Pralay Mukhopadhyay

Population Pharmacokinetics of Durvalumab in Cancer Patients and Association With Longitudinal Biomarkers of Disease Status

The objectives of this analysis were to develop a population pharmacokinetics (PK) model of durvalumab, an anti‐PD‐L1 antibody, and quantify the impact of baseline and time‐varying patient/disease characteristics on PK. Pooled data from two studies (1,409 patients providing 7,407 PK samples) were analyzed with nonlinear mixed effects modeling. Durvalumab PK was best described by a two‐compartment model with both linear and nonlinear clearances. Three candidate models were evaluated: a time‐invariant clearance (CL) model, an empirical time‐varying CL model, and a semimechanistic time‐varying CL model incorporating longitudinal covariates related to disease status (tumor shrinkage and albumin). The data supported a slight decrease in durvalumab clearance with time and suggested that it may be associated with a decrease in nonspecific protein catabolic rate among cancer patients who benefit from therapy. No covariates were clinically relevant, indicating no need for dose adjustment. Simulations indicated similar overall PK exposures following weight‐based and flat‐dosing regimens.

Population Modeling of Tumor Kinetics and Overall Survival to Identify Prognostic and Predictive Biomarkers of Efficacy for Durvalumab in Patients With Urothelial Carcinoma

Durvalumab is an anti‐PD‐L1 monoclonal antibody approved for patients with locally advanced or metastatic urothelial carcinoma (UC) that has progressed after platinum‐containing chemotherapy. A population tumor kinetic model, coupled with dropout and survival models, was developed to describe longitudinal tumor size data and predict overall survival in UC patients treated with durvalumab (NCT01693562) and to identify prognostic and predictive biomarkers of clinical outcomes. Model‐based covariate analysis identified liver metastasis as the most influential factor for tumor growth and immune‐cell PD‐L1 expression and baseline tumor burden as predictive factors for tumor killing. Tumor or immune‐cell PD‐L1 expression, liver metastasis, baseline hemoglobin, and albumin levels were identified as significant covariates for overall survival. These model simulations provided further insights into the impact of PD‐L1 cutoff values on treatment outcomes. The modeling framework can be a useful tool to guide patient selection and enrichment strategies for immunotherapies across various cancer indications.