Tumor reactive ringlet oxygen approach for Monte Carlo modeling of photodynamic therapy dosimetry

Devices and Methods for Dosimetry of Personalized Photodynamic Therapy of Tumors: A Review on Recent Trends

Significance: Despite the widespread use of photodynamic therapy in clinical practice, there is a lack of personalized methods for assessing the sufficiency of photodynamic exposure on tumors, depending on tissue parameters that change during light irradiation. This can lead to different treatment results. Aim: The objective of this article was to conduct a comprehensive review of devices and methods employed for the implicit dosimetric monitoring of personalized photodynamic therapy for tumors. Methods: The review included 88 peer-reviewed research articles published between January 2010 and April 2024 that employed implicit monitoring methods, such as fluorescence imaging and diffuse reflectance spectroscopy. Additionally, it encompassed computer modeling methods that are most often and successfully used in preclinical and clinical practice to predict treatment outcomes. The Internet search engine Google Scholar and the Scopus database were used to search the literature for relevant articles. Results: The review analyzed and compared the results of 88 peer-reviewed research articles presenting various methods of implicit dosimetry during photodynamic therapy. The most prominent wavelengths for PDT are in the visible and near-infrared spectral range such as 405, 630, 660, and 690 nm. Conclusions: The problem of developing an accurate, reliable, and easily implemented dosimetry method for photodynamic therapy remains a current problem, since determining the effective light dose for a specific tumor is a decisive factor in achieving a positive treatment outcome.

Simulating photodynamic therapy for the treatment of glioblastoma using Monte Carlo radiative transport

Significance

Glioblastoma (GBM) is a rare but deadly form of brain tumor with a low median survival rate of 14.6 months, due to its resistance to treatment. An independent simulation of the INtraoperative photoDYnamic therapy for GliOblastoma (INDYGO) trial, a clinical trial aiming to treat the GBM resection cavity with photodynamic therapy (PDT) via a laser coupled balloon device, is demonstrated.

Aim

To develop a framework providing increased understanding for the PDT treatment, its parameters, and their impact on the clinical outcome.

Approach

We use Monte Carlo radiative transport techniques within a computational brain model containing a GBM to simulate light path and PDT effects. Treatment parameters (laser power, photosensitizer concentration, and irradiation time) are considered, as well as PDT's impact on brain tissue temperature.

Results

The simulation suggests that 39% of post-resection GBM cells are killed at the end of treatment when using the standard INDYGO trial protocol (light fluence = 200    J / cm 2 at balloon wall) and assuming an initial photosensitizer concentration of 5    μ M . Increases in treatment time and light power (light fluence = 400    J / cm 2 at balloon wall) result in further cell kill but increase brain cell temperature, which potentially affects treatment safety. Increasing the p hotosensitizer concentration produces the most significant increase in cell kill, with 61% of GBM cells killed when doubling concentration to 10    μ M and keeping the treatment time and power the same. According to these simulations, the standard trial protocol is reasonably well optimized with improvements in cell kill difficult to achieve without potentially dangerous increases in temperature. To improve treatment outcome, focus should be placed on improving the photosensitizer.

Conclusions

With further development and optimization, the simulation could have potential clinical benefit and be used to help plan and optimize intraoperative PDT treatment for GBM.

A singlet state oxygen generation model based on the Monte Carlo method of visible antibacterial blue light inactivation

Visible antibacterial blue light (VABL) has received much attention recently as a nondestructive inactivation approach. However, due to the sparse distribution of bacteria, the light energy evaluation method used in existing studies is inaccurate. Thus, the sensitivity of microorganisms to VABL in different experiments cannot be compared. In this paper, a Monte Carlo-based photon transport model with the optimized scattering phase function was constructed. The model calculated the spatial light energy distribution and the temporal distribution of cumulative singlet state oxygen (CSO) under various cell and medium parameters. The simulation results show that when the cells are sparsely distributed, <30% of light energy from the light source is absorbed by microbes and participates in photochemical reactions. The CSO produced increases with cell density and cell size. Little light energy is available, and thus, the concentration of CSO produced is insufficient to inactivate microbes at deeper depths. As the light intensity and inactivation time increased, the production of singlet state oxygen tended to level off. The model proposed here can quantify the generation of singlet state oxygen and provide a more accurate light energy guide for the VABL inactivation process.

