Model-Based Evaluation of Radiation and Radiosensitizing Agents in Oncology

A physicochemical model of X-ray induced photodynamic therapy (X-PDT) with an emphasis on tissue oxygen concentration and oxygenation

X-PDT is one of the novel cancer treatment approaches that uses high penetration X-ray radiation to activate photosensitizers (PSs) placed in deep seated tumors. After PS activation, some reactive oxygen species (ROS) like singlet oxygen (1O2) are produced that are very toxic for adjacent cells. Efficiency of X-PDT depends on 1O2 quantum yield as well as X-ray mortality rate. Despite many studies have been modeled X-PDT, little is known about the investigation of tissue oxygen content in treatment outcome. In the present study, we predicted X-PDT efficiency through a feedback of physiological parameters of tumor microenvironment includes tissue oxygen and oxygenation properties. The introduced physicochemical model of X-PDT estimates 1O2 production in a vascularized and non-vascularized tumor under different tissue oxygen levels to predict cell death probability in tumor and adjacent normal tissue. The results emphasized the importance of molecular oxygen and the presence of a vascular network in predicting X-PDT efficiency.

Model-based assessment of combination therapies - ranking of radiosensitizing agents in oncology

BACKGROUND

To increase the chances of finding efficacious anticancer drugs, improve development times and reduce costs, it is of interest to rank test compounds based on their potential for human use as early as possible in the drug development process. In this paper, we present a method for ranking radiosensitizers using preclinical data.

METHODS

We used data from three xenograft mice studies to calibrate a model that accounts for radiation treatment combined with radiosensitizers. A nonlinear mixed effects approach was utilized where between-subject variability and inter-study variability were considered. Using the calibrated model, we ranked three different Ataxia telangiectasia-mutated inhibitors in terms of anticancer activity. The ranking was based on the Tumor Static Exposure (TSE) concept and primarily illustrated through TSE-curves.

RESULTS

The model described data well and the predicted number of eradicated tumors was in good agreement with experimental data. The efficacy of the radiosensitizers was evaluated for the median individual and the 95% population percentile. Simulations predicted that a total dose of 220 Gy (5 radiation sessions a week for 6 weeks) was required for 95% of tumors to be eradicated when radiation was given alone. When radiation was combined with doses that achieved at least 8 [Formula: see text] of each radiosensitizer in mouse blood, it was predicted that the radiation dose could be decreased to 50, 65, and 100 Gy, respectively, while maintaining 95% eradication.

CONCLUSIONS

A simulation-based method for calculating TSE-curves was developed, which provides more accurate predictions of tumor eradication than earlier, analytically derived, TSE-curves. The tool we present can potentially be used for radiosensitizer selection before proceeding to subsequent phases of the drug discovery and development process.

Exposure-response modeling improves selection of radiation and radiosensitizer combinations

A central question in drug discovery is how to select drug candidates from a large number of available compounds. This analysis presents a model-based approach for comparing and ranking combinations of radiation and radiosensitizers. The approach is quantitative and based on the previously-derived Tumor Static Exposure (TSE) concept. Combinations of radiation and radiosensitizers are evaluated based on their ability to induce tumor regression relative to toxicity and other potential costs. The approach is presented in the form of a case study where the objective is to find the most promising candidate out of three radiosensitizing agents. Data from a xenograft study is described using a nonlinear mixed-effects modeling approach and a previously-published tumor model for radiation and radiosensitizing agents. First, the most promising candidate is chosen under the assumption that all compounds are equally toxic. The impact of toxicity in compound selection is then illustrated by assuming that one compound is more toxic than the others, leading to a different choice of candidate.

