Effect of intratumoral administration on biodistribution of 64Cu-labeled nanoshells

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Predicting the Spatio-Temporal Response of Recurrent Glioblastoma Treated With Rhenium-186 Labelled Nanoliposomes

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. METHODS: We 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\u27s 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\u27s tumor, based on cohort parameters. RESULTS: Across 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. CONCLUSION: We 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 SIGNIFICANCE: This 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

Liposomal formulations of poorly soluble camptothecin: drug retention and biodistribution

Context: Camptothecin (CPT) represents a potent anticancer drug. Its therapeutic use however is impaired by both drug solubility, hydrolysis and protein interactions in vivo. Use of liposomes as drug formulation approach could overcome some of these challenges. Objective: The objective of this study was to perform a mechanistic study of the incorporation and retention of the lipophilic parent CPT-compound in different liposome formulations using radiolabeled CPT and thus be able to identify promising CPT delivery systems. In this context we also wanted to establish an appropriate mouse tumor model, in vivo scintigraphic imaging and biodistribution methodology for testing the most promising formulation. Materials and methods: CPT retention in various liposome formulations following incubation in buffer and serum was determined. The HT-29 mouse tumor model, 111In-labeled liposomes as well as 3H-labeled CPT were used to investigate the biodistribution of liposomes and drug. Results and discussion: The ability of different liposome formulations to retain CPT in buffer was influenced by the lipid concentration and the drug:lipid ratio rather than lipid composition. The tested formulations were cleared from the blood in the following order:CPT-solutionCPTliposomes 111In-labeled liposomes, and liposomes mainly accumulated in liver. Conclusion: Lipid composition did not influence CPT retention to the same extent as earlier observed in incorporation studies. The set up for the biodistribution study works well and is suited for future in vivo studies on CPT liposomes. The biodistribution study showed that liposomes circulated longer than free drug, but premature release of drug from liposomes occurred. Further studies to develop formulations with higher retention potential and prolonged circulation are desired

Utility of a Novel Three-Dimensional and Dynamic (3DD) Cell Culture System for PK/PD Studies: Evaluation of a Triple Combination Therapy at Overcoming Anti-HER2 Treatment Resistance in Breast Cancer

Background: Emergence of Human epidermal growth factor receptor 2 (HER2) therapy resistance in HER2-positive (HER2+) breast cancer (BC) poses a major clinical challenge. Mechanisms of resistance include the over-activation of the PI3K/mTOR and Src pathways. This work aims to investigate a novel combination therapy that employs paclitaxel (PAC), a mitotic inhibitor, with everolimus (EVE), an mTOR inhibitor, and dasatinib (DAS), an Src kinase inhibitor, as a modality to overcome resistance.Methods: Static (two dimensional, 2D) and three-dimensional dynamic (3DD) cell culture studies were conducted using JIMT-1 cells, a HER2+ BC cell line refractory to HER2 therapies. Cell viability and caspase-3 expression were examined after JIMT-1 cell exposure to agents as monotherapy or in combination using a 2D setting. A pharmacokinetic/pharmacodynamic (PK/PD) combination study with PAC+DAS+EVE was conducted over 3 weeks in a 3DD setting. PAC was administered into the system via a 3 h infusion followed by the addition of a continuous infusion of EVE+DAS 24 h post-PAC dosing. Cell counts and caspase-3 expression were quantified every 2 days. A semi-mechanistic PK/PD model was developed using the 2D data and scaled up to capture the 3DD data. The final model integrated active caspase-3 as a biomarker to bridge between drug exposures and cancer cell dynamics. Model fittings were performed using Monolix software.Results: The triple combination significantly induced caspase-3 activity in the 2D cell culture setting. In the 3DD cell culture setting, sequential dosing of PAC then EVE+DAS showed a 5-fold increase in caspase-3 activity and 8.5-fold decrease in the total cell number compared to the control. The semi-mechanistic PK/PD models fit the data well, capturing the time-course profiles of drug concentrations, caspase-3 expression, and cell counts in the 2D and 3DD settings.Conclusion: A novel, sequential triple combination therapeutic regimen was successfully evaluated in both 2D and 3DD in vitro cell culture systems. The efficacy of this combination at inhibiting the cellular proliferation and re-growth of HER2/mTOR resistant cell line, JIMT-1, is demonstrated. A biomarker-linked PK/PD model successfully captured all time-course data. The latter can be used as a modeling platform for a direct translation from 3DD in vitro settings to the clinic

Patient specific, imaging-informed modeling of rhenium-186 nanoliposome delivery via convection-enhanced delivery in glioblastoma multiforme

Convection-enhanceddeliveryofrhenium-186(186Re)-nanoliposomesisapromisingapproachto provideprecisedeliveryoflargelocalizeddosesofradiationforpatientswithrecurrentglioblastoma multiforme.Currentapproachesfortreatmentplanningutilizingconvection-enhanceddeliveryare designedforsmallmoleculedrugsandnotforlargerparticlessuchas186Re-nanoliposomes.Toenable thetreatmentplanningfor186Re-nanoliposomesdelivery,wehavedevelopedacomputationalfluid dynamicsapproachtopredictthedistributionofnanoliposomesforindividualpatients.Inthiswork,we construct,calibrate,andvalidateafamilyofcomputationalfluiddynamicsmodelstopredictthespatiotemporaldistributionof186Re-nanoliposomeswithinthebrain,utilizingpatient-specificpre-operative magneticresonanceimaging(MRI)toassignmaterialpropertiesforanadvection-diffusiontransport model.Themodelfamilyiscalibratedtosinglephotonemissioncomputedtomography(SPECT) imagesacquiredduringandaftertheinfusionof186Re-nanoliposomesforfivepatientsenrolledina PhaseI/IItrial(NCTNumberNCT01906385),andisvalidatedusingaleave-one-outbootstrapping methodologyforpredictingthefinaldistributionoftheparticles.Aftercalibration,ourmodelsare capableofpredictingthemid-deliveryandfinalspatialdistributionof186Re-nanoliposomeswithaDice valueof0.69 ± 0.18andaconcordancecorrelationcoefficientof0.88 ± 0.12(mean ± 95%confidence interval),usingonlythepatient-specific,pre-operativeMRIdata,andcalibratedmodelparametersfrom priorpatients.Theseresultsdemonstrateaproof-of-conceptforapatient-specificmodelingframework, whichpredictsthespatialdistributionofnanoparticles.Furtherdevelopmentofthisapproachcould enableoptimizingcatheterplacementforfuturestudiesemployingconvection-enhanceddelivery