Dataset Information

LMD-13. ReSPECT-LM: Maximum tolerated dose, safety, and efficacy of intraventricular Rhenium-186 Nanoliposome (186RNL) for leptomeningeal metastases

ABSTRACT: Abstract

Introduction

Leptomeningeal metastases (LM) are a rare but typically fatal complication of advanced cancer that affects the fluid-lined structures of the central nervous system and are diagnosed in approximately 5 percent of patients with metastatic cancer. With survival measured in weeks to months, novel approaches are needed that can both improve quality and quantity of life. Rhenium-186 NanoLiposome (186RNL) permits the selective delivery of beta-emitting radiation of high specific activity directly to the tumor. In a Phase 1 trial in adults with recurrent glioblastoma (NCT01906385), the mean absorbed dose to the tumor when coverage was 75% or greater (n=10) was 392 Gy (CI 306 – 478). Thus far, the therapy has been well tolerated with one possible treatment-related serious adverse event, cerebral edema, that resolved after steroid treatment.

Methods

This is a two-part, Phase 1 dose-finding study followed by an expansion cohort to explore efficacy. Part 1 will enroll up to 21 subjects to characterize the safety and tolerability of a single dose of 186RNL administered intraventricularly via an Ommaya reservoir and to identify a maximum tolerated dose (MTD) / maximum feasible dose (MFD) for future studies. The dose limiting toxicity period is 28 days post infusion. Part 2 will independently evaluate 186RNL in 2 different cohorts: Cohort A: up to 20 subjects with a diagnosis of LM from primary breast cancer; Cohort B: up to 20 subjects with a diagnosis of LM from primary non-small cell lung cancer. The primary endpoint is to estimate the anti-tumor activity of 186RNL as a single agent. Secondary endpoints are to characterize the pharmacokinetic and dosimetry profile of a single dose of 186RNL, determine the overall response rate (ORR) based on CSF and radiographic findings, and to describe the survival distribution. Planned enrollment will begin in H2 2021.

SUBMITTER: Brenner A

PROVIDER: S-EPMC8351289 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Rhenium-186 (186Re) labeled nanoliposome (RNL) therapy for recurrent glioblastoma patients has shown promise to improve outcomes by locally delivering radiation to affected areas. To optimize the delivery of RNL, we have developed a framework to predict patient-specific response to RNL using image-guided mathematical models.MethodsWe calibrated a family of reaction-diffusion type models with multi-modality imaging data from ten patients (NCR01906385) to predict the spatio-temporal dynamics of each patient's tumor. The data consisted of longitudinal magnetic resonance imaging (MRI) and single photon emission computed tomography (SPECT) to estimate tumor burden and local RNL activity, respectively. The optimal model from the family was selected and used to predict future growth. A simplified version of the model was used in a leave-one-out analysis to predict the development of an individual patient's tumor, based on cohort parameters.ResultsAcross the cohort, predictions using patient-specific parameters with the selected model were able to achieve Spearman correlation coefficients (SCC) of 0.98 and 0.93 for tumor volume and total cell number, respectively, when compared to the measured data. Predictions utilizing the leave-one-out method achieved SCCs of 0.89 and 0.88 for volume and total cell number across the population, respectively.ConclusionWe have shown that patient-specific calibrations of a biology-based mathematical model can be used to make early predictions of response to RNL therapy. Furthermore, the leave-one-out framework indicates that radiation doses determined by SPECT can be used to assign model parameters to make predictions directly following the conclusion of RNL treatment.Statement of significanceThis manuscript explores the application of computational models to predict response to radionuclide therapy in glioblastoma. There are few, to our knowledge, examples of mathematical models used in clinical radionuclide therapy. We have tested a family of models to determine the applicability of different radiation coupling terms for response to the localized radiation delivery. We show that with patient-specific parameter estimation, we can make accurate predictions of future glioblastoma response to the treatment. As a comparison, we have shown that population trends in response can be used to forecast growth from the moment the treatment has been delivered.In addition to the high simulation and prediction accuracy our modeling methods have achieved, the evaluation of a family of models has given insight into the response dynamics of radionuclide therapy. These dynamics, while different than we had initially hypothesized, should encourage future imaging studies involving high dosage radiation treatments, with specific emphasis on the local immune and vascular response.

Dataset Information

LMD-13. ReSPECT-LM: Maximum tolerated dose, safety, and efficacy of intraventricular Rhenium-186 Nanoliposome (186RNL) for leptomeningeal metastases

Introduction

Methods

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