarding its use in both proactive and reactive clinical settings in preventing and managing treatment failure. This review also presents the existing evidence regarding the association of various clinical outcomes with specific thresholds of drug concentrations, in everyday practice. A narrative review of published articles and conference abstracts regarding the use of TDM in IBD management, through an electronic search using PubMed and ScienceDirect. TDM has proven to be superior and more cost effective in guiding management of patients with treatment failure compared to empiric dose escalation or change in treatment. Despite a trend towards an association between clinical outcomes and drug concentrations, proactive TDM based strategies have not been shown to achieve clear benefit in long-term outcomes. In the clinical setting, TDM has proven to be useful in managing IBD patients, and its use in the reactive setting, as an additional tool to help manage patients with treatment failure, is being promoted as newer guidelines and consensus groups implement TDM as part of the management plan.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".