Infliximab in paediatric inflammatory bowel disease: External evaluation of population pharmacokinetic models
Bibliographic record
Abstract
AIMS: Use of infliximab (IFX) has improved outcomes in children with inflammatory bowel disease (IBD). However, a proportion of patients does not respond to IFX or loses response over time. Population pharmacokinetic (PopPK) modelling is a promising approach for IFX dose optimization, but with the increasing number of PopPK models in literature, model evaluation is essential. The aims of this study are: (i) to validate the predictive performance of existing IFX PopPK models using a cohort of children with IBD; and (ii) to perform a Bayesian estimation of the most suitable model to predict the next IFX concentrations. METHODS: PubMed was searched for IFX PopPK models in children. Selected models were rebuilt and analysed using R. Model performance was assessed through goodness-of-fit-plots, residuals against time, prediction error and prediction-corrected visual predictive checks. The validation cohort consisted of 73 children with IBD who were treated with IFX in our centre between 2017 and 2023 (340 IFX measurements). RESULTS: We identified 9 PopPK models. Model bias for individual predicted values ranged from -9.29% to 8.01% compared to bias for population predicted values. The model by Vande Casteele et al. demonstrated superior performance (individual predicted bias 2.13, population predicted bias -6.11); upon Bayesian estimation, it predicted induction trough levels with median error of 12.95% but had a median error of -69% predicting maintenance concentrations. CONCLUSION: The model by Vande Casteele et al. displayed superior performance in initial evaluations but had a high error in estimating next IFX levels and can only be used in practice to predict induction levels.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | high |
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 | 0.014 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".