2015 - Hersonissos, Crete - Greece

PAGE 2015: Drug/Disease modeling - Oncology
María García-Cremades

Modelling tumour growth and survival of patients with pancreatic cancer receiving Gemcitabine

Maria Garcia-Cremades (1), Celine Pitou (2), Philip W Iversen (3), Iñaki F Troconiz (1)

(1)Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, Pamplona, Spain, (2) Global PK/PD & Pharmacometrics, Eli Lilly and Company, Windlesham, Surrey, UK, (3) Lilly Research laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, USA.

Background: Gemcitabine is a nucleoside antimetabolite anticancer pro-drug that shows activity against several solid tumours. Its main indication given as a single agent is to treat pancreatic cancer. Gemcitabine has been chosen as model drug to build a translational approach from early to advanced (clinical) stages in drug development as a part of pillar 3 of working package I (models in oncology) within the IMI7 founded project, Drug Disease Models Resource (DDMoRe).

Objective: The aim of this evaluation was to build a joint tumour size and survival pharmacokinetic-pharmacodynamic model of Gemcitabine in patients with advanced pancreatic cancer.

Methods: Information related to tumour size and survival was obtained from a clinical phase II and a phase III studies  where Gemcitabine was given on standard treatment (1500 mg/kg over 30 min i.v infusion) to patients (n=287) with unresectable pancreatic cancer (locally advanced or metastic). Drug exposure was calculated for each patient using a pharmacokinetic model previously developed. Tumour size and Survival versus time data were linked and described using the population approach with NONMEM 7.2. Model evaluation was performed through predictive checks.

Results: The model used to describe the tumour mass over time incorporates a disease progression component modelled as an exponential growth, a drug efficacy part dependent of drug exposure represented by the metabolite AUC, together with resistance development. Predicted tumour changes over time were linked to probability of survival as an argument for the hazard, which is described using a Weibull model.  

Conclusion: The modelling exercise, which is currently ongoing, predicts the efficacy of Gemcitabine in terms of tumour growth inhibition and survival of patients with pancreatic cancer. It is expected to have a potential impact on the development of new anticancer drugs as well as optimizing the standard treatment of patients receiving Gemcitabine, predicting the likelihood of the treatment success and assisting with the dosing regimen selection.

Acknowledgements: This work was supported by the DDMoRe project.

Reference: PAGE 24 (2015) Abstr 3343 [www.page-meeting.org/?abstract=3343]
Poster: Drug/Disease modeling - Oncology
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