Analyzing efficacy and safety of anti-fungal blue light therapy via kernel-based modeling the reactive oxygen species induced by light

OBJECTIVE

The goal of this study is to investigate the efficacy, safety, and mechanism of ABL for inactivating Candida albicans (C. albicans), and to determine the best wavelength for treating candida infected disease, by experimental measurements and dynamic modeling.

METHODS

The changes in reactive oxygen species (ROS) in C. albicans and human host cells under the irradiation of 385, 405, and 415nm wavelengths light with irradiance of 50mW/cm2 were measured. Moreover, a kernel-based nonlinear dynamic model, i.e., nonlinear autoregressive with exogenous inputs (NARX), was developed and applied to predict the concentration of light-induced ROS, whose kernels were selected by a newly developed algorithm based on particle swarm optimization (PSO).

RESULTS

The ROS concentration was increased respectively about 10-12 times in C. albicans and about 3-6 times in human epithelial cells by the ABL treatment with the same fluence of 90J/cm2. The NARX models were respectively fitted to the data from the experiments on both types of cells. Besides, four different kernel functions, including Gaussian, Laplace, linear and polynomial kernels, were compared in their fitting accuracies. The errors with the Laplace kernel turned out to be only 0.2704 and 0.0593, as respectively fitted to the experimental data of the C. albicans and human host cells.

CONCLUSION

The results demonstrated the effectiveness of the NARX modeling approach, and revealed that the 415nm light was more effective as an anti-fungal treatment with less damage to the host cells than the 405 or 385nm light.

SIGNIFICANCE

The kernel-based NARX model identification algorithm offers opportunities for determining the effective and safe light dosages in treating various fungal infection diseases.

Comparison of actual and simulated tumoricidal effects induced by photodynamic therapy

OBJECTIVES

Numerous studies employ mathematical methods, such as Monte Carlo simulation, to predict the tumor killing effects of photodynamic therapy (PDT) by simulating optical propagation, photosensitizer distribution, and oxygen distribution. Whether these models faithfully reflect tumor killing is unknown, and model validation using tumor cross sections in these studies is usually insufficient to answer this question. To fill this gap in our knowledge, we employed a mouse model of breast cancer to determine the spatiotemporal effects of PDT using direct histopathological and biochemical analyses of whole tumors.

METHODS

We prepared approximately 700 5-μm-thick serial sections of breast tumors of syngeneic mice treated with PDT employing the photosensitizer photocarcinorin (PsD-007, a second-generation photosensitizer developed in China). Three adjoining sections were subjected to hematoxylin and eosin staining to assess necrosis, the TUNEL assay to evaluate apoptosis, and CD31 staining to detect angiogenesis, respectively. We then generated a three-dimensional (3D) reconstruction of the tumor to evaluate these processes. We simultaneously used the Monte Carlo method to develop a model of light distribution throughout the tumor to evaluate the actual and simulated tumor killing effects induced by PDT.

RESULTS

Tumor necrosis decreased exponentially as a function of distance from the source of illumination, while the distributions of apoptosis and neovascularization were independent of light distribution. Most apoptosis occurred in the lower layers (3000-4000 μm) of the tumor where the light intensity was too low to excite the photosensitizer. Neovascularization occurred at depths ranging from 2500 to 3500 μm. These analyses provided a 3D view of how a tumor is destroyed using PDT.

CONCLUSIONS

Although the optical distribution model predicted tumor necrosis caused by PDT, it was ineffective in predicting the sites of apoptosis and vascular destruction. Mathematical modeling is limited in its capabilities required to gain a comprehensive understanding of the spatiotemporal events associated with PDT. The mouse model developed here will serve as a platform for detailed direct histopathological, biochemical, and molecular genetic analyses of the effects of PDT, which will facilitate the development of optimized treatment strategies.

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

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.

