Andrew Raddatz

BME PhD Defense Presentation

Date: 2023-04-05
Time: 12:00 PM - 2:00 PM
Location / Meeting Link: EBB 5029 / https://emory.zoom.us/j/8277488202

Committee Members:
Melissa Kemp, PhD (Advisor, BME); Cristina Furdui, PhD (Wake Forest School of Medicine); Eberhard Voit, PhD (BME); Peng Qiu, PhD (BME); Susan Thomas, PhD (ME)


Title: Multiscale Computational Modeling of ROS-Generating Chemotherapies in Head and Neck Squamous Cell Carcinoma

Abstract:
Unlike promising developments for many cancers in the past decade, head and neck cancer has not realized the gains in 5-year survivability nor approval of many new therapeutic strategies. New computational and experimental techniques are crucial to better understand the disease. The cancer scientific community has recently begun to understand the role of tumor heterogeneity in the complexity of both cancer biology and how these cancers respond to treatment. Additionally, computational tools to simulate drug mechanism of action and cell-cell communication that occurs during treatment has been shown to provide important insights that experimentalists can then utilize when developing treatments and how best to apply them. To expand the community’s knowledge of head and neck cancer in response to chemotherapeutic treatment and the impact that tumor heterogeneity plays in this process, I developed a dynamic intracellular drug metabolism model and a microscopy-based pipeline to analyze and simulate the impact of tumor heterogeneity on drug response. In the dynamic intracellular drug metabolism model, I focused on ROS-generating chemotherapies, specifically beta-lapachone, and the reduction of these ROS by constructing an ordinary differential equation system representing the enzymatic reactions of drug metabolism and ROS reduction on a single cell level. I compared patients’ healthy and cancer cells by their simulated production rates of ROS during beta-lapachone treatment and found that treatment could be cancer-targeting depending on the patient and the enzyme profiles of their cells. Furthermore, using the output of these simulations in machine learning algorithms identified which combinations of enzymes were most important to the model, providing potential biomarkers to be probed for when applying these drugs to animal or human studies. In order to probe how spatial distribution of heterogeneous redox enzymes could impact beta-lapachone potency non-uniformly within tumors, I developed an agent-based model that accounts for diffusion for drug metabolized H2O2 to simulate how neighboring cells interact with one another under treatment. The developed agent-based model inputs experimentally-determined location and expression values of enzymes to predict oxygen and ROS concentration profiles as well as cell viability at various time points. The model demonstrates that spatial tumor heterogeneity impacted drug potency depending on the expression of the antioxidant enzymes and location within a tumor, reflecting known bystander effect of beta-lapachone. Agent-based simulations suggest that the more heterogeneous a tumor is, the more effective beta-lapachone will be due to an increased bystander effect given similar bulk phenotypes.