Tumor Static Concentration Curves in Combination Therapy
Journal article, 2017

Combination therapies are widely accepted as a cornerstone for treatment of different cancer types. A tumor growth inhibition (TGI) model is developed for combinations of cetuximab and cisplatin obtained from xenograft mice. Unlike traditional TGI models, both natural cell growth and cell death are considered explicitly. The growth rate was estimated to 0.006 h−1 and the natural cell death to 0.0039 h−1 resulting in a tumor doubling time of 14 days. The tumor static concentrations (TSC) are predicted for each individual compound. When the compounds are given as single-agents, the required concentrations were computed to be 506 μg · mL−1 and 56 ng · mL−1 for cetuximab and cisplatin, respectively. A TSC curve is constructed for different combinations of the two drugs, which separates concentration combinations into regions of tumor shrinkage and tumor growth. The more concave the TSC curve is, the lower is the total exposure to test compounds necessary to achieve tumor regression. The TSC curve for cetuximab and cisplatin showed weak concavity. TSC values and TSC curves were estimated that predict tumor regression for 95% of the population by taking between-subject variability into account. The TSC concept is further discussed for different concentration-effect relationships and for combinations of three or more compounds.

tumor xenograft

mixture dynamics

model-based drug development

oncology

pharmacokinetic/pharmacodynamic modeling

Author

Tim Cardilin

University of Gothenburg

Chalmers, Mathematical Sciences, Mathematics

Joachim E Almqvist

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Mats Jirstrand

Fraunhofer-Chalmers Centre

A. Sostelly

Merck KGaA

F. Hoffmann-La Roche AG

C. Amendt

Merck KGaA

S. El Bawab

Merck KGaA

Johan Gabrielsson

Swedish University of Agricultural Sciences (SLU)

AAPS Journal

1550-7416 (eISSN)

Vol. 19 2 456-467

Subject Categories

Pharmaceutical Sciences

Computational Mathematics

Bioinformatics (Computational Biology)

Areas of Advance

Life Science Engineering (2010-2018)

DOI

10.1208/s12248-016-9991-1

More information

Latest update

4/11/2018