Optimising olaparib-radiotherapy combination therapy administration schedules for glioblastoma

Background Glioblastoma is highly resistant to multimodality therapy and invariably fatal.

As radiotherapy forms the backbone of adjuvant therapy and most recurrences occur within the radiation field, there is great interest in combining radiosensitisers with radiotherapy to improve survival. One such radiosensitiser under clinical evaluation is the PARP inhibitor olaparib. However, almost all clinical trials of drug-radiation combination therapies have failed to improve patient outcomes.

We posit that a crucial contributing factor to the failure of those trials is the use of highly suboptimal administration schedules.

A mechanistic mathematical model of treatment response predicts that the olaparib-radiotherapy schedule currently being employed in clinical trials will fail to improve survival, but that the efficacy of this combination can be substantially enhanced with a novel optimised schedule.

Aims We aim to: (1) prospectively validate the mathematical model-predicted effect of olaparib-radiotherapy schedule on survival in a mouse trial; (2) retrospectively test the association between the predicted efficacy of the schedule administered and survival in glioblastoma patients; and (3) translate the mouse optimised schedule to glioblastoma patients for use in future clinical trials.

Methods We will conduct mouse pharmacokinetics-pharmacodynamics and survival studies to validate the superiority of the optimised schedule using three different intracranial patient-derived glioblastoma xenograft models.

We will compare the impact of twice (current) versus once (optimal) daily olaparib administration, and the optimal (approximately 1 hour) versus suboptimal time interval between olaparib and radiotherapy administration.

In parallel, we will perform a retrospective clinical study to determine whether the time interval between olaparib and radiotherapy administrations (currently no specific interval prescribed) impacts patient survival using data from clinical trials of olaparib-radiotherapy.

We will extract radiotherapy and olaparib administration times for the patients from drug diaries and the patient management system.

We will use these data, together with an updated version of the mathematical model incorporating human olaparib pharmacokinetic data, to predict the efficacy of the schedules administered to patients.

We will analyse the relationship between the model-predicted efficacy and patient survival using a causal survival analysis leveraging an “external control arm”.

Finally, we will use the validated mathematical model to translate the optimised olaparib-radiotherapy schedule from mice to glioblastoma patients.

How the results of this research will be used The optimised patient administration schedule will be used in future efficacy trials of olaparib-radiotherapy.

This proof-of-concept will pave the way for mathematical model-based optimisation and clinical translation of schedules for future drug-radiotherapy combinations.