Personalizing IL-23 Inhibitor Therapy in IBD: Current Evidence and Future Directions in Therapeutic Drug Monitoring and Dose Optimization
Bibliographic record
Abstract
Interleukin-23 (IL-23) inhibitors have rapidly become an essential component of the therapeutic armamentarium for inflammatory bowel disease (IBD). Risankizumab, mirikizumab, and guselkumab share broadly similar pharmacokinetic and pharmacodynamic properties, including linear clearance, long half-lives, and low immunogenicity. While therapeutic drug monitoring (TDM) is well established in the use of anti-TNF agents, its role in IL-23 inhibitors remains undefined. Emerging evidence, mostly for risankizumab, demonstrates dose-response relationships, suggests potential maintenance thresholds, and outlines possible dose optimization strategies. However, this preliminary data is predominantly retrospective, often single-center, and involves a small number of patients. Until more robust evidence supporting the efficacy of TDM and dose optimization emerges, routine use of this clinical practice in IBD remains investigational.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".