Analyzing the distribution of progression-free survival for combination therapies: A study of model-based translational predictive methods in oncology
Journal article, 2024

Progression-free survival (PFS) is an important clinical metric in oncology and is typically illustrated and evaluated using a survival function. The survival function is often estimatedĀ post-hocĀ using the Kaplan-Meier estimator but more sophisticated techniques, such as population modeling using the nonlinear mixed-effects framework, also exist and are used for predictions. However, depending on the choice of population model PFS will follow different distributions both quantitatively and qualitatively. Hence the choice of model will also affect the predictions of the survival curves.

In this paper, we analyze the distribution of PFS for a frequently used tumor growth inhibition model with and without drug-resistance and highlight the translational implications of this. Moreover, we explore and compare how the PFS distribution for combination therapy differs under the hypotheses of additive and independent-drug action.

Furthermore, we calibrate the model to preclinical data and use a previously calibrated clinical model to show that our analytical conclusions are applicable to real-world setting. Finally, we demonstrate that independent-drug action can effectively describe the tumor dynamics of patient-derived xenografts (PDXs) given certain drug combinations.

Oncology

Progression-free survival

Nonlinear mixed effects

Combination therapy

Author

Marcus Baaz

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Tim Cardilin

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Mats Jirstrand

Chalmers, Electrical Engineering, Systems and control

European Journal of Pharmaceutical Sciences

0928-0987 (ISSN) 1879-0720 (eISSN)

Vol. 203 106901

Subject Categories

Mathematics

Computational Mathematics

Probability Theory and Statistics

DOI

10.1016/j.ejps.2024.106901

More information

Latest update

10/2/2024