Quantitative relationship between infliximab exposure and inhibition of C‐reactive protein synthesis to support inflammatory bowel disease management

Aim Quantitative and kinetic insights into the drug exposure‐disease response relationship might enhance our knowledge on loss of response and support more effective monitoring of inflammatory activity by biomarkers in patients with inflammatory bowel disease (IBD) treated with infliximab (IFX). This study aimed to derive recommendations for dose adjustment and treatment optimisation based on mechanistic characterisation of the relationship between IFX serum concentration and C‐reactive protein (CRP) concentration. Methods Data from an investigator‐initiated trial included 121 patients with IBD during IFX maintenance treatment. Serum concentrations of IFX, antidrug antibodies (ADA), CRP, and disease‐related covariates were determined at the mid‐term and end of a dosing interval. Data were analysed using a pharmacometric nonlinear mixed‐effects modelling approach. An IFX exposure‐CRP model was generated and applied to evaluate dosing regimens to achieve CRP remission. Results The generated quantitative model showed that IFX has the potential to inhibit up to 72% (9% relative standard error [RSE]) of CRP synthesis in a patient. IFX concentration leading to 90% of the maximum CRP synthesis inhibition was 18.4 μg/mL (43% RSE). Presence of ADA was the most influential factor on IFX exposure. With standard dosing strategy, ≥55% of ADA+ patients experienced CRP nonremission. Shortening the dosing interval and co‐therapy with immunomodulators were found to be the most beneficial strategies to maintain CRP remission. Conclusions With the generated model we could for the first time establish a robust relationship between IFX exposure and CRP synthesis inhibition, which could be utilised for treatment optimisation in IBD patients

Semimechanistic Clearance Models of Oncology Biotherapeutics and Impact of Study Design: Cetuximab as a Case Study

This study aimed to explore the currently competing and new semimechanistic clearance models for monoclonal antibodies and the impact of clearance model misspecification on exposure metrics under different study designs exemplified for cetuximab. Six clearance models were investigated under four different study designs (sampling density and single/multiple-dose levels) using a rich data set from two cetuximab clinical trials (226 patients with metastatic colorectal cancer) and using the nonlinear mixed-effects modeling approach. A two-compartment model with parallel Michaelis-Menten and time-decreasing linear clearance adequately described the data, the latter being related to post-treatment response. With respect to bias in exposure metrics, the simplified time-varying linear clearance (CL) model was the best alternative. Time-variance of the linear CL component should be considered for biotherapeutics if response impacts pharmacokinetics. Rich sampling at steady-state was crucial for unbiased estimation of Michaelis-Menten elimination in case of the reference (parallel Michaelis-Menten and time-varying linear CL) model

Model-Based Characterization of the Bidirectional Interaction Between Pharmacokinetics and Tumor Growth Dynamics in Patients with Metastatic Merkel Cell Carcinoma Treated with Avelumab

Purpose: Empirical time-varying clearance models have been reported for several immune checkpoint inhibitors, including avelumab (anti-programmed death ligand 1). To investigate the exposure response relationship for avelumab, we explored semimechanistic pharmacokinetic (PK)-tumor growth dynamics (TGD) models. Patients and Methods: Plasma PK data were pooled from three phase I and II trials (JAVELIN Merkel 200, JAVELIN Solid Tumor, and JAVELIN Solid Tumor JPN); tumor size (TS) data were collected from patients with metastatic Merkel cell carcinoma (mMCC) enrolled in JAVELIN Merkel 200. A PK model was developed first, followed by TGD modeling to investigate interactions between avelumab exposure and TGD. A PK-TGD feedback loop was evaluated with simultaneous fitting of the PK and TGD models. Results: In total, 1,835 PK observations and 338 TS observations were collected from 147 patients. In the final PK-TGD model, which included the bidirectional relationship between PK and TGD, avelumab PK was described by a two-compartment model with a positive association between clearance and longitudinal TS, with no additional empirical time-varying clearance identified. TGD was described by first-order tumor growth/shrinkage rates, with the tumor shrinkage rate decreasing exponentially over time; the exponential time-decay constant decreased with increasing drug concentration, representing the treatment effect through tumor shrinkage inhibition. Conclusions: We developed a TGD model that mechanistically captures the prevention of loss of antitumor immunity (i.e., T-cell suppression in the tumor microenvironment) by avelumab, and a bidirectional interaction between PK and TGD in patients with mMCC treated with avelumab, thus mechanistically describing previously reported time variance of avelumab elimination