P-103 Refinement of Population Pharmacokinetic Model of Certolizumab Pegol in Crohnʼs Disease Patients to Account for Time Varying Nature of Covariates
Bibliographic record
Abstract
A population pharmacokinetic (pop PK) analysis for certolizumab pegol (CZP) was developed in patients with Crohn's disease (CD),1 which consisted of a baseline concentration, first-order absorption, and one-compartment disposition. Covariates that influence the disposition of CZP were identified, but only baseline values were used. We subsequently refined the existing model so that time-varying demographic and pathophysiologic characteristics on the estimated variability were taken into account. Data collected from 2157 patients with CD in 9 studies were analyzed using nonlinear mixed effects modeling (NONMEM) software. The CZP concentration-time data were described by a one-compartment pop PK model with first-order absorption and one-compartment disposition with linear, time-dependent elimination. Pop PK estimates, based on the final covariate model, were absorption rate (1.83/day), clearance (CL; 0.527 L/day), and apparent volume of distribution (V; 8.33 L). CZP exhibited interindividual variability for CL of 19.6%. Anti-CZP antibodies were included as a continuous, time-varying covariate on CZP CL in the structural model. CZP CL increased from 142% to 174% for a typical patient with CD over the 5th to the 95th percentile of the anti-CZP antibody range (respectively, 2.5–212.3 units/mL). Covariate analysis showed that time-varying albumin concentration, C-reactive protein concentration, and body weight influenced CZP CL. Female gender was associated with a modest increase in CZP CL. Time-varying body weight influenced CZP V. A pop PK model that takes into account the time-varying nature of patients' covariates reduced the between-patient variability on CZP CL from 27.5%1 to 19.6%, extending learnings from the previous model that included baseline values. By taking into account anti-CZP antibodies as a time-varying and continuous variable in the updated model, future analyses can assess the relative influence of anti-CZP antibodies on CZP CL. This is an important improvement, as inflammatory bowel disease patient covariates are often time-dependent, making this model more reflective of patient drug exposure with sustained treatment.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".