Browsing by Author "Hanenberg, H."

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    Consensus of German Transplant Centers on Hematopoietic Stem Cell Transplantation in Fanconi Anemia
    (Thieme, 2015) Chao, M. M.; Ebell, W.; Bader, P.; Beier, R.; Burkhardt, B.; Feuchtinger, T.; Handgretinger, R.; Hanenberg, H.; Koehl, U.; Kratz, C.; Kremens, B.; Lang, P.; Meisel, R.; Mueller, I.; Roessig, C.; Sauer, M.; Schlegel, P. G.; Schulz, A.; Strahm, B.; Thol, F.; Sykora, K. W.; Department of Pediatrics, Indiana University School of Medicine
    Allogeneic hematopoietic stem cell transplantation (HSCT) is currently the only curative therapy for the severe hematopoietic complications associated with Fanconi anemia (FA). In Germany, it is estimated that 10–15 transplants are performed annually for FA. However, because FA is a DNA repair disorder, standard conditioning regimens confer a high risk of excessive regimen-related toxicities and mortality, and reduced intensity regimens are linked with graft failure in some FA patients. Moreover, development of graft-versus-host disease is a major contributing factor for secondary solid tumors. The relative rarity of the disorder limits HSCT experience at any single center. Consensus meetings were convened to develop a national approach for HSCT in FA. This manuscript outlines current experience and knowledge about HSCT in FA and, based on this analysis, general recommendations reached at these meetings.
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    Dynamic Bioluminescence Imaging: Development of a Physiological Pharmacokinetic Model of Tumor Metabolism
    (Office of the Vice Chancellor for Research, 2013-04-05) Territo, P. R.; Shannon, H. E.; Freise, K. J.; Riley, A. A.; McCarthy, B. P.; Bailey, B. J.; Cai, S.; Cai, W.; Sinn, T. L.; Wang, H.; Wiek, C.; Hanenberg, H.; Pollok, Karen E.; Hutchins, Gary D.
    Bioluminescent imaging (BLI) has proven to be a valuable tool for the study of cellular biology and therapeutic response in a wide array of tumor types. Several BLI analytical approaches have been developed to assess tumor function and growth, all with the primary assumption that substrate concentrations saturate the luciferase enzyme. Recent work suggests that when D-luciferin is administered over the range from 75-600mg/kg, target tissue concentrations of D-luciferin are well below the Km of luciferase for the reaction, and, that the pharmacokinetics of D-luciferin significantly impact observed emission rates. To address the concentration and PK concerns, we developed a three compartment physiologically based pharmacokinetic (PhPK) model for D-luciferin including oxidation by luciferase via Michaelis-Menten kinetics. The model was applied to dynamically acquired BLI in NOD/SCID mice with ectopic luciferase-transfected SF767 tumors. The current PhPK model estimates tumor volume, tumor substrate metabolism (M ̅), tumor blood flow (Vb) and substrate extraction from the blood (Er). Studies were conducted using intraperitoneal, subcutaneous and intravenous routes of administration of 150 mg/kg of D-luciferin, where dynamic BLI was conducted weekly for four weeks. The D-luciferin concentration in tumor tissue, determined immediately after the last imaging session, was found to be approximately 8-fold below the reported Km for the reaction across all routes of administration, supporting the need for a PhPK modeling approach for analyzing BLI data. The model-predicted tumor volumes increased over time and were highly correlated with caliper-measured tumor volumes (y=1.984x, R2=0.980, p<0.0001). Tumor D-luciferin metabolism was found to increase exponentially over the 4 weeks, while blood flow decreased over this same interval, a finding which is consistent with the interpretation of a Warburg effect. When tumor M ̅ was compared with the traditional measures of peak emission (Cmax) and area under the curve (AUC), it was found that metabolism increased exponentially with increases in either Cmax (y=92.7exp(8E-11x), R2= 0.997) or AUC ( y=86.4exp(5E-14x), R2= 0.989), suggesting that Cmax and AUC may substantially underestimate the magnitude of tumor metabolism. The present PhPK model of D-luciferin distribution and metabolism overcomes limitations in the Cmax and AUC approaches caused by incorrect substrate: enzyme concentration assumptions, and thus provides a more reliable estimate of tumor burden, growth, and therapeutic response.
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    Dynamic Bioluminescence Imaging: Development of a Physiological Pharmacokinetic Model of Tumor Metabolism
    (Office of the Vice Chancellor for Research, 2012-04-13) Territo, P.R.; Shannon, H.E.; Freise, K.J.; Riley, A.A.; McCarthy, B.P.; Bailey, B.J.; Cai, S.; Cai, W.; Sinn, T.L.; Wang, H.; Hanenberg, H.; Pollok, K.E.; Hutchins, G.D.
    Bioluminescence (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.