Real-World Infliximab Pharmacokinetic Study Informs an Electronic Health Record-Embedded Dashboard to Guide Precision Dosing in Children with Crohn's Disease.

Abstract

Standard-of-care infliximab dosing regimens were developed prior to the routine use of therapeutic drug monitoring and identification of target concentrations. Not surprisingly, subtherapeutic infliximab concentrations in pediatric Crohn's disease (CD) are common. The primary aim was to conduct a real-world pharmacokinetic (PK) evaluation to discover blood biomarkers of rapid clearance, identify exposure targets, and a secondary aim to translate PK modeling to the clinic. In a multicenter observational study, 671 peak and trough infliximab concentrations from 78 patients with CD were analyzed with a drug-tolerant assay (Esoterix; LabCorp, Calabasas, CA). Individual area under the curve (AUC) estimates were generated as a measure of drug exposure over time. Population PK modeling (nonlinear mixed-effect modeling) identified serum albumin, antibody to infliximab, erythrocyte sedimentation rate (ESR), and neutrophil CD64 as biomarkers for drug clearance. Week 14 and week 52 biochemical remitters (fecal calprotectin<250µg/g) had higher infliximab exposure (AUC) throughout induction. The optimal infliximab AUC target during induction for week 14 biochemical remission was 79,348µg*h/mL (area under the receiver operating characteristic curve (AUROC) 0.77, [0.63-0.90], 85.7% sensitive, and 64.3% specific) with those exceeding the AUC target more likely to achieve a surgery-free week 52 biochemical remission (OR 4.3, [1.2-14.6]). Pretreatment predictors for subtherapeutic week 14 AUC included neutrophil CD64>6 (OR 4.5, [1.4-17.8]), ESR>30mm/h (OR 3.8, [1.4-11]), age<10years old (OR 4.2, [1.2-20]), and weight<30kg (OR 6.6, [2.1-25]). We created a decision-support PK dashboard with an iterative process and embedded the modeling program within the electronic health record. Model-informed precision dosing guided by real-world PKs is now available at the bedside in real-time.