Circumventing glioblastoma resistance to temozolomide through optimal drug combinations designed by systems pharmacology and machine learning
We successfully demonstrated the relevance of combined QSP and ML to design efficient drug combinations re-sensitising glioblastoma cells initially resistant to TMZ. The developed framework may further serve to identify personalised therapies and administration schedules by extending it to account for additional patient-specific altered pathways and whole-body features.
Authors
- Maïté Verreault (ORCID: https://orcid.org/0000-0001-6708-7698)
- Cécile Carrère (ORCID: https://orcid.org/0000-0003-3988-4010)
- Hugo Martin (ORCID: https://orcid.org/0000-0001-8029-8940)
- Annabelle Ballesta (ORCID: https://orcid.org/0000-0001-7204-1205)
- Thibault Delobel (ORCID: https://orcid.org/0000-0003-3993-9691)
- Sergio Corridore
- Ahmed Idbaïh (ORCID: https://orcid.org/0000-0001-5290-1204)
Institutions
- Université d'Orléans (FR)
- Centre National de la Recherche Scientifique (FR)
- Inserm (FR)
- École des Hautes Études en Santé Publique (FR)
- Université Paris Sciences et Lettres (FR)
- Sorbonne Université (FR)
- Institut du Cerveau (FR)
- Arènes: politique, santé publique, environnement, médias (FR)
- Institut Denis Poisson (FR)
- Université de Rennes (FR)
- Institut Curie (FR)
Publication Details
- Journal
- British Journal of Pharmacology
- Published
- 2025-04-14
- DOI
- https://doi.org/10.1111/bph.70027
- Citations
- 4
- Primary Topic
- Glioma Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 6.50
Funders
- Institut National de la Santé et de la Recherche Médicale
- Advanced Remanufacturing and Technology Centre