Role for Therapeutic Drug Monitoring During Induction Therapy with TNF Antagonists in IBD
Bibliographic record
Abstract
: Primary nonresponse and primary nonremission are important limitations of tumor necrosis factor (TNF) antagonists, occurring in 10% to 40% and 50% to 80% of patients with inflammatory bowel disease, respectively. The magnitude of primary nonresponse differs between phase III clinical trials and cohort studies, indicating differences, e.g., in definition, patient population or blinding. The causes of nonresponse can be attributed to the drug (pharmacokinetics, immunogenicity), the patient (genetics, disease activity), the disease (type, location, severity), and/or the treatment strategy (dosing regimen, combination therapy). Primary nonresponse has been attributed to "non-TNF-driven disease" which is an overly simplified and potentially misleading approach to the problem. Many patients with primary nonresponse could successfully be treated with dose optimization during the induction phase or switching to another TNF antagonist. Therefore, primary nonresponse is frequently not a non-TNF-driven disease. Recent studies from rheumatoid arthritis and preliminary data from inflammatory bowel disease evaluating therapeutic drug monitoring have suggested that early measurement of drug and anti-drug antibody concentrations could help to define primary nonresponse and rationalize patient management of this problem. Moreover, a modeling approach including pharmacological parameters and patient-related covariants could potentially be predictive for response to the treatment. We describe an overview of this evolution in thinking, underpinned by previous findings, and assess the potential role of early measurement of drug and antidrug antibody concentrations in the definition and management of primary nonresponse.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".