Optimized scaling of translational factors in oncology: from xenografts to RECIST
Artikel i vetenskaplig tidskrift, 2022

Purpose: Tumor growth inhibition (TGI) models are regularly used to quantify the PK–PD relationship between drug concentration and in vivo efficacy in oncology. These models are typically calibrated with data from xenograft mice and before being used for clinical predictions, translational methods have to be applied. Currently, such methods are commonly based on replacing model components or scaling of model parameters. However, difficulties remain in how to accurately account for inter-species differences. Therefore, more research must be done before xenograft data can fully be utilized to predict clinical response. Method: To contribute to this research, we have calibrated TGI models to xenograft data for three drug combinations using the nonlinear mixed effects framework. The models were translated by replacing mice exposure with human exposure and used to make predictions of clinical response. Furthermore, in search of a better way of translating these models, we estimated an optimal way of scaling model parameters given the available clinical data. Results: The predictions were compared with clinical data and we found that clinical efficacy was overestimated. The estimated optimal scaling factors were similar to a standard allometric scaling exponent of − 0.25. Conclusions: We believe that given more data, our methodology could contribute to increasing the translational capabilities of TGI models. More specifically, an appropriate translational method could be developed for drugs with the same mechanism of action, which would allow for all preclinical data to be leveraged for new drugs of the same class. This would ensure that fewer clinically inefficacious drugs are tested in clinical trials.

Oncology

Combination therapy

Mathematical modeling

Translational research

Nonlinear mixed effects

Författare

Marcus Baaz

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Stiftelsen Fraunhofer-Chalmers Centrum för Industrimatematik

Tim Cardilin

Stiftelsen Fraunhofer-Chalmers Centrum för Industrimatematik

Floriane Lignet

Merck KGaA

Mats Jirstrand

Stiftelsen Fraunhofer-Chalmers Centrum för Industrimatematik

Cancer Chemotherapy and Pharmacology

0344-5704 (ISSN) 14320843 (eISSN)

Vol. 90 3 239-250

Ämneskategorier

Farmaceutisk vetenskap

Bioinformatik (beräkningsbiologi)

Bioinformatik och systembiologi

DOI

10.1007/s00280-022-04458-8

PubMed

35922568

Mer information

Senast uppdaterat

2024-03-07