Exposure‐response relationship of certolizumab pegol induction and maintenance therapy in patients with Crohn's disease
Bibliographic record
Abstract
BACKGROUND: Therapeutic drug monitoring may optimize therapy for Crohn's disease (CD). AIM: To use a population pharmacokinetic model that accounts for the time-varying nature of covariates to simulate certolizumab pegol (CZP) concentrations to evaluate the exposure-response relationship for CZP in Crohn's disease. METHODS: Adults (N = 2157) with Crohn's disease were treated with CZP in nine clinical trials. Simulated CZP concentrations were compared to outcomes at weeks 6 and 26, including Crohn's disease activity index (CDAI) response (decrease from baseline ≥ 100 points), remission (CDAI ≤ 150), C-reactive protein (CRP) ≤ 5 mg/L, faecal calprotectin (FC) ≤ 250 μg/g, and a composite endpoint of CDAI ≤ 150 and FC ≤ 250 μg/g. Multivariable analyses identified covariates associated with outcomes and receiver operating characteristic analyses determined optimal CZP concentrations. RESULTS: CZP concentrations at weeks 2, 4 and 6 were higher in patients with clinical response, remission, CRP ≤ 5 mg/L or FC ≤ 250 μg/g at week 6 than without. In multivariable analyses, higher CZP concentrations at week 6 were associated with the composite outcome at weeks 6 and 26 (P < .001). Although the exposure-response relationship varied among patients, approximate CZP concentrations of at least 36.1 μg/mL (positive predictive value [PPV] 22.8% and negative predictive value [NPV] 92.7%) and at least 14.8 μg/mL (PPV 28.0% and NPV 90.4%) at weeks 6 and 12 were associated with weeks 6 and 26 outcomes. CONCLUSIONS: An exposure-response relationship was apparent for CZP in Crohn's disease and achieving higher CZP concentrations may increase the likelihood of attaining efficacy outcomes, but this remains to be evaluated prospectively.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".