Model-Informed Drug Development of Subcutaneous Nivolumab: Comparison of Pharmacokinetic Analysis Methodologies Using...

A subcutaneous formulation of nivolumab was evaluated in the phase III study CheckMate 67T (NCT04810078). The co-primary pharmacokinetic exposure endpoints, time-averaged serum concentration over the first 28 days (C_avgd28), and steady-state trough concentration (C_minss) were determined through population pharmacokinetic analysis as compared to conventional non-compartmental analysis (NCA). We proposed a model-based approach to determine subcutaneous and intravenous nivolumab exposures in CheckMate 67T, leveraging extensive prior pharmacokinetic data across various tumor types. The robustness of this model-informed drug development (MIDD) approach was assessed via clinical trial simulations. Concentration-time profiles in randomly sampled patients with renal cell carcinoma receiving second-line systemic therapy were simulated for subcutaneous/intravenous nivolumab. Population pharmacokinetic parameters, sampled from the joint parameter uncertainty distribution, were applied in simulations based on the CheckMate 67T design. Two population pharmacokinetic approaches-the PRIOR subroutine ($PRIOR) in NONMEM and a pooled analysis with historical nivolumab pharmacokinetic data-were used to analyze the simulated data. Results were compared to NCA using intensive and conventional sampling schemes. Analyses showed that model-based analysis provided more accurate area under the curve estimates than NCA. Model-predicted exposure measures, including C_avgd28, maximum serum concentration after the first dose, and minimum serum concentration at day 28, were also consistent across both population pharmacokinetic approaches, with minimal differences in geometric means. In conclusion, both the $PRIOR and pooled population pharmacokinetic methods yielded more accurate results compared to conventional NCA. The MIDD approach was validated as a robust and feasible method to support non-inferiority assessment based on clinical trial simulations.