Population pharmacokinetics of the anti-PD-1 antibody camrelizumab in patients with multiple tumor types and model-informed dosing strategy

Abstract

Camrelizumab, a programmed cell death 1 (PD-1) inhibitor, has been approved for the treatment of patients with relapsed or refractory classical Hodgkin lymphoma, nasopharyngeal cancer and non-small cell lung cancer. The aim of this study was to perform a population pharmacokinetic (PK) analysis of camrelizumab to quantify the impact of patient characteristics and to investigate the appropriateness of a flat dose in the dosing regimen. A total of 3092 camrelizumab concentrations from 133 patients in four clinical trials with advanced melanoma, relapsed or refractory classical Hodgkin lymphoma and other solid tumor types were analyzed using nonlinear mixed effects modeling. The PKs of camrelizumab were properly described using a two-compartment model with parallel linear and nonlinear clearance. Then, covariate model building was conducted using stepwise forward addition and backward elimination. The results showed that baseline albumin had significant effects on linear clearance, while actual body weight affected intercompartmental clearance. However, their impacts were limited, and no dose adjustments were required. The final model was further evaluated by goodness-of-fit plots, bootstrap procedures, and visual predictive checks and showed satisfactory model performance. Moreover, dosing regimens of 200 mg every 2 weeks and 3 mg/kg every 2 weeks provided similar exposure distributions by model-based Monte Carlo simulation. The population analyses demonstrated that patient characteristics have no clinically meaningful impact on the PKs of camrelizumab and present evidence for no advantage of either the flat dose or weight-based dose regimen for most patients with advanced solid tumors.

Keywords: Monte Carlo method; camrelizumab; dosing regimen; population pharmacokinetics; programmed cell death 1 receptor.

Fig. 1. Model structure.

k₀, infusion rate; k₁₂, elimination rate from central compartment to peripheral compartment; k₂₁, elimination rate from peripheral compartment to central compartment; k_linear, linear elimination rate; CL_linear, clearance of linear elimination; Q, intercompartmental clearance; V₁, apparent distribution volume of central compartment; V₂, apparent distribution volume of peripheral compartment; CL_nonlinear, clearance of nonlinear elimination; C₁, concentration of central compartment; V_m, maximum elimination rate; K_m, Michaelis–Menten constant.

Fig. 2. Goodness-of-fit plots of the final population pharmacokinetic model.

The upper left plot represents the observations versus the population predictions (a). The upper right plot represents the observations versus the individual predictions (b). The lower left plot represents the conditional weighted residuals versus the population predictions (c). The lower right plot represents the conditional weighted residuals versus the time after dosing (d). The red line represents the locally weighted scatterplot smoothing line

Fig. 3. Visual predictive check.

Circles represent observed data. Lines represent the 5% (dashed), 50% (solid), and 95% (dashed) percentiles of the observed data. Shaded areas represent nonparametric 95% confidence intervals about the 5% (light blue), 50% (light red), and 95% (light blue) percentiles of the predicted concentrations

Fig. 4. Sensitivity plots comparing the effect of covariates on steady-state exposure.

a C_min; b C_max; c C_average. Vertical reference lines represent the typical steady-state exposure value of a 62-kg patient with an albumin level of 44 g/L receiving 200 mg of camrelizumab every 2 weeks. The top bars in each plot represent the 5%–95% exposure values across the entire population. The labels at each of the lower bars indicate the range of the covariate values. The length of each bar describes the impact of that particular covariate on the observed PK parameter