Towards therapeutic drug monitoring of TNF inhibitors for children with juvenile idiopathic arthritis: a scoping review
Bibliographic record
Abstract
OBJECTIVES: Before a clinician decides whether treatment with TNF inhibition in children with JIA has failed, one should ensure adequate systemic exposure has been achieved. Therapeutic drug monitoring might allow for improved treatment outcome with lower treatment-associated costs. However, this requires understanding of the pharmacokinetic (PK) characteristics, and the pharmacokinetic/pharmacodynamic (PK/PD) relationship for children with JIA. We performed a scoping review to summarize the available literature and identify areas for future research. METHODS: A systematic search was conducted of the Medline, EMBASE, Web of Science and Cochrane databases as well as the clinicaltrials.gov registry. In total, 3959 records were screened and 130 publications were selected for full text assessment. RESULTS: Twenty publications were included and divided into three categories: PK (n = 9), PK/PD (n = 3) and anti-drug antibodies (n = 13). Industry involvement was significant in 14 publications. Although data are limited, systemic exposure to TNF inhibitors is generally lower in younger children but meta-analysis is not possible. The PK/PD relationship has had limited study but there is partial evidence for infliximab. Anti-drug antibodies are common, and are related to impaired clinical outcome with adalimumab and infliximab therapy. CONCLUSION: The current knowledge about the PK and PK/PD of TNF inhibitors in the treatment of children with JIA is limited, which prevents the introduction of TDM. Re-analysis of available data from previous trials, incorporation of pharmacologic assessments into existing biorepository studies as well as new prospective PK and PK/PD trials are required to obtain this knowledge.
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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 | 0.016 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".