Optimizing interstitial photodynamic therapy with custom cylindrical diffusers

Interstitial photodynamic therapy (iPDT) has shown promise recently as a minimally invasive cancer treatment, partially due to the development of non-toxic photosensitizers in the absence of activation light. However, a major challenge in iPDT is the pre-treatment planning process that specifies the number of diffusers needed, along with their positions and allocated powers, to confine the light distribution to the target volume as much as possible. In this work, a new power allocation algorithm for cylindrical light diffusers including those that can produce customized longitudinal (tailored) emission profiles is introduced. The proposed formulation is convex to guarantee the minimum over-dose possible on the surrounding organs-at-risk. The impact of varying the diffuser lengths and penetration angles on the quality of the plan is evaluated. The results of this study are demonstrated for different photosensitizers activated at different wavelengths and simulated on virtual tumors modeling virtual glioblastoma multiforme cases. Results show that manufacturable cylindrical diffusers with tailored emission profiles can significantly outperform those with conventional flat profiles with an average damage reduction on white matter of 15% to 55% and on gray matter of 23% to 58%.

Photodynamic therapy: Toward a systemic computational model

We have designed a systemic model to understand the effect of Photodynamic Therapy (PDT) on long time scales. The model takes into account cell necrosis due to oxygen reactive species, cell apoptosis through the caspase pathway and the competition between healthy and tumor cells. We attempted to describe the system using state of the art computational techniques (necrosis and apoptosis) and simple models that allow a deeper understanding of the long time scale processes involved (healing and tumor growth). We analyzed the influence of the surface and tumor depth on the effectiveness of different treatment plans and we proposed, for the set of parameters used in this work, an optimum timing between sessions of PDT.

The influence of different illumination parameters on protoporphyrin IX induced cell death in squamous cell carcinoma cells

BACKGROUND

Topical photodynamic therapy (PDT) is a highly effective therapy especially for extended cancerized fields of the skin. Whenever extended fields are treated pain management is advisable. Light source mediated pain management can be performed by reducing fluence rates, as long as this does not compromise efficacy.

METHODS

Two squamous cell carcinoma cell lines (A431 and SCC-13) were subjected to in vitro PDT using two different ALA concentrations and synthesis intervals and protoporphyrin IX (PpIX) synthesis was assessed. Two total light doses (6 J/cm² and 37 J/cm²) were applied at three different fluence rates and cell viability was measured using the MTS-test.

RESULTS

Both cell lines synthetized PpIX at different kinetics. A431 cells produced a maximum 28.6 nmol/l PpIX, while SCC-13 reached only a production of 8.7 nmol/l. Illumination reduced cell viability depending on PpIX content and light dose. When a lower light dose (6 J/cm²) was applied, only the combination with the highest PpIX content was effective in A431 cells and no effect could be detected in SCC-13 cells. With a light dose of 37 J/cm², lower PpIX amounts became effective in A431 and cell death could be induced in SCC-13 cells. Light fluence rate had no differential effect in this setup.

CONCLUSIONS

In both, A431 and SCC-13 cells, total light dose is a key factor for photodynamic efficacy. Additionally, our results hint towards a threshold concentration of PpIX upon which a drastic loss of viability occurs. Light fluence rate in the analyzed range is not a limiting factor of photodynamic cytotoxicity. This may allow for the clinical implementation of low fluence rate protocols for pain management without compromising efficacy.

In silico modelling of apoptosis induced by photodynamic therapy

Photodynamic therapy (PDT) is an emergent technique used for the treatment of several diseases. After PDT, cells die by necrosis, apoptosis or autophagy. Necrosis is produced immediately during photodynamic therapy by high concentration of reactive oxygen species, apoptosis and autophagy are triggered by mild or low doses of light and photosensitizer. In this work we model the cell response to low doses of PDT assuming a bi-dimensional matrix of interacting cells. For each cell of the matrix we simulate in detail, with the help of the Gillespie's algorithm, the two main chemical pathways leading to apoptosis. We unveil the role of both pathways in the cell death rate of the tumor, as well as the relevance of several molecules in the process. Our model suggests values of concentrations for several species of molecules to enhance the effectiveness of PDT.