First-in-Human Phase I Study to Evaluate the Brain-Penetrant PI3K/mTOR Inhibitor GDC-0084 in Patients with Progressive or Recurrent High-Grade Glioma.

PurposeGDC-0084 is an oral, brain-penetrant small-molecule inhibitor of PI3K and mTOR. A first-in-human, phase I study was conducted in patients with recurrent high-grade glioma.Patients and methodsGDC-0084 was administered orally, once daily, to evaluate safety, pharmacokinetics (PK), and activity. Fluorodeoxyglucose-PET (FDG-PET) was performed to measure metabolic responses.ResultsForty-seven heavily pretreated patients enrolled in eight cohorts (2-65 mg). Dose-limiting toxicities included 1 case of grade 2 bradycardia and grade 3 myocardial ischemia (15 mg), grade 3 stomatitis (45 mg), and 2 cases of grade 3 mucosal inflammation (65 mg); the MTD was 45 mg/day. GDC-0084 demonstrated linear and dose-proportional PK, with a half-life (∼19 hours) supportive of once-daily dosing. At 45 mg/day, steady-state concentrations exceeded preclinical target concentrations producing antitumor activity in xenograft models. FDG-PET in 7 of 27 patients (26%) showed metabolic partial response. At doses ≥45 mg/day, a trend toward decreased median standardized uptake value in normal brain was observed, suggesting central nervous system penetration of drug. In two resection specimens, GDC-0084 was detected at similar levels in tumor and brain tissue, with a brain tissue/tumor-to-plasma ratio of >1 and >0.5 for total and free drug, respectively. Best overall response was stable disease in 19 patients (40%) and progressive disease in 26 patients (55%); 2 patients (4%) were nonevaluable.ConclusionsGDC-0084 demonstrated classic PI3K/mTOR-inhibitor related toxicities. FDG-PET and concentration data from brain tumor tissue suggest that GDC-0084 crossed the blood-brain barrier

Clinical management of seizures in patients with meningiomas: Efficacy of surgical resection for seizure control and patient-tailored postoperative anti-epileptic drug management

Meningiomas are the most common primary intracranial tumor. They are slow growing and often incidentally found tumors that arise from the arachnoid villi. As they grow, they have a greater likelihood of becoming symptomatic with seizures being one of the most clinically significant symptoms. Seizures are more likely to present as a symptom of larger meningiomas and meningiomas that compress cortical areas particularly those in non-skull base locations. These seizures are often managed medically, utilizing the same anti-seizure medications that are used to treat other causes of epilepsy. We discuss common anti-seizure medications used including valproate, phenobarbital, carbamazepine, phenytoin, lacosamide, lamotrigine, levetiracetam and topiramate and their common adverse effects. The goal of pharmacotherapy for seizure control is to maximize seizure control while minimizing the adverse effects of the medication. The decision to provide medical management is dependent on individual seizure history and plans for surgical treatment. Patients who did not require seizure prophylaxis before surgery are commonly prescribed seizure prophylaxis postoperatively. Symptomatic meningiomas not controlled by medical management alone are commonly evaluated for surgical resection. The efficacy of surgical resection in providing seizure freedom is dependent on several features of the tumor including tumor size, the extent of the peritumoral edema, the number of tumors, sinus infiltration and the degree of resection

Multimodality imaging and mathematical modelling of drug delivery to glioblastomas

MAJC would like to thank the Isaac Newton Institute for Mathematical Sciences for its hospitality during the programme “Coupling Geometric PDEs with Physics for Cell Morphology, Motility and Pattern Formation” supported by EPSRC Grant Number EP/K032208/1.Patients diagnosed with glioblastoma, an aggressive brain tumour, have a poor prognosis, with a median overall survival of less than 15 months. Vasculature within these tumours is typically abnormal, with increased tortuosity, dilation and disorganization and they typically exhibit a disrupted blood brain barrier. Although it has been hypothesized that the “normalization” of the vasculature resulting from anti-angiogenic therapies could improve drug delivery through improved blood flow, there is also evidence that suggests that the restoration of blood brain barrier integrity might limit the delivery of therapeutic agents and hence their effectiveness. In this paper we apply mathematical models of blood flow, vascular permeability and diffusion within the tumour microenvironment to investigate the effect of these competing factors on drug delivery. Preliminary results from the modelling indicate that all three physiological parameters investigated – flow rate, vessel permeability, and tissue diffusion coefficient – interact nonlinearly to produce the observed average drug concentration in the microenvironment.PostprintPeer reviewe

