II-53 Sarah Lobet

Target-mediated pharmacokinetics of cetuximab: target occupancy influences progression-free survival

Sarah LOBET (1), Gilles PAINTAUD (2, 3), Nicolas AZZOPARDI (4), Christophe PASSOT (5), Morgane CAULET (6), Romain CHAUTARD (1, 6), Celine VIGNAULT-DESVIGNES (2,3), Olivier CAPITAIN (5), David TOUGERON (7), Michelle BOISDRON-CELLE (5), Thierry LECOMTE (1, 6), David TERNANT (2,3)

(1) Tours University, Inserm UMR 1069, Nutrition Croissance et Cancer (N2C), Tours, France (2) Tours University, EA4245 Transplantation, Immunologie, Inflammation, Tours, France. (3) Pharmacology Department, Tours University Hospital, Tours, France. (4) Tours University, EA7501 GICC, Team PATCH, Tours, France, Tours, France. (5) Oncopharmacology - Pharmacogenetics Department INSERM U892, Institut de Cancérologie de l'Ouest site Paul Papin, Angers, France. (6) Gastroenterology and Digestive oncology Department, Tours University Hospital, Tours, France. (7) Gastroenterology Department, Poitiers University Hospital, Poitiers, France.

Introduction: Cetuximab (ERBITUX®), an anti-EGFR monoclonal IgG1 antibody, has been approved for the treatment of metastatic colorectal cancer (mCRC). The pharmacokinetics of cetuximab has a nonlinear elimination shape, that could be dependent on the turnover of EGFR (1). Target-mediated drug disposition (TMDD) models (2) have been commonly used to describe target-mediated pharmacokinetics (TMPK) of monoclonal antibodies. To our knowledge, TMDD models have never been used to describe cetuximab TMPK. Hence, the kinetics of EGFR during cetuximab treatment was never assessed.

Objectives: This study aimed at investigating TMPK of cetuximab, the relationship between the target occupancy (TO) and progression free survival (PFS), and the relevance of dosing alteration.

Methods: In this retrospective ancillary phase II study (3,4), ninety-one patients with mCRC received cetuximab as an infusion with a loading dose of 400 mg/m2 followed by weekly infusions of 250 mg/m2. A population approach was used for the analysis of concentration-time data by using the non-linear mixed effect modeling with Monolix software (5). Cetuximab was described under the quasi-steady state (QSS) approximation (6). The association between TO and PFS was investigated by using Kaplan-Meier methods and Cox proportional-hazards models (7,8). The final TMDD model were used to simulate several dosing regimens 250mg/m² once weekly (QW) and 500mg/m² every two weeks (Q2W) and to compute corresponding TO distributions. For each dosing strategy, 1000 patients were simulated by Monte Carlos simulation using Simulx software (9).

Results: Cetuximab concentration-time data were satisfactorily described by the TMDD QSS model. Estimates of parameters describing the kinetics of free cetuximab were central (V1 = 2.7 L) and peripheral (V2 = 4.6 L) volumes of distribution, and systemic (CL = 0.37 L/d) and intercompartment (Q = 1.0 L) clearances. Target turnover parameter estimates were EGFR concentration without treatment (R0 = 2.4 nM) and first-order elimination rate (kdeg = 15.1 day-1). Target-mediated kinetic parameter estimates were steady-state dissociation rate (KSS = 0.61 nM) and first-order elimination rate of complexes (kint = 4.0 day-1). Median PFS of the 91 patients was 6,4 months (95% CI : 4,1-7,4 months). On day 7, PFS was higher in patients with EGFR concentration below to its median value (7,5 months) than others (4 months) (p=0,019). At steady state, PFS was higher in patients with EGFR concentration below to its median value (9 months) than others (4 months) (p=0,0012). The median target occupancy reached at 250mg/m² QW and 500mg/m² Q2W was similar (99.96% vs 99.98%).

Conclusions: This is the first study describing TMPK of cetuximab using a two-compartment TMDD model with QSS approximation. Our model allowed quantifying EGFR kinetics over time, which was associated to PFS. Our simulations suggested that a 500mg/m² Q2W regimen could be used instead of 250mg/m² QW in some patients. This dosing regimen would lead to the same efficacy with fewer constraints in terms of administration for patients.

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  7. Therneau T (2020). _A Package for Survival Analysis in R_. R package version 3.2-7. https://CRAN.R-project.org/package=survival.
  8. Alboukadel Kassambara, Marcin Kosinski and Przemyslaw Biecek (2021). survminer: Drawing Survival Curves using « ggplot2 ». R package version 0.4.9. https://CRAN.R-project.org/package=survminer.
  9. Simulx version 2020R1. Antony, France: Lixoft SAS, 2020. http://lixoft.com/products/simulx/.

Reference: PAGE 29 (2021) Abstr 9695 [www.page-meeting.org/?abstract=9695]

Poster: Drug/Disease Modelling - Oncology