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Front Phys. 2019 Apr;7. pii: 46. doi: 10.3389/fphy.2019.00046. Epub 2019 Apr 2.

Illuminating the Numbers: Integrating Mathematical Models to Optimize Photomedicine Dosimetry and Combination Therapies.

Author information

1
Translational Biophotonics Cluster, Northeastern University, Boston, MA, United States.
2
Department of Physics, Northeastern University, Boston, MA, United States.
3
Department of Bioengineering, Northeastern University, Boston, MA, United States.
4
Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC, United States.
5
Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
6
Department of Gynecologic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States.
7
Department of Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States.
8
Wellman Center for Photomedicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
9
Division of Health Sciences and Technology, Harvard University and Massachusetts Institute of Technology, Cambridge, MA, United States.
10
Department of Diagnostic Imaging and Interventional Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States.
11
Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States.
12
Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States.

Abstract

Cancer photomedicine offers unique mechanisms for inducing local tumor damage with the potential to stimulate local and systemic anti-tumor immunity. Optically-active nanomedicine offers these features as well as spatiotemporal control of tumor-focused drug release to realize synergistic combination therapies. Achieving quantitative dosimetry is a major challenge, and dosimetry is fundamental to photomedicine for personalizing and tailoring therapeutic regimens to specific patients and anatomical locations. The challenge of dosimetry is perhaps greater for photomedicine than many standard therapies given the complexity of light delivery and light-tissue interactions as well as the resulting photochemistry responsible for tumor damage and drug-release, in addition to the usual intricacies of therapeutic agent delivery. An emerging multidisciplinary approach in oncology utilizes mathematical and computational models to iteratively and quantitively analyze complex dosimetry, and biological response parameters. These models are parameterized by preclinical and clinical observations and then tested against previously unseen data. Such calibrated and validated models can be deployed to simulate treatment doses, protocols, and combinations that have not yet been experimentally or clinically evaluated and can provide testable optimal treatment outcomes in a practical workflow. Here, we foresee the utility of these computational approaches to guide adaptive therapy, and how mathematical models might be further developed and integrated as a novel methodology to guide precision photomedicine.

KEYWORDS:

cancer; combination therapy; computational modeling; immunotherapy; mathematical oncology; photodynamic therapy; photomedicine

Conflict of interest statement

Conflict of Interest Statement: RW reports being a research fund recipient, consultant, advisor, steering committee or DSMB member, or speaker for Tesaro, Clovis, Genentech, Merck, Mersana, Oncomed, Ovation Diagnostics, Janseen, Amgen, and Tapimmune. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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