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Background and Objective: Cetuximab, an anti-epidermal growth factor receptor (EGFR) monoclonal immunoglobulin (Ig)G1 antibody, has been approved for the treatment of metastatic colorectal cancer (mCRC). The influence of target-antigen on cetuximab pharmacokinetics has never been investigated using target-mediated drug disposition (TMDD) modelling. This study aimed to investigate the relationship between cetuximab concentrations, target kinetics and progression-free survival (PFS)., Methods: In this ancillary study (NCT00559741), 91 patients with mCRC treated with cetuximab were assessed. Influence of target levels on cetuximab pharmacokinetics was described using TMDD modelling. The relationship between cetuximab concentrations, target kinetics and time-to-progression (TTP) was described using a joint pharmacokinetic-TTP model, where unbound target levels were assumed to influence hazard of progression by an E max model. Mitigation strategies of concentration-response relationship, i.e., time-varying endogenous clearance and mutual influences of clearance and time-to-progression were investigated., Results: Cetuximab concentration-time data were satisfactorily described using the TMDD model with quasi-steady-state approximation and time-varying endogenous clearance. Estimated target parameters were baseline target levels (R 0 = 43 nM), and complex elimination rate constant (k int = 0.95 day -1 ). Estimated time-varying clearance parameters were time-invariant component of CL (CL 0 = 0.38 L/day -1 ), time-variant component of CL (CL 1 = 0.058 L/day -1 ) and first-order rate of CL 1 decreasing over time (k des = 0.049 day -1 ). Part of concentration-TTP was TTP-driven, where clearance and TTP were inversely correlated. In addition, increased target occupancy was associated with increased TTP., Conclusion: This is the first study describing the complex relationship between cetuximab target-mediated pharmacokinetics and PFS in mCRC patients using a joint PK-time-to-progression model. Further studies are needed to provide a more in-depth description of this relationship., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Background and Objectives: Rituximab is an anti-CD20 monoclonal antibody approved in several diseases, including chronic lymphocytic leukemia (CLL), diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), rheumatoid arthritis (RA), and anti-neutrophil cytoplasmic antibody-associated vasculitis (AAV). The influence of underlying disease on rituximab pharmacokinetics has never been investigated for several cancer and non-cancer diseases simultaneously. This study aimed at assessing this influence using an integrated semi-mechanistic model accounting for target-mediated elimination of rituximab., Methods: Rituximab concentration-time data from five studies previously published in patients with CLL, DLBCL, FL, RA, and AAV were described using a two-compartment model with irreversible binding of rituximab to its target antigen. Both underlying disease and target antigen measurements were assessed as covariates., Results: Central volume of distribution was [95% confidence interval] 1.7-fold [1.6-1.9] higher in DLBCL than in RA, FL, and CLL, and it was 1.8-fold [1.6-2.1] higher in RA, FL, and CLL than in AAV. First-order elimination rate constants were 1.8-fold [1.7-2.0] and 1.3-fold [1.2-1.5] higher in RA, DLBCL, and FL than in CLL and AAV, respectively. Baseline latent antigen level (L 0 ) was 54-fold [30-94], 20-fold [11-36], and 29-fold [14-64] higher in CLL, DLBCL, and FL, respectively, than in RA and AAV. In lymphoma, L 0 increased with baseline total metabolic tumor volume (p = 6.10 -7 ). In CLL, the second-order target-mediated elimination rate constant (k deg ) increased with baseline CD20 count on circulating B cells (CD20 cir , p = 0.0081)., Conclusions: Our results show for the first time that rituximab pharmacokinetics is strongly influenced by underlying disease and disease activity. Notably, neoplasms are associated with higher antigen amounts that result in decreased exposure to rituximab compared to inflammatory diseases. Our model might be used to estimate unbound target amounts in upcoming studies., (© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Background and Objectives: The pharmacokinetics of infliximab are highly variable and influence clinical response in chronic inflammatory diseases. The goal of this study was to build a Bayesian model allowing predictions of upcoming infliximab concentrations and dosing regimen adjustment, using only one concentration measurement and information regarding the last infliximab infusion., Methods: This retrospective study was based on data from 218 patients treated with infliximab in Tours University Hospital who were randomly assigned to learning (two-thirds) or validation (one-third) data subsets. One-compartment pharmacokinetic and time since last dose (TLD) models were built and compared using learning and validation subsets. From these models, Bayesian pharmacokinetic and TLD models using one concentration measurement (1C-PK and 1C-TLD) were designed. The predictive performances of the 1C-TLD model were tested on two external validation cohorts., Results: Pharmacokinetic and TLD models described the data satisfactorily and provided accurate parameter estimations. Comparable predictions of infliximab concentrations were obtained from pharmacokinetic versus TLD models, as well as from Bayesian 1C-PK versus 1C-TLD models. The 1C-TLD model showed satisfactory prediction of future infliximab concentrations and provided satisfactory predictions of infliximab steady-state concentration for up to three upcoming visits after a blood sample., Conclusions: Accurate individual concentration predictions can be obtained using a single infliximab concentration measurement and information regarding only the last infusion. The 1C-TLD model may help to optimize the dosing regimen of infliximab in routine therapeutic drug monitoring.