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Gadolinium labelled nanoliposomes as the platform for MRI theranostics: in vitro safety study in liver cells and macrophages.

ABSTRACT: Gadolinium (Gd)-based contrast agents are extensively used for magnetic resonance imaging (MRI). Liposomes are potential nanocarrier-based biocompatible platforms for development of new generations of MRI diagnostics. Liposomes with Gd-complexes (Gd-lip) co-encapsulated with thrombolytic agents can serve both for imaging and treatment of various pathological states including stroke. In this study, we evaluated nanosafety of Gd-lip containing PE-DTPA chelating Gd⁺³ prepared by lipid film hydration method. We detected no cytotoxicity of Gd-lip in human liver cells including cancer HepG2, progenitor (non-differentiated) HepaRG, and differentiated HepaRG cells. Furthermore, no potential side effects of Gd-lip were found using a complex system including general biomarkers of toxicity, such as induction of early response genes, oxidative, heat shock and endoplasmic reticulum stress, DNA damage responses, induction of xenobiotic metabolizing enzymes, and changes in sphingolipid metabolism in differentiated HepaRG. Moreover, Gd-lip did not show pro-inflammatory effects, as assessed in an assay based on activation of inflammasome NLRP3 in a model of human macrophages, and release of eicosanoids from HepaRG cells. In conclusion, this in vitro study indicates potential in vivo safety of Gd-lip with respect to hepatotoxicity and immunopathology caused by inflammation.

SUBMITTER: Simeckova P

PROVIDER: S-EPMC7075985 | 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.

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Gadolinium labelled nanoliposomes as the platform for MRI theranostics: in vitro safety study in liver cells and macrophages.

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