NIMG-75. ANALYZING THE INTERFACE BETWEEN MRI AND DRUG DISTRIBUTION USING ORTHOTOPIC GBM-DERIVED XENOGRAFT (PDX) MODELS. (12th November 2021)

• Record Type: Journal Article
• Authors: Channar, Sameer; Ranjbar, Sara; Jackson, Pamela; Hu, Leland; Regan, Michael; Abdelmoula, Walid; Lopez, Begona; Macura, Slobodan; Agar, Jefferey; Sarkaria, Jann; Agar, Nathalie; Swanson, Kristin
• Is Part Of: Neuro-oncology. Volume 23: Supplement 6(2021)
• Page Start: vi146
• Page End: vi146
• Publication Date: 2021-11-12
• Subjects: Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/noab196.572
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: INTRODUCTION: Glioblastoma (GBM) is a diffusely invasive primary brain tumor with significant spread of tumor cells to the periphery of visible image abnormality. Enhancement of Gadolinium (Gd) contrast agent on magnetic resonance imaging (MRI) has historically been considered a confirmation of local breakdown of the blood brain barrier (BBB) and sufficient drug delivery to the bulk of tumors. In this work, we used GBM-derived xenograft (PDX) models to compare drug delivery in GBM brain for high and low BBB-permeable drugs. MATERIALS AND METHODS: Five patient-derived orthotopic xenograft models from two GBM cell lines (GBM39 and GBM12) were co-dosed with erlotinib and osimertinib, two drugs with low and high BBB-permeability, respectively. T1Gd and T2-weighted MRIs were acquired from all animals prior to model sacrifice. Tumors were manually segmented on denoised and standardized MRIs and intensity patterns were captured using first and second order statistical features in the moving 3x3 kernel. We compared drug levels found in Matrix Assisted Laser Desorption Ionization (MALDI) in T1Gd enhancement, T2 enhancement, and normal brain. We also performed linear regression modeling to predict drug levels using MRI features. Model performance was measured using root mean squared error (RMSE). RESULTS: Our analysis showed correlations between imaging features and MALDI drug levels. Osimertinib had a uniform distribution across the brain for all animals and all cell lines, consistent with our expectation for a high BBB-penetrant drug. Erlotinib showed the highest drug levels in T2 for GBM39 and in T1Gd for GBM12. Regression models showed promising results for predicting Erlotinib with a low RMSE of 0.037. CONCLUSION: Our preliminary results suggest MRI can be predictive of drug levels for low-BBB penetrant drugs. Understanding the relationship between MRIs and drug distribution in diffuse tumors can be beneficial to developing effective treatment.