Improved analysis of in vivo drug combination experiments with a comprehensive statistical framework and web-tool

Rafael Romero-Becerra (), Zhi Zhao, Daniel Nebdal, Elisabeth Müller, Helga Bergholtz, Jens Henrik Norum and Tero Aittokallio ()
Additional contact information
Rafael Romero-Becerra: Oslo University Hospital, Department of Cancer Genetics, Institute for Cancer Research
Zhi Zhao: University of Oslo, Department of Biostatistics, Faculty of Medicine, Oslo Centre for Biostatistics and Epidemiology (OCBE)
Daniel Nebdal: Oslo University Hospital, Department of Cancer Genetics, Institute for Cancer Research
Elisabeth Müller: Oslo University Hospital, Department of Cancer Genetics, Institute for Cancer Research
Helga Bergholtz: Oslo University Hospital, Department of Cancer Genetics, Institute for Cancer Research
Jens Henrik Norum: Oslo University Hospital, Department of Cancer Genetics, Institute for Cancer Research
Tero Aittokallio: Oslo University Hospital, Department of Cancer Genetics, Institute for Cancer Research

Nature Communications, 2025, vol. 16, issue 1, 1-21

Abstract: Abstract Drug combination therapy is often required to overcome the limited benefits of monotherapy in cancer treatment. While several tools exist for in vitro drug synergy screening and assessment, there is a lack of integrated methods for statistical analysis of in vivo combination experiments. To fill this gap, we present SynergyLMM, a comprehensive modeling and design framework for evaluating drug combination effects in preclinical in vivo studies. Unlike other methods, SynergyLMM accommodates complex experimental designs, including multi-drug combinations, and offers practical options for statistical analysis of both synergy and antagonism through longitudinal drug interaction analysis, including model diagnostics and statistical power analysis. These functionalities allow researchers to optimize study designs and determine an appropriate number of animals and follow-up time points required to achieve sufficient synergy and statistical power. SynergyLMM is implemented as an easy-to-use web-application, making it widely accessible for researchers without programming skills. We demonstrate the versatility and added value of SynergyLMM through its applications to various experimental setups and treatment experiments with chemo-, targeted- and immunotherapy. These case studies showcase its potential to improve robustness, statistical rigor and consistency of preclinical drug combination results, enabling a faster and safer transition from preclinical to clinical testing.

Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-65218-9 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-65218-9

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-65218-9

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().