Prediction of Individual Serum Infliximab Concentrations in Inflammatory Bowel Disease by a Bayesian Dashboard System

Abstract

Despite a robust exposure-response relationship of infliximab in inflammatory bowel disease (IBD), attempts to adjust dosing to individually predicted serum concentrations of infliximab (SICs) are lacking. Compared with labor-intensive conventional software for pharmacokinetic (PK) modeling (eg, NONMEM) dashboards are easy-to-use programs incorporating complex Bayesian statistics to determine individual pharmacokinetics. We evaluated various infliximab detection assays and the number of samples needed to precisely forecast individual SICs using a Bayesian dashboard. We assessed long-term infliximab retention in patients being dosed concordantly versus discordantly with Bayesian dashboard recommendations. Three hundred eighty-two serum samples from 117 adult IBD patients on infliximab maintenance therapy were analyzed by 3 commercially available assays. Data from each assay was modeled using NONMEM and a Bayesian dashboard. PK parameter precision and residual variability were assessed. Forecast concentrations from both systems were compared with observed concentrations. Infliximab retention was assessed by prediction for dose intensification via Bayesian dashboard versus real-life practice. Forecast precision of SICs varied between detection assays. At least 3 SICs from a reliable assay are needed for an accurate forecast. The Bayesian dashboard performed similarly to NONMEM to predict SICs. Patients dosed concordantly with Bayesian dashboard recommendations had a significantly longer median drug survival than those dosed discordantly (51.5 versus 4.6 months, P < .0001). The Bayesian dashboard helps to assess the diagnostic performance of infliximab detection assays. Three, not single, SICs provide sufficient information for individualized dose adjustment when incorporated into the Bayesian dashboard. Treatment adjusted to forecasted SICs is associated with longer drug retention of infliximab.

Using Real‐World Data to Guide Ustekinumab Dosing Strategies for Psoriasis: A Prospective Pharmacokinetic‐Pharmacodynamic Study

“…[4][5][6] Broadly speaking, TDM strategies advocate populationbased target trough concentrations for dose adjustment, using a reactive rather than proactive approach. [7][8][9] Integration of pharmacodynamic (PD) outcomes to yield PK/PD models is rare, but these could feasibly be included in Bayesian prediction algorithms to predict and adjust dosing strategy on an individual level. 10,11 To date, investigation of the effectiveness and utility of TDM has largely been confined to tumor necrosis factor inhibitors, the first of many cytokine-targeted biologic therapies in immune-mediated inflammatory diseases.…”

“…Broadly speaking, TDM strategies advocate population‐based target trough concentrations for dose adjustment, using a reactive rather than proactive approach . Integration of pharmacodynamic (PD) outcomes to yield PK/PD models is rare, but these could feasibly be included in Bayesian prediction algorithms to predict and adjust dosing strategy on an individual level .…”

Outcomes and Strategies to Support a Treat-to-target Approach in Inflammatory Bowel Disease: A Systematic Review

“…We are getting closer to the point where, using dashboard systems, the pharmacokinetics and early drug clearance can be predicted and could be integrated in the T2T approach. 100 A strength of the evidence presented in this review is the availability-though very limited-of real-world data. It will be critical that T2T strategies are adequately tested in pragmatic studies, as adopting treatment algorithms in practice can be challenging.…”

Individualized Dosing of Therapeutic Monoclonal Antibodies—a Changing Treatment Paradigm?

“…To facilitate a broader application, the American Gastroenterology Association published a paper with recommendations for reactive TDM (62). Although not all studies used the same bioanalytical method to measure drug concentrations, independent reviewers from the society have indicated that results from various assays appear to be generally in good agreement (62,63) although a retrospective study showed that some assays do not have good performance (54).…”

“…With a PK model, incorporated patient factors, and a Bayesian approach, the dashboard system utilizes the concentration data from the previous dose and calculates the exact dosage a patient should receive and the exact date this dosage should be given to maintain a certain drug concentration. To make this prediction as accurate as possible, frequent blood sampling throughout treatment is desirable, although not necessary (54,55). However, care should be taken to ensure that the underlying structural model in the dashboard is appropriate.…”