Doctor of Philosophy, Case Western Reserve University, 2023, Molecular Medicine

Cardiovascular disease is the second leading cause of death in cancer survivors. As a result, the cardio-oncology field was established to explore the connection between cancer treatments and adverse cardiovascular outcomes. Understanding the independent mechanisms of cardiotoxicity and implementing precision medicine approaches are crucial for enhancing cancer survivorship. This work investigates novel network medicine, drug repurposing, and translational science strategies to identify novel therapeutic avenues for cardiovascular adverse events. Chapter 1 offers an overview of cardio-oncology adverse events, known cardiotoxic cancer therapies, and an introduction to implementing precision medicine approaches in the field. Chapter 2 presents a case study of using systems biology and network medicine to identify repurposable drugs for atrial fibrillation, a common cardio-oncology adverse event. Metformin emerged as a top candidate, and our study validated its efficacy for relieving atrial fibrillation risk and genomic signatures, using large-scale electronic health record epidemiologic data and functional validation in hiPSC-cardiomyocytes. Chapter 3 investigates a likely mechanism of doxorubicin-mediated heart failure by examining branched-chain amino acid (BCAA) metabolism. We observed that doxorubicin is associated with impaired breakdown of alpha ketoacids, which affects mitochondrial ATP synthesis and baseline oxygen consumption rate. Treatment with metformin improved BCAA catabolism, mitochondrial phenotype, and glycolytic capacity. Subnetwork analysis of CELF5 and IGFL2/ IGFL3 revealed transcriptional signatures related to tissue remodeling and repair, cardiac cell development, and drug metabolism in doxorubicin and metformin treated hiPSC-cardiomyocytes. Lastly, we show that metformin improves doxorubicin-induced cardiotoxicity by improved heart function and cardiac tissue integrity. Chapter 4 explores biomarker discovery techniques to a unique card (open full item for complete abstract)

Committee: Feixiong Cheng (Advisor); Jonathan Smit (Committee Chair); Patrick Collier (Committee Chair); John Barnard (Committee Chair); Timothy Chan (Committee Chair); Mina Chung (Committee Member) Subjects: Bioinformatics; Biology; Health Care; Medicine; Pharmaceuticals

Doctor of Philosophy, The Ohio State University, 0, Biomedical Sciences

Electronic Health Records (EHRs) clinical narratives provide longitudinal information about drug-induced adverse events. However, it is time and labor-expensive to manually review those clinical narratives and extract adverse drug events (ADEs). A robust automated system needs to be included in current clinical settings for early detection of ADEs. So, building an automated system that uses Artificial Intelligence (AI) to process those clinical narratives and extract ADEs is in demand. Moreover, a generalized system will work on different types of clinical notes, thus reducing the technical dependencies and associated costs. Natural Language Processing (NLP), a field of AI, can automatically process free texts and extract semantic information. So, the central hypothesis of this research is that NLP models can automatically detect ADEs from unstructured EHRs. The long-term goal is to build an automated system in clinical settings for the early detection of ADEs. This dissertation has three aims that are connected to each other to accomplish the long-term goal. Aim 1 focuses on the generalizability of the NLP model to identify drug-induced ADEs from different EHR sources. The primary objective of Aim 1 is to evaluate the applicability of the NLP model in determining drug-induced ADEs across various EHR systems. To facilitate this goal, we also created a novel gold standard corpus. Aim 2 develops an ADE detection model to identify drug-induced adverse events at the patient level. Aim 3: Identify drug discontinuation information to develop a temporal model for the novel causal drug-ADE relation discovery.

Committee: Lang Li (Advisor) Subjects: Bioinformatics; Biomedical Research; Oncology