DOP033 Severely active ulcerative colitis is associated with high baseline infliximab clearance, reduced serum half-life and worse endoscopic outcomes
Bibliographic record
Abstract
Background: Therapeutic drug monitoring is a useful tool to optimize infliximab (IFX) therapy in clinical practice. The relationship between IFX concentration and change from baseline in endoscopic disease activity during induction and maintenance therapy is unknown. Methods: Data from the randomized controlled ACT-1 and -2 trials encompassing 484 patients with ulcerative colitis treated with IFX therapy were analyzed. A two-compartment population pharmacokinetic model was used to estimate IFX clearance (CL) at baseline. The Mayo endoscopic score (MES; range 0–3, with 3 representing most severe inflammation) was available at Week (W) 0, W8 and W30. A test for a linear trend between mean baseline IFX CL and MES (0–3) at W8 was performed. A concentration-effect curve was established by sorting all patients from low to high IFX concentration at specific time points with corresponding change in MES from W0 to W8 (ΔMES8) and from W0 to W30 (ΔMES30). Receiver operating curve (ROC) analysis was performed to identify IFX concentration cut-points with combined maximal sensitivity and specificity that corresponded to a MES of 0–1 or 0. Patients with missing data at W8 and/or W30 were considered treatment failures. Results: A linear relationship was observed between baseline IFX CL and MES at W8 (p<0.001). Concurrently, approximate median baseline IFX half-life was significantly shorter in patients with W8 MES ≥2 vs W8 MES <2 (12 vs 15 days, respectively, p<0.001). In patients with higher IFX exposure during induction and maintenance therapy, ΔMES8 and ΔMES30 was greater (Fig. 1A and 1B, respectively). Figure 1. IFX concentration-effect curve. Each point is the median of ≥40 IFX concentrations at specified time-points, stratified in ascending order with corresponding change (Δ) in MES (with standard deviation) from (A) Week 0 to 8 and (B) Week 0 to 30. IFX cut-points of 26 ug/mL at W2, 11 ug/mL at W6 and 35 ug/mL at W8 correlated with W8 MES <2, and 5.0 ug/mL at W14 and 2.3 ug/mL at W30 correlated with W30 MES <2 (Table 1). Table 1. Association between IFX cut-points and endoscopic outcomes Conclusions: Ulcerative colitis patients with high baseline IFX CL may benefit from accelerated dosing during induction treatment to achieve drug exposure above the cut-points associated with short- and long-term mucosal healing.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".