Characterization of A Type 1 Collagen Targeted PET Tracer

poster abstractRenal fibrosis occurs in many diseases of the kidney, including chronic kidney disease (CKD). Renal fibrosis is characterized by an excessive accumulation and deposition of extracellular matrix components, mainly type I collagen. Determination of the presence and extent of renal fibrosis may aid in the prediction of the long-term outcome of renal function in CKD. Biopsy is considered the gold standard in the diagnosis of renal fibrosis; however biopsy is inherently invasive and does not easily lend itself to following the disease thru time. A noninvasive technique such as PET would both allow the detection and monitoring of renal fibrosis progression. A type I collagen-specific cyclic peptide, EP-3533, has been identified and used as a contrast agent in MRI after conjugation with three Gd-DOTA chelates (Caravan et al 2007). To explore the potential for imaging with PET, which can provide a quantitative assessment of regional peptide localization, we have prepared an EP-3533 conjugate incorporating the NODAGA chelating agent at its amine terminus, and radiolabeled that conjugate with generator-produced positron-emitting 68Ga (68-minute half-life). In vitro association kinetics binding of the labeled peptide was performed in collagen type 1 coated plates, where 68GaDOTA-EP-3533 exhibited a Kd of 0.2 M for type I collagen. To better characterize the tracer in an animal model, renal fibrosis was induced in male Wistar rats by clamping the renal artery and vein of the left kidney for 50 minutes. Thus providing both a diseased and control kidney in each animal. Approximately 10 weeks after surgery both left (fibrotic) and right (normal) kidneys were resected and frozen and mounted in OTC for cryotomy. Longitudinal sections obtained from each kidney were used for autoradiography. ROI analysis found an approximate two- to four-fold region-dependent increase in binding in fibrotic tissue compared to normal. Collagen and non-collagen protein levels were determined in the same kidney sections that had been used for autoradiography using a commercially available staining assay. This assay yielded a 1.7-fold difference in collagen levels between normal and fibrotic tissue. Additionally, representative slices were stained with Sirius Red for histological evaluation. Preliminary data indicates that 68Ga-NODAGA-EP-3533 binds to collagen-rich tissue, consistent with the literature for Gd-DOTA-EP-3533. In vivo studies in an animal model of fibrosis are needed to further characterize this tracer and its potential for PET tracer detection and monitoring of Renal Fibrosis

Dynamic Bioluminescence Imaging: Development of a Physiological Pharmacokinetic Model of Tumor Metabolism

poster abstractBioluminescence (BLI) is a technology which has been studied extensively across multiple genera for more than 90 years. Over this period, BLI has emerged as a powerful noninvasive tool to study tumor localization, growth, and response to therapy due to the relatively recent technological advancements in instrumentation and molecular biology. This technology takes advantage of molecular transfection of the luciferase (LUC) gene from the North American firefly, Photinus pyralis, into human cancer cells, which are then implanted (ectopic or orthotopic) in mice. Oxidation of the exogenously administered substrate D-luciferin by the LUC gene product results in emission of green-yellow photons which are then evaluated in the context of tumor growth and development. Despite the more than 30 years of characterization, there exists a fundamental gap in our knowledge of the underlying PK/PD processes which are at the heart of nearly all BLI interpretation, and has lead to a dogmatic adherence in the literature to numerical methods which are at best simple corollaries of tumor metabolic rate. In an attempt to fill this void, this paper will present a new PK/PD model which takes advantage of the temporal nature of both substrate transport and light evolution. In addition, we will compare these results to traditional non-model based analyses and show how they differ. Lastly we will present OATS (One at A Time) Parameter Sensitivity and Monte Carlo Noise Analysis to characterize the numerical stability and sensitivity of this new model