Analyzing the distribution of progression-free survival for combination therapies: A study of model-based translational predictive methods in oncology
Artikel i vetenskaplig tidskrift, 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 estimated post-hoc 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

Författare

Marcus Baaz

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Tim Cardilin

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Mats Jirstrand

Chalmers, Elektroteknik, System- och reglerteknik

European Journal of Pharmaceutical Sciences

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

Vol. 203 106901

Ämneskategorier

Matematik

Beräkningsmatematik

Sannolikhetsteori och statistik

DOI

10.1016/j.ejps.2024.106901

Mer information

Senast uppdaterat

2024-10-02