The Hasan Program Project
Physical and Biological Determinants for Optimal PDT
Brian W. Pogue, Ph.D.
Associate Professor, Thayer School of Engineering
Phone: 603-646-3861
Fax: 603-646-3856
E-mail: pogue@dartmouth.edu
Complete curriculum vitae is available in text format.
Positions held
- 1996. Ph.D., McMaster University, Hamilton, ON, Canada
- 1996-2000. Assistant Professor, Thayer School of Engineering, Dartmouth College, Hanover, NH.
- 1997-. Associate Member, Norris Cotton Cancer Center, Dartmouth-Hitchcock Medical Center, Hanover, NH
- 2000-. Research Scientist, Wellman Laboratories of Photomedicine, Department of Dermatology, Harvard Medical School
- 2001-. Associate Professor, Thayer School of Engineering, Dartmouth College, Hanover, NH.
Role and rationale in PPG
Leader of Project 3
Our studies focus on developing a complete understanding of the in vivo processes which occur during photodynamic therapy (PDT) and developing new tools which can provide accurate estimation of the deposited dose during treatment. Dosimetry tools in PDT are still evolving and those which provide the most important information to monitor and control the deposited PDT dose need to be developed. In order to develop these tools, it is crucial to understand the photochemical and photobiological processes which occur in vivo, and how variations in the treatment can alter the nature of the biological effect. For example, examination of the vascular versus cellular targeting that can be achieved with PDT is not well understood, but simply varying the time between photosensitizer injection and the onset of light irradiation can alter the compartment within the tissue which is photosensitized. Our modeling of these processes has focused on the interaction between the photosensitizer, light, oxygen, tissue compartment and the resulting biological consequences. Essentially this becomes a matter of being able to measure these parmeters in vivo, and designing reliable systems to monitor the most useful parameters in standard application of the therapy.
A good model for refining dosimetry in patient treatment comes from radiation therapy, where decades of development have produced reliable and efficient tools for treatment planning prior to irradiation and dose monitoring with tissue simulating phantoms. Dosimetry in chemotherapy involves an accurate understanding of the drug pharmacokinetics in the patient. In PDT dosimetry, we must include both of these types of dosimetry in order to understand the patient-to-patient variability, as well as the intra-tumoral heterogeneity in response. Both of these effects can be minimized with careful attention to monitoring photosensitizer, light dose, oxygen and timing.
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.