Model-based evaluation of image-guided fractionated whole-brain radiation therapy in pediatric diffuse intrinsic pontine glioma xenografts

Radiation therapy (RT) is currently the standard treatment for diffuse intrinsic pontine glioma (DIPG), the most common cause of death in children with brain cancer. A pharmacodynamic model was developed to describe the radiation-induced tumor shrinkage and overall survival in mice bearing DIPG. CD1-nude mice were implanted in the brain cortex with luciferase-labeled patient-derived orthotopic xenografts of DIPG (SJDIPGx7 H3F3AWT / K27 M and SJDIPGx37 H3F3AK27M / K27M ). Mice were treated with image-guided whole-brain RT at 1 or 2 Gy/fraction 5-days-on 2-days-off for a cumulative dose of 20 or 54 Gy. Tumor progression was monitored with bioluminescent imaging (BLI). A mathematical model describing BLI and overall survival was developed with data from mice receiving 2 Gy/fraction and validated using data from mice receiving 1 Gy/fraction. BLI data were adequately fitted with a logistic tumor growth function and a signal distribution model with linear radiation-induced killing effect. A higher tumor growth rate in SJDIPGx37 versus SJDIPGx7 xenografts and a killing effect decreasing with higher tumor baseline (p < 0.0001) were identified. Cumulative radiation dose was suggested to inhibit the tumor growth rate according to a Hill function. Survival distribution was best described with a Weibull hazard function in which the hazard baseline was a continuous function of tumor BLI. Significant differences were further identified between DIPG cell lines and untreated versus treated mice. The model was adequately validated with mice receiving 1 Gy/fraction and will be useful in guiding future preclinical trials incorporating radiation and to support systemic combination therapies with RT.

Crosstalk between HER2 and PD-1/PD-L1 in Breast Cancer: From Clinical Applications to Mathematical Models

Breast cancer is one of the major causes of mortality in women worldwide. The most aggressive breast cancer subtypes are human epidermal growth factor receptor-positive (HER2+) and triple-negative breast cancers. Therapies targeting HER2 receptors have significantly improved HER2+ breast cancer patient outcomes. However, several recent studies have pointed out the deficiency of existing treatment protocols in combatting disease relapse and improving response rates to treatment. Overriding the inherent actions of the immune system to detect and annihilate cancer via the immune checkpoint pathways is one of the important hallmarks of cancer. Thus, restoration of these pathways by various means of immunomodulation has shown beneficial effects in the management of various types of cancers, including breast. We herein review the recent progress in the management of HER2+ breast cancer via HER2-targeted therapies, and its association with the programmed death receptor-1 (PD-1)/programmed death ligand-1 (PD-L1) axis. In order to link research in the areas of medicine and mathematics and point out specific opportunities for providing efficient theoretical analysis related to HER2+ breast cancer management, we also review mathematical models pertaining to the dynamics of HER2+ breast cancer and immune checkpoint inhibitors.

Modeling long-term tumor growth and kill after combinations of radiation and radiosensitizing agents

PURPOSE

Radiation therapy, whether given alone or in combination with chemical agents, is one of the cornerstones of oncology. We develop a quantitative model that describes tumor growth during and after treatment with radiation and radiosensitizing agents. The model also describes long-term treatment effects including tumor regrowth and eradication.

METHODS

We challenge the model with data from a xenograft study using a clinically relevant administration schedule and use a mixed-effects approach for model-fitting. We use the calibrated model to predict exposure combinations that result in tumor eradication using Tumor Static Exposure (TSE).

RESULTS

The model is able to adequately describe data from all treatment groups, with the parameter estimates taking biologically reasonable values. Using TSE, we predict the total radiation dose necessary for tumor eradication to be 110 Gy, which is reduced to 80 or 30 Gy with co-administration of 25 or 100 mg kg^-1 of a radiosensitizer. TSE is also explored via a heat map of different growth and shrinkage rates. Finally, we discuss the translational potential of the model and TSE concept to humans.

CONCLUSIONS

The new model is capable of describing different tumor dynamics including tumor eradication and tumor regrowth with different rates, and can be calibrated using data from standard xenograft experiments. TSE and related concepts can be used to predict tumor shrinkage and eradication, and have the potential to guide new experiments and support translations from animals to humans.