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

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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.

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Acknowledgements

Support was received from the Innovative Medicines Initiative Joint Undertaking under grant agreement no 115156, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and the EFPIA companies’ in kind contribution. The DDMoRe project is also financially supported by contributions from the Academic and SME partners.

At the time the research was performed, Iñaki F Troconiz was an employee of the University of Navarra and Maria Garcia-Cremades was a PhD student from the University of Navarra. Celine Pitou and Philip W Iversen were employees of Elli Lilly and Company.

The authors would like to thank Sonya Tate for providing assistance during the writing process.

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Authors and Affiliations

  1. Pharmacometrics & Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy and Nutrition, University of Navarra, 31080, Pamplona, Spain

    Maria Garcia-Cremades & Iñaki F. Troconiz

  2. Navarra Institute for Health Research (IdisNA), University of Navarra, 31080, Pamplona, Spain

    Maria Garcia-Cremades & Iñaki F. Troconiz

  3. Department of Bioengineering and Therapeutic Sciences, UCSF, San Francisco, California, USA

    Maria Garcia-Cremades

  4. Global PK/PD & Pharmacometrics, Eli Lilly and Company, Windlesham, Surrey, UK

    Celine Pitou

  5. Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana, USA

    Philip W. Iversen

Authors
  1. Maria Garcia-Cremades
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  2. Celine Pitou
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  3. Philip W. Iversen
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  4. Iñaki F. Troconiz
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Correspondence to Iñaki F. Troconiz.

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The first version of this manuscript was part of the thesis of Garcia-Cremades M and appears in dadun.unav.edu, a digital repository of the University of Navarra.

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Garcia-Cremades, M., Pitou, C., Iversen, P.W. et al. Translational Framework Predicting Tumour Response in Gemcitabine-Treated Patients with Advanced Pancreatic and Ovarian Cancer from Xenograft Studies. AAPS J 21, 23 (2019). https://doi.org/10.1208/s12248-018-0291-9

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  • DOI: https://doi.org/10.1208/s12248-018-0291-9

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