Séance Séminaire

Séminaire ACSIOM

Glioblastoma cell variability and circadian rhythms control temozolomide efficacy: from cellular pharmacokinetics-pharmacodynamics to heterogeneous cancer cell population models

Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adults, and is currently associated with a dismal prognosis despite intensive treatments combining surgery, radiotherapy and temozolomide-based chemotherapy. Clinical trials over the last two decades testing various multi-agent pharmacotherapies have failed demonstrating any significant patient survival improvement so far. Chronotherapy, that consists in administering antitumor drug according to the patient’s 24h-rhythms is considered as a promising therapeutic approach to improve treatment tolerability and efficacy. Interestingly, recent clinical and preclinical studies have highlighted the dependency of temozolomide (TMZ) efficacy on administration timing. Median overall survival (OS) of GBM patients receiving TMZ in the morning was equal to 1.43 years as compared to 1.13 for patients taking the same drug dose in the evening. In a subgroup of patients whose tumor presented methylated promoter of MGMT DNA repair enzyme (resulting in decreased MGMT protein expression and increased sensitivity to TMZ), the difference in survival was even higher as the median OS was 6 months longer for AM patients as compared to evening patients. In order to obtain quantitative predictions on the mechanisms underlying temozolomide chronoefficacy, we designed a systems pharmacology model at the cell population level as follows. A simplified ODE-based model of TMZ pharmacokinetics-pharmacodynamics (PK-PD) was connected to a model representing the cancer cell population dynamics though a PDE structured in the amount of DNA damage in a cell and sensitivity to damage. The PK part of the ODE model was fully designed and calibrated to data, whereas the remaining elements of this combined model were inferred from cell culture circadian datasets. To properly fit all datasets, we had to include in the model an inter-cell variability accounting, standing either for different rates of DNA damage formation or repair. This addition allowed a successful model calibration, in contrast to the model in which population heterogeneity came solely from the initial damage distribution, prior any drug exposure. In the talk, I will present the data available, on which we tailored our model on. Then I shall introduce a simplified version of the PDE model, that suggested the need of inter-cell variability, and afterwards the complete model, that covers more datasets and includes more biological assumptions. I will conclude on the first conclusions of this work in progress, and say a few works on the dataset that is not yet included.