Population pharmacokinetics and exposure-response analyses of trastuzumab in patients with advanced gastric or gastroesophageal junction cancer.

PURPOSE: The aim of this study was to characterize trastuzumab population pharmacokinetics (PKs) in patients with human epidermal growth factor receptor 2-positive advanced gastric or gastroesophageal junction cancer and the relationship of trastuzumab PK with patient response. METHODS: A nonlinear mixed effects PK model was built using data from the ToGA study. Patients were randomized to intravenous trastuzumab plus chemotherapy or chemotherapy alone. The influence of demographic, laboratory, and disease characteristics on PK parameters was assessed. An exploratory exposure-response analysis compared various PK parameters at steady state with best overall tumor response and overall survival (OS). RESULTS: Trastuzumab PK was best described by a two-compartment model with parallel linear and nonlinear (Michaelis-Menten) elimination from the central compartment. Total clearance (and half-life) of trastuzumab was concentration-dependent. Body weight, prior gastrectomy, and serum albumin had the greatest influence on trastuzumab PK; increasing weight and decreasing albumin levels were associated with increased clearance, while prior gastrectomy correlated with decreased clearance. Median values for AUC, Cmax, and Cmin were lower in patients with progressive disease (PD) than other response categories, although the 1.5 interquartile ranges overlapped. Patients with the lowest Cmin had the highest PD rate and a shorter OS. CONCLUSIONS: In the advanced gastric cancer population, trastuzumab PK was best described by a two-compartment model with parallel linear and nonlinear elimination. Predicted PK exposure was lower than previously reported for breast cancer. Patients with the lowest Cmin had a shorter OS and the highest PD rate, but a distinct correlation was not observed for tumor response.

A model-based approach to predict longitudinal HbA1c, using early phase glucose data from type 2 diabetes mellitus patients after anti-diabetic treatment.

Predicting late phase outcomes from early-phase findings can help inform decisions in drug development. If the measurements in early-phase differ from those in late phase, forecasting is more challenging. In this paper, we present a model-based approach for predicting glycosylated hemoglobin (HbA1c) in late phase using glucose and insulin concentrations from an early-phase study, investigating an anti-diabetic treatment. Two previously published models were used; an integrated glucose and insulin (IGI) model for meal tolerance tests and an integrated glucose-red blood cell-HbA1c (IGRH) model predicting the formation of HbA1c from the average glucose concentration (Cg,av ). Output from the IGI model was used as input to the IGRH model. Parameters of the IGI model and drug effects were estimated using data from a phase1 study in 59 diabetic patients receiving various doses of a glucokinase activator. Cg,av values were simulated according to a Phase 2 study design and used in the IGRH model for predictions of HbA1c. The performance of the model-based approach was assessed by comparing the predicted to the actual outcome of the Phase 2 study. We have shown that this approach well predicts the longitudinal HbA1c response in a 12-week study using only information from a 1-week study where glucose and insulin concentrations were measured.