MRI Based Bayesian Personalization of a Tumor Growth Model

International audienceThe mathematical modeling of brain tumor growth has been the topic of numerous research studies. Most of this work focuses on the reaction-diffusion model, which suggests that the diffusion coefficient and the proliferation rate can be related to clinically relevant information. However, estimating the parameters of the reaction-diffusion model is difficult because of the lack of identifiability of the parameters, the uncertainty in the tumor segmentations, and the model approximation, which cannot perfectly capture the complex dynamics of the tumor evolution. Our approach aims at analyzing the uncertainty in the patient specific parameters of a tumor growth model, by sampling from the posterior probability of the parameters knowing the magnetic resonance images of a given patient. The estimation of the posterior probability is based on: i) a highly parallelized implementation of the reaction-diffusion equation using the Lattice Boltzmann Method (LBM), and ii) a high acceptance rate Monte Carlo technique called Gaussian Process Hamiltonian Monte Carlo (GPHMC). We compare this personalization approach with two commonly used methods based on the spherical asymptotic analysis of the reaction-diffusion model, and on a derivative-free optimization algorithm. We demonstrate the performance of the method on synthetic data, and on seven patients with a glioblastoma, the most aggressive primary brain tumor. This Bayesian personalization produces more informative results. In particular, it provides samples from the regions of interest and highlights the presence of several modes for some patients. In contrast, previous approaches based on optimization strategies fail to reveal the presence of different modes, and correlation between parameters

Personalized Radiotherapy Planning Based on a Computational Tumor Growth Model

International audienceIn this article, we propose a proof of concept for the automatic planning of personalized radiotherapy for brain tumors. A computational model of glioblastoma growth is combined with an exponential cell survival model to describe the effect of radiotherapy. The model is personalized to the magnetic resonance images (MRIs) of a given patient. It takes into account the uncertainty in the model parameters, together with the uncertainty in the MRI segmentations. The computed probability distribution over tumor cell densities, together with the cell survival model, is used to define the prescription dose distribution, which is the basis for subsequent Intensity Modulated Radiation Therapy (IMRT) planning. Depending on the clinical data available, we compare three different scenarios to personalize the model. First, we consider a single MRI acquisition before therapy, as it would usually be the case in clinical routine. Second, we use two MRI acquisitions at two distinct time points in order to personalize the model and plan radiotherapy. Third, we include the uncertainty in the segmentation process. We present the application of our approach on two patients diagnosed with high grade glioma. We introduce two methods to derive the radiotherapy prescription dose distribution, which are based on minimizing integral tumor cell survival using the maximum a posteriori or the expected tumor cell density. We show how our method allows the user to compute a patient specific radiotherapy planning conformal to the tumor infiltration. We further present extensions of the method in order to spare adjacent organs at risk by redistributing the dose. The presented approach and its proof of concept may help in the future to better target the tumor and spare organs at risk

Deformable image registration between pathological images and MR image via an optical macro image

Computed tomography (CT) and magnetic resonance (MR) imaging have been widely used for visualizing the inside of the human body. However, in many cases, pathological diagnosis is conducted through a biopsy or resection of an organ to evaluate the condition of tissues as definitive diagnosis. To provide more advanced information onto CT or MR image, it is necessary to reveal the relationship between tissue information and image signals. We propose a registration scheme for a set of PT images of divided specimens and a 3D-MR image by reference to an optical macro image (OM image) captured by an optical camera. We conducted a fundamental study using a resected human brain after the death of a brain cancer patient. We constructed two kinds of registration processes using the OM image as the base for both registrations to make conversion parameters between the PT and MR images. The aligned PT images had shapes similar to the OM image. On the other hand, the extracted cross-sectional MR image was similar to the OM image. From these resultant conversion parameters, the corresponding region on the PT image could be searched and displayed when an arbitrary pixel on the MR image was selected. The relationship between the PT and MR images of the whole brain can be analyzed using the proposed method. We confirmed that same regions between the PT and MR images could be searched and displayed using resultant information obtained by the proposed method. In terms of the accuracy of proposed method, the TREs were 0.56 ± 0.39 mm and 0.87 ± 0.42 mm. We can analyze the relationship between tissue information and MR signals using the proposed method