Corridore Sergio, Verreault Maïté, Martin Hugo, Delobel Thibault, Carrère Cécile, Idbaih Ahmed, Ballesta Annabelle
INSERM Unit 1331, Institut Curie, PSL Research University, CBIO-Center for Computational Biology, Mines Paris, Cancer Systems Pharmacology team, Saint Cloud, France.
AP-HP, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, Hôpitaux Universitaires La Pitié Salpêtrière - Charles Foix, DMU Neurosciences, Service de Neuro-Oncologie-Institut de Neurologie, Sorbonne Université, Paris, France.
Br J Pharmacol. 2025 Aug;182(16):3726-3743. doi: 10.1111/bph.70027. Epub 2025 Apr 14.
Glioblastoma (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.
A 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.
A 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.
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.
胶质母细胞瘤（GBM）是成人中最常见且侵袭性最强的脑肿瘤，尽管采用了包括手术、放疗和基于替莫唑胺（TMZ）的化疗在内的强化治疗，其预后仍很差。GBM对TMZ的初始或获得性耐药促使人们采用精准医学方法来设计新型高效联合药物疗法。此类研究需要考虑到O6-甲基鸟嘌呤-DNA甲基转移酶（MGMT）修复酶的过表达，该酶是导致患者对TMZ耐药的原因。
采用定量系统药理学（QSP）模型与机器学习（ML）相结合的综合方法，设计基于TMZ的药物组合，以克服对烷化剂的初始耐药性。
利用对照细胞或MGMT过表达细胞中可用的多类型时间和剂量分辨数据集，开发并验证了一个代表TMZ细胞药代动力学-药效学以及GBM中失调通路的QSP模型。计算机模拟药物筛选及后续实验验证确定了一种使TMZ耐药细胞重新敏感的策略，即联合使用TMZ与碱基切除修复和同源重组的抑制剂。利用ML得出了对这种最佳多药疗法反应的功能特征，以辅助患者的决策制定。
我们成功证明了QSP与ML相结合对于设计有效的药物组合使最初对TMZ耐药的胶质母细胞瘤细胞重新敏感的相关性。通过扩展所开发的框架以考虑额外的患者特异性改变通路和全身特征，其可能进一步用于确定个性化疗法和给药方案。
