Irreversible electroporation (IRE) is an emerging focal therapy which is demonstrating utility in the treatment of unresectable tumors where thermal ablation techniques are contraindicated. IRE uses ultra-short duration, high-intensity monopolar pulsed electric fields to permanently disrupt cell membranes within a well-defined volume. Though preliminary clinical results for IRE are promising, implementing IRE can be challenging due to the heterogeneous nature of tumor tissue and the unintended induction of muscle contractions. High-frequency IRE (H-FIRE), a new treatment modality which replaces the monopolar IRE pulses with a burst of bipolar pulses, has the potential to resolve these clinical challenges. We explored the pulse-duration space between 250 ns and 100 μs and determined the lethal electric field intensity for specific H-FIRE protocols using a 3D tumor mimic. Murine tumors were exposed to 120 bursts, each energized for 100 μs, containing individual pulses 1, 2, or 5 μs in duration. Tumor growth was significantly inhibited and all protocols were able to achieve complete regressions. The H-FIRE protocol substantially reduces muscle contractions and the therapy can be delivered without the need for a neuromuscular blockade. This work shows the potential for H-FIRE to be used as a focal therapy and merits its investigation in larger pre-clinical models.

New directions in medical and biomedical sciences have gradually emerged over recent years that will change the way diseases are diagnosed and treated and are leading to the redirection of medicine toward patientspecific treatments. We refer to these new approaches for studying biomedical systems as predictive medicine, a new version of medical science that involves the use of advanced computer models of biomedical phenomena, high-performance computing, new experimental methods for model data calibration, modern imaging technologies, cutting-edge numerical algorithms for treating large stochastic systems, modern methods for model selection, calibration, validation, verification, and uncertainty quantification, and new approaches for drug design and delivery, all based on predictive models. The methodologies are designed to study events at multiple scales, from genetic data, to sub-cellular signaling mechanisms, to cell interactions, to tissue physics and chemistry, to organs in living human subjects. The present document surveys work on the development and implementation of predictive models of vascular tumor growth, covering aspects of what might be called modeling-and-experimentally based computational oncology. The work described is that of a multi-institutional team, centered at ICES with strong participation by members at M. D. Anderson Cancer Center and University of Texas at San Antonio. This exposition covers topics on signaling models, cell and cell-interaction models, tissue models based on multi-species mixture theories, models of angiogenesis, and beginning work of drug effects. A number of new parallel computer codes for implementing finite-element methods, multi-level Markov Chain Monte Carlo sampling methods, data classification methods, stochastic PDE solvers, statistical inverse algorithms for model calibration and validation, models of events at different spatial and temporal scales is presented. Importantly, new methods for model selection in the presence of uncertainties fundamental to predictive medical science, are described which are based on the notion of Bayesian model plausibilities. Also, as part of this general approach, new codes for determining the sensitivity of model outputs to variations in model parameters are described that provide a basis for assessing the importance of model parameters and controlling and reducing the number of relevant model parameters. Model specific data is to be accessible through careful and model-specific platforms in the Tumor Engineering Laboratory. We describe parallel computer platforms on which large-scale calculations are run as well as specific time-marching algorithms needed to treat stiff systems encountered in some phase-field mixture models. We also cover new non-invasive imaging and data classification methods that provide in vivo data for model validation. The study concludes with a brief discussion of future work and open challenges.

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Microfluidic technology has led to the development of advanced in vitro tumor platforms that overcome the challenges of in vivo animal and in vitro two dimensional models. This paper presents platform designs and methods used to develop complex vascularized in vitro models to mimic the tumor microenvironment. Features of these platforms include a continuous, aligned endothelium that allows for cell-cell interactions between vasculature and tumor cells. A novel platform for fabrication of a single endothelialized microchannel encased within a collagen platform hosting breast cancer cells was developed and utilized to study the influence of cellular interaction on transport phenomenon through vasculature in a hyperpermeable tumor microenvironment. This platform relies on subtractive tissue engineering fabrication techniques. Through confocal imaging we have demonstrated that the platform produces enhanced vessel leakiness recapitulating physiological features of the tumor microenvironment. The influence of tumor endothelial interactions on transport of particles was also demonstrated. Additionally, we designed two more complex and intricate endothelialized microfluidic networks by combining lithographic techniques with additive tissue engineering methods. We created a network platform consisting of interconnected microchannels to model a highly vascularized system and successfully perfused the system with fluorescent particles. Finally, we developed a physiologically representative in vitro microfluidic platform with vasculature patterned from in vivo data showing the versatility of these systems to replicate the complex geometries of tumor microvasculature and dynamically measured particle transport. Overall, we have shown the ability to develop functional microfluidic vascular tumor platforms of varying complexities and demonstrated their utility for studying spatial particle transport within these systems.

H-FIRE can be used to induce rapid, predictable ablations in hepatic tissue without the need for intraoperative paralytics or cardiac synchronization. These advantages may overcome limitations that restrict currently available IRE technology for hepatic ablations.