Therapeutic Drug Monitoring of Biologics for Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND: Although tumor necrosis factor (TNF) antagonists are effective for the treatment of Crohn's disease and ulcerative colitis, lack and loss of clinical response is a clinical challenge. Accordingly, the use of therapeutic drug monitoring has been proposed as a means to optimize treatment. This article reviews the mechanisms of and factors which influence clearance of biologics, the relationship between serum drug concentrations and antidrug antibody presence and treatment efficacy, and identifies areas for future research needs regarding the use of therapeutic drug monitoring in clinical practice. METHODS: Publications regarding these topics were identified from literature searching and supplemented by review of gastroenterology meeting presentations and reference lists. RESULTS: The clearance of monoclonal antibodies and pegylated antibody fragments is complex, and may be affected by demographic variables, concomitant medications, inflammatory burden, and immunogenicity, leading to high interpatient variability in plasma concentration of drug and clinical response. Several observational studies have demonstrated a relationship between anti-TNF agent serum drug concentrations and/or antidrug antibody presence and various symptomatic and objective clinical endpoints. However, these relationships are not absolute, and although some algorithms for the use of therapeutic drug monitoring in clinical practice have been proposed, none have yet been validated in a prospective clinical trial. CONCLUSIONS: Further research to identify the most appropriate use of therapeutic drug monitoring is needed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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; both teacher heads agree on what is shown here.
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".