Pharmacokinetic model selection for infliximab based on inflammatory bowel disease phenotype and severity: Toward...

Ulcerative colitis (UC) and Crohn's disease (CD) differ in intestinal location and depth of involvement. Patients with UC who have or experienced a flare-up according to Truelove-Witts criteria (ACUS), as well as those with fistulizing Crohn's disease (FIST), may represent difficult-to-treat populations. This study investigates the phenotype- and severity-driven selection of infliximab (IFX) population pharmacokinetic models to optimize predictive performance and support precision dosing in inflammatory bowel disease (IBD) care. A retrospective analysis was conducted at University Hospital Medical Centre "Zvezdara". Twenty published IFX models were evaluated across five datasets (overall, ASUC, CD, FIST, UC), primarily using trough concentrations obtained via routine therapeutic drug monitoring. Predictive accuracy was assessed using median prediction error (MDPE) and median absolute prediction error (MAPE). Bayesian forecasts were evaluated using median individual prediction error (MDIPE) and its absolute value (MAIPE). Normalized prediction distribution errors (NPDE) and visual predictive check (VPC) were used for simulation-based diagnostics. Moreover, probability of probability of target-attainment (PTA%) per model and subgroup was calculated. No model met all predefined prediction-based performance criteria. In a priori analysis, the Matsuoka model showed the satisfactory overall performance, while the Xu model was most suitable for CD patients. In a posteriori analysis, the Ternant 2008 model had the well overall accuracy, with other models (Dreesen 2021 for ASUC, Matsuoka for CD, Brandse 2016 for UC) performing satisfactory in respective subgroups. Simulation NPDE diagnostics identified the Aubourg model as suitable for the overall dataset, while VPC results mainly supported the a priori analysis conclusions. These findings highlight the relevance of disease phenotype and severity in selecting IFX pharmacokinetic models. Rather than relying on a one-size-fits-all approach, tailoring model choice to patient subgroups enhances predictive performance. This supports a more individualized, model-informed precision dosing (MIPD) strategy for optimizing IFX dosing in IBD.