The Hasan Program Project

Molecular Response and Imaging-based Combination Strategies for Optimal PDT

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Brian W. Pogue, Ph.D.

Professor, Thayer School of Engineering
Dean of Graduate Studies, Dartmouth College

Phone: 603-646-3861

Fax: 603-646-3856

E-mail: pogue@dartmouth.edu

Role and Rationale in PPG

This project advances the use of two novel technologies to image tissue in experimental cancer models, and will test hypothesis that these can be used to image tissue biophysical parameters, photosensitizer concentration and blood flow in tumors. The central theme in this project is that optical spectroscopy can be used in vivo to accurately measure bulk tissue values, but must be used in a way which is image-guided. Image-guided spectroscopy utilizes transmission or remission measurements for their superior signal to noise in measuring molecular concentration in vivo, yet also allows integration of the measurement into existing medical imaging technologies, such as MRI and OCT.

The first aim utilizes a commercial prototype system for combined MRI-NIR tomography of experimental rodent models, to study the biophysical changes in hemoglobin, oxygen saturation, water and sub-cellular granularity which can be assessed by NIR tomography. The changes in response to chemotherapy and receptor signaling can be measured by longitudinal imaging studies using this new imaging system. The pancreas cancer model will also be used to validate the imaging, by systematic and quantitative comparison to ex vivo measurements.

In aim 2, the ability to combine fluorescence optical tomography with structural imaging systems such as OCT and ultrasound will be tested, as a way to quantify the photosensitizer concentrations in skin. These unique hybrid systems will be validated in phantoms and tissue cultures, and then used to study skin cancer tumors in vivo.

The final aim will advance the concept of using photosensitizer imaging as a way to individualize dosimetry treatment planning, using the systems described above. The uptake rate in the tissue can be quantified using image-guided absorbance imaging. This work will also involve modeling of the parenchyma versus vascular partitioning of the drug in a pancreas tumor model, to aid in the related studies of clinical pancreas cancer PDT.

The project will validate new methods which can be directly applied to basic PDT dosimetry or eventually translated into clinical PDT dosimetry tools.

NIH Biosketch is available for download.

Major research interests

Understanding the basic photochemistry occurring in vivo is all part of providing accurate dosimetry, yet the ability to accurately know the in vivo photochemistry occurring is limited. We can model the action of the molecules given some knowledge of the photochemical rate parameters, and if the parameters are accurate we can then predict photoxicity of cells due to a given PDT dose. However, parameters such as the photobleaching rate can be up to 1000 times faster in vivo than that observed in solution, so it is imperative to develop measurement techniques to observe photochemistry occurring in vivo. We are examining ways to directly measure in vivo, the (i) photosensitizer concentration, (ii) photobleaching rate, (iii) triplet yield, (iv) oxygen distribution changes, and (v) cellular toxicity.

Thus in order to directly probe the molecules activity and heterogeneity in vivo, we have developed micro-dosimetry instrumentation which can be used to measure fluorescence intensity in vivo and tissue oxygen tension in vivo (pO2). These tools provide the basic measurements required to provide an accurate model-based interpretation of the photodynamic treatment as well as appreciate the heterogeneity of response, which is observed between patients and also within an individual tumor. In these studies we contrast the efficacy of different photosensitizers and different treatment regimens to improve the overall treatment outcome and reduce inter-patient variability in treatment outcome.

Prediction of the photochemistry in vivo is challenging unless methods to directly measure relevant parameters in vivo are developed along with modeling studies. In our work, computer models are developed to predict oxygen distribution and consumption in tumor sections, using advanced finite element methods to simulate the geometries of capillaries taken from histologic slices. The deposited dose from singlet oxygen can be calculated spatially in this modeling to understand the dose dependence of tissue-killing and the nature of the microscopic heterogeneity in this dose. This type of modeling improves our understanding of both the heterogeneity of the dose deposition as well as the magnitude of dose which is required to kill tissue.

If you are a patient or family member of a patient, please visit the Massachusetts General Hospital Cancer Center.

Please send questions or comments on this web site to Sriram Anbil.