Quantitative Systems Pharmacology to Personalize Temozolomide-based Drug Combinations against Brain Tumors.
Sergio Corridore (1), Maité Verreault (2), Hugo Martin (1), Thibault Delobel (1), Cécile Carrère (3), Ahmed Idbaih (4), Annabelle Ballesta (1)
(1) INSERM U900 Institut Curie, Saint Cloud, France ; MINES ParisTech, Paris, France ; PSL Research University, Paris, France; (2) Paris Brain Institute, Inserm UMR 1127, Hopital Pitié Salpetrière AP-HP, Paris, France; (3) Institut Denis Poisson, Université d’Orléans, CNRS, 45100 Orléans, France; (4) Sorbonne Université, AP-HP, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, Hôpitaux Universitaires La Pitié Salpêtrière - Charles Foix, DMU Neurosciences, Service de Neurologie 2-Mazarin, F-75013, Paris, France
Objectives: Large inter-patient heterogeneity in anticancer drug response highlights the critical need for personalized cancer management which has favored the generation of multi-type individual patient data. However, quantitative systems pharmacology (QSP) approaches handling the complexity of multiple preclinical and clinical data types for designing patient-specific treatments are critically lacking [1-2]. This study aims to design such methodology, to individualize the combination of cytotoxic drugs with targeted molecules, towards a high benefit for patients. Multiple regulatory pathways may be altered initially or activated upon drug exposure in cancer cells, which advocates for the design of combination therapies simultaneously inhibiting multiple targets [3-4]. Such theoretical considerations are backed up by success stories of associating cytotoxic drugs with targeted therapies. The approach was developed here for Glioblastoma multiforme (GBM), the most frequent and aggressive primary brain tumors in adults, which is associated to a median overall survival <18 months despite intensive treatments combining maximal safe neurosurgery, radiotherapy and temozolomide (TMZ)-based chemotherapy. The objective was to develop a QSP pipeline to potentiate TMZ treatment by priming cancer cells with targeted molecules affecting key intracellular functions.
Methods: A mathematical model of TMZ cellular pharmacokinetics-pharmacodynamics (PK-PD) based on ordinary differential equations (ODEs) was designed, building on existing works [5]. The model describes key regulatory networks that count among the most deregulated pathways in GBM according to TCGA [6]. Briefly, TMZ is a methylating agent that is spontaneously activated upon a two-step pH-dependent process. Four types of DNA adduct are formed upon TMZ exposure, which are handled either by base excision repair (BER) or by O6-methylguanine-DNA methyltransferase (MGMT). If these initial processes of DNA repair are unsuccessful, DNA single- or double-strand breaks are created, which triggers Homologous Recombination (HR), ATR/Chk1 and p53 activation, cell cycle arrest and possibly apoptosis. TMZ PK-PD model was connected to an ODE-based cell population model that represented cell viability during drug exposure. Model calibration consisted in a modified least square approach ensuring data best-fit under biologically-sound constraints. The minimization task was performed by the Covariance Matrix Evolutionary Strategy (CMAES) algorithm. The same algorithm was used for therapeutic optimization procedures.
Results: Parameters of TMZ PK-PD model were estimated in sequential steps involving the use of longitudinal and dose-dependent datasets, informing on the concentrations of TMZ PK, DNA adducts, MGMT, double-stranded breaks, ATR, Chk1 and p53 phosphorylation, and cell death (295 datapoints in total). Most of the datasets were performed in two LN229 glioblastoma human cell lines: the parental TMZ sensitive (MGMT-) and the MGMT-overexpressing TMZ resistant (MGMT+) cells [7-11]. The model was able to faithfully reproduce these multi-type datasets coming from several independent studies. Next, the calibrated model was used as a powerful tool to investigate new therapeutic targets. As a start, we investigated drug combinations involving TMZ and only one targeted inhibitor, which was computationally represented by decreasing the value of the corresponding model parameter. The only strategy leading to a drastic increase of TMZ efficacy in both parental and resistant cell lines consisted in the complete (>90%) inhibition of the BER pathway, prior to TMZ exposure. Such high level of inhibition being challenging to achieve in the clinics, we further explored the combination of TMZ and two inhibitors. This numerical study revealed three possible parameters to be jointly targeted: MGMT protein level, BER activity, and HR activity. The optimal strategy, defined as the one requiring the smaller percentages of inhibition for both targets, was the combined administration of BER and HR inhibitors, prior to TMZ exposure. This therapeutic strategy was investigated experimentally in both LN229 cell lines and led to a drastic increase in TMZ efficacy. The model prediction of cell viability under exposure of TMZ after either BER inhibitor or HR inhibitor only, were also validated.
Conclusions: A model of TMZ PK-PD model was carefully calibrated to data and allowed to identify a non-intuitive TMZ-based drug combination leading to a drastic increase of cell death in initially resistant cells. This QSP model is being personalized using multi-omics datasets available in GBM patient-derived cell lines towards the design of patient-specific therapeutic strategies.
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