AI Article Synopsis

  • * This study tested the accuracy of existing population pharmacokinetic models for predicting infliximab levels in 105 IBD patients, revealing that predictions are less reliable for patients with anti-drug antibodies (ADA).
  • * The best models were able to suggest necessary dose increases for low drug levels in 88% of cases, indicating that individualized dosing might help maintain effective drug concentrations, although challenges remain for patients developing ADA.

Article Abstract

Infliximab is approved for treatment of various chronic inflammatory diseases including inflammatory bowel disease (IBD). However, high variability in infliximab trough levels has been associated with diverse response rates. Model-informed precision dosing (MIPD) with population pharmacokinetic models could help to individualize infliximab dosing regimens and improve therapy. The aim of this study was to evaluate the predictive performance of published infliximab population pharmacokinetic models for IBD patients with an external data set. The data set consisted of 105 IBD patients with 336 infliximab concentrations. Literature review identified 12 published models eligible for external evaluation. Model performance was evaluated with goodness-of-fit plots, prediction- and variability-corrected visual predictive checks (pvcVPCs) and quantitative measures. For anti-drug antibody (ADA)-negative patients, model accuracy decreased for predictions > 6 months, while bias did not increase. In general, predictions for patients developing ADA were less accurate for all models investigated. Two models with the highest classification accuracy identified necessary dose escalations (for trough concentrations < 5 µg/mL) in 88% of cases. In summary, population pharmacokinetic modeling can be used to individualize infliximab dosing and thereby help to prevent infliximab trough concentrations dropping below the target trough concentration. However, predictions of infliximab concentrations for patients developing ADA remain challenging.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8468301PMC
http://dx.doi.org/10.3390/pharmaceutics13091368DOI Listing

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