Graduate Defense: Celin Younan

Thesis Information

Title: Automating Pharmacokinetic Predictions in Artemisia

Program: Master of Science in Materials Science and Engineering

Advisor: Dr. Jennifer Forbey, Biological Sciences

Committee Members: Dr. Sven Buerki, Biological Sciences; and Dr. Lisa Warner, Chemistry and Biochemistry

Abstract

A thorough understanding of pharmacokinetics (PK) is essential to predict the consequences of organisms exposed to chemicals. In medicine, predictions of PK of drugs allows us to properly prescribe drug treatments. In toxicology, PK allows us to predict the potential exposure of environmental contaminants and how they may affect organisms at the time of exposure or in the future. Chemical ecology could benefit from computational predictions of PK to better understand which plants wild herbivores consume or avoid. A limitation in computational predictions of PK in chemical ecology is the large quantities of biodiverse natural products involved in complex plant-herbivore-microbial interactions compared to biomedical and environmental toxicology studies that focus on a select number of chemicals. The objective of this research was to automate the process of predicting PK of known chemical structures in plants consumed by herbivores and to use predicted PK output to test hypotheses in chemical ecology. While a single plant taxa (Artemisia spp.) was used, this automated approach could also predict PK for chemicals that may be used to understand drug-drug interactions in pharmacology, predict exposure to environmental contaminant in toxicology, and identify mechanisms mediating plant-microbe-herbivore interactions. However, the broad benefits of predicting PK across disciplines requires a workforce with competency in chemistry, physiology, and computing who can propose and test hypotheses relative to different disciplines. A Lab-based Undergraduate Research Experiences (LURE) helped build these competencies. Specifically, a PK lab module in an undergraduate course was used to create a sustainable quality control step to continuously validate chemical structures used in the automated process of predicting PK. The course simultaneously provided students with an authentic research experience where they used training that integrated chemistry, pharmacology, computing, public databases, and literature searches to propose and test new hypotheses. Students gained indispensable interdisciplinary research skills that can be transferred to jobs in veterinary and human medicine, pharmaceutics, and natural sciences. Moreover, undergraduates used existing and new PK data to generate and test novel hypotheses that go beyond that work of any single graduate student or discipline. Overall, the integration of computing and authentic research experiences has advanced the research capacity of a diverse workforce who can predict exposure and consequences of chemicals in organisms.