Translational Framework Predicting Tumour Response in Gemcitabine-Treated Patients with Advanced Pancreatic and Ovarian Cancer from Xenograft Studies

Abstract

The aim of this evaluation was to predict tumour response to gemcitabine in patients with advanced pancreas or ovarian cancer using pre-clinical data obtained from xenograft tumour-bearing mice. The approach consisted of building a translational model combining pre-clinical pharmacokinetic–pharmacodynamic (PKPD) models and parameters, with dosing paradigms used in the clinics along with clinical PK models to derive tumour profiles in humans driving overall survival. Tumour growth inhibition simulations were performed using drug effect parameters obtained from mice, system parameters obtained from mice after appropriate scaling, patient PK models for gemcitabine and carboplatin, and the standard dosing schedules given in the clinical scenario for both types of cancers. Tumour profiles in mice were scaled by body weight to their equivalent values in humans. As models for survival in humans showed that tumour size was the main driver of the hazard rate, it was possible to describe overall survival in pancreatic and ovarian cancer patients. Simulated tumour dynamics in pancreatic and ovarian cancer patients were evaluated using available data from clinical trials. Furthermore, calculated metrics showed values (maximal tumour regression [0–17%] and tumour size ratio at week 12 with respect to baseline [− 9, − 4.5]) in the range of those predicted with the clinical PKPD models. The model-informed Drug Discovery and Development paradigm has been successfully applied retrospectively to gemcitabine data, through a semi-mechanistic translational approach, describing the time course of the tumour response in patients from pre-clinical studies.