Circumventing glioblastoma resistance to temozolomide through optimal drug combinations designed by systems pharmacology and machine learning

Abstract

Background and PurposeGlioblastoma (GBM), the most frequent and aggressive brain tumour in adults, is associated with a dismal prognostic despite intensive treatment involving surgery, radiotherapy and temozolomide (TMZ)‐based chemotherapy. The initial or acquired resistance of GBM to TMZ appeals for precision medicine approaches to the design of novel efficient combination pharmacotherapies. Such investigation needs to account for the overexpression of the O6‐methylguanine‐DNA methyl‐transferase (MGMT) repair enzyme which is responsible for TMZ resistance in patients.Experimental ApproachA comprehensive approach combining quantitative systems pharmacology (QSP) models and machine learning (ML) was undertaken to design TMZ‐based drug combinations circumventing the initial resistance to the alkylating agent.Key ResultsA QSP model representing TMZ cellular pharmacokinetics‐pharmacodynamics and dysregulated pathways in GBM was developed and validated using multi‐type time‐ and dose‐resolved datasets, available in control or MGMT‐overexpressing cells. In silico drug screening and subsequent experimental validation identified a strategy to re‐sensitise TMZ‐resistant cells consisting in combining TMZ with inhibitors of the base excision repair and of homologous recombination. Using ML, functional signatures of response to such optimal multi‐agent therapy were derived to assist decision‐making in patients.Conclusion and ImplicationsWe 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.