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The AAPS Journal

, 21:23 | Cite as

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

  • Maria Garcia-Cremades
  • Celine Pitou
  • Philip W. Iversen
  • Iñaki F. TroconizEmail author
Research Article
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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.

KEY WORDS

MID3 oncology PKPD modelling translational tumour size 

Notes

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.

Supplementary material

12248_2018_291_MOESM1_ESM.pdf (5.2 mb)
ESM 1 (PDF 5367 kb)

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Copyright information

© American Association of Pharmaceutical Scientists 2019

Authors and Affiliations

  • Maria Garcia-Cremades
    • 1
    • 2
    • 3
  • Celine Pitou
    • 4
  • Philip W. Iversen
    • 5
  • Iñaki F. Troconiz
    • 1
    • 2
    Email author
  1. 1.Pharmacometrics & Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy and NutritionUniversity of NavarraPamplonaSpain
  2. 2.Navarra Institute for Health Research (IdisNA)University of NavarraPamplonaSpain
  3. 3.Department of Bioengineering and Therapeutic SciencesUCSFSan FranciscoUSA
  4. 4.Global PK/PD & PharmacometricsEli Lilly and CompanyWindleshamUK
  5. 5.Lilly Research LaboratoriesEli Lilly and CompanyIndianapolisUSA

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