Time-to-event modelling of effect of codrituzumab on overall survival in patients with hepatocellular carcinoma

Abstract

Aims: Codrituzumab (GC33) is a recombinant, humanized mAb that binds to glypican-3 (GPC3), an oncofetal protein highly expressed in hepatocellular carcinoma (HCC). This investigation aimed to identify clinically relevant factors that may affect the overall survival (OS) in HCC patients treated with codrituzumab and to quantitatively annotate their effects.

Methods: Codrituzumab exposure was estimated by a population pharmacokinetics model with a nonlinear elimination pathway. Analysis of OS was performed using a time-to-event model in 181 patients with advanced HCC. The model was tested with the addition of various covariates, including levels of immune biomarkers, such as CD16 (measured in terms of molecules of equivalent soluble fluorophore; CD16_MESF ) and CD4, codrituzumab exposure and potential prognostic biomarkers of HCC such as baseline tumour size and soluble GPC3.

Results: The time-to-event model estimated a prolonged OS (>3 months) in patients with codrituzumab exposure of ≥230 μg ml^-1 and high CD16_MESF level (>5.26 × 10⁵ MESF at least). The Weibull model was selected as the base hazard model. The baseline tumour size was included in the hazard model as a parameter independent of the drug effect. A logistic model was applied to explain the effects of drug exposure and CD16_MESF level.

Conclusions: The final model indicates that adequate drug exposure plus a favourable immune environment are associated with prolonged OS. This quantitative model should be further validated with emerging data so as to guide study design in future clinical trials.

Keywords: GC33/codrituzumab; GPC3; hepatocellular carcinoma; overall survival; pharmacokinetics; time-to-event model.

Figure 1

Visual predictive check plots of observed OS and survival predictions of the base models. The black line shows the observed Kaplan–Meier curve; the blue dotted line and shaded band show the simulated median survival curve and the 90% CI from 200 simulations for each model. Plots were prepared for three base models: exponential model (A), Gompertz model (B) and Weibull model (C)

Figure 2

Visual predictive check plots of OS and survival predictions of the final model. The black line shows the observed Kaplan–Meier curve, the blue line and shaded band show the simulated median survival curve and the 90% CI from 200 simulations. Visual predictive check plots were prepared for all patients (A), subgroups stratified by μ of C_trough,3 and μ of CD16_MESF (B), and subgroups stratified by median value of SLD (C)