A PK/PD Analysis of Circulating Biomarkers and Their Relationship to Tumor Response in Atezolizumab‐Treated non‐small Cell Lung Cancer Patients

Abstract

To assess circulating biomarkers as predictors of antitumor response to atezolizumab (anti-programmed death-ligand 1 (PD-L1), Tecentriq) serum pharmacokinetic (PK) and 95 plasma biomarkers were analyzed in 88 patients with relapsed/refractory non-small cell lung cancer (NSCLC) receiving atezolizumab i.v. q3w (10-20 mg/kg) in the PCD4989g phase I clinical trial. Following exploratory analyses, two plasma biomarkers were chosen for further study and correlation with change in tumor size (the sum of the longest diameter) was assessed in a pharmacokinetic/pharmacodynamic (PK/PD) tumor modeling framework. When longitudinal kinetics of biomarkers and tumor size were modeled, tumor shrinkage was found to significantly correlate with area under the curve (AUC), baseline factors (metastatic sites, liver metastases, and smoking status), and relative change in interleukin (IL)-18 level from baseline at day 21 (RCFB ). Although AUC was a major predictor of tumor shrinkage, the effect was estimated to dissipate with an average half-life of 80 days, whereas RCFB seemed relevant to the duration of the response.

Association Between Tumor Size Kinetics and Survival in Patients With Urothelial Carcinoma Treated With Atezolizumab: Implication for Patient Follow‐Up

“…Although drug pharmacokinetics is an obvious candidate for model extension, the impact of drug exposure on model parameters and OS seems to be limited once baseline clinical characteristics have been properly accounted for . The characterization of some aspects of the immune response, which is directly targeted by these treatments, could also be relevant but it is not yet clear which biomarkers should be included . Second, the model did not account for other events associated with disease progression, in particular the apparition of new lesions.…”

“…14,15,32,33 The characterization of some aspects of the immune response, which is directly targeted by these treatments, could also be relevant but it is not yet clear which biomarkers should be included. 34 Second, the model did not account for other events associated with disease progression, in particular the apparition of new lesions. Including these data in future models will be critical to provide a more comprehensive understanding of the response kinetics and to evaluate the benefit of model-based approach compared with empirically based metrics, such as the RECIST criterion.…”

Tumor Time‐Course Predicts Overall Survival in Non‐Small Cell Lung Cancer Patients Treated with Atezolizumab: Dependency on Follow‐Up Time

“…Details of the observed PK, biomarker, and TS data have been reported elsewhere . The median time to death was 1.4 years.…”

“…Second, model‐derived variables ( Table ) were explored on top of the baseline covariates, using a sequential PK‐IL18‐TS‐OS model. Interferon‐inducible T‐cell alpha chemoattractant (ITAC) metrics were also explored using an available PK/pharmacodynamic model for ITAC . Model‐derived variables were also evaluated stepwise, where the variable that provided the best improvement of the model fit was added to the model first and if any variable improved the model fit significantly, it was added to the model until no more variables provided statistically significant improvement.…”

“…The previously developed PK‐IL18‐TS model predicted sustained tumor suppression for high values of RCFB IL‐18,d21 . This relationship can be associated with a slower on‐treatment apparent tumor growth rate, which has been related to longer survival in patients with NSCLC treated with atezolizumab or docetaxel in phase II and III studies .…”