A comparison of different model-based approaches to scale preclinical to clinical tumour growth inhibition in gemcitabine-treated pancreatic cancer
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.
Objectives: To compare four translational scaling approaches to predict clinical response to gemcitabine treatment in pancreatic cancer patients using preclinical data obtained in xenograft tumour-bearing mice. This work formed part of pillar 3 in working package I (models in oncology) within the IMI7 founded project Drug Disease Models Resource (DDMoRe).
Methods: Pharmacokinetic/Pharmacodynamic models for Gemcitabine describing longitudinal tumour size data have been developed in xenograft mice, for different pancreatic cell lines, and in patients with pancreas cancer (1, 2). The information used to develop the translational simulation exercise comprises for both mice and human: Treatment (dose, dosing schedule), Pharmacokinetics (clearance, volume of distribution, drug exposure), Tumour dynamics (tumour proliferation rate) and Pharmacodynamics (drug-effects & resistance). Based on the above information, the following approaches were evaluated for predictive translational performance:
- Direct comparison between preclinical and clinical model parameters.
- Direct comparison between preclinical and clinical maximal tumour growth inhibition (TGI) and tumour growth delay (TGD).
- Allometric scaling of the pharmacokinetic and pharmacodyamics parameters.
- Simulations of tumour growth inhibition in patients using Treatment and Pharmacokinetics, from human and Tumour dynamics and Pharmacodynamics from mice. (3).
Results: Preliminary results show that direct comparison between preclinical and clinical parameters and descriptors is not possible due to the disparity of doses, schedules and structural models used for the trials. Meanwhile descriptors simulated with the combined preclinical-clinical model (such as TGI or TGD), appeared to provide better link; in fact the predicted percentage of TGI coupling patient dosing and PK with mice derived drug effect parameters resulted close with TGI predictions found with the human PKPD model (> 100 % TGI according to (3) in both cases). The results were more promising in the case of the panc1 xenografts than the rest of tumour cells lines implanted in mice.
Conclusions: Currently the preclinical tumor growth inhibition modelling work is often used more qualitatively then quantitatively to predict the clinical results. In this work, we compare different translational approaches which could lead to more quantitative alternatives. The same exercise will also be performed with the gemcitabine-treated ovarian cancer.
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