Prospective medicinal compounds progress through multiple testing phases before becoming licensed drugs. Testing of novel compounds includes a preclinical phase where the potential therapeutic is tested in vitro and/or in animal models in vivo to predict its potential efficacy and/or toxicity in humans. The failure of preclinical models to accurately predict human drug responses can lead to potentially dangerous compounds being administered to humans, or potentially beneficial compounds being kept in development abeyance. Moreover, inappropriate choice in model organism for studying disease states may result in pushing forward inappropriate drug targets and/or compounds and wasting valuable time and resources in producing much-needed medications. In this dissertation, models for basic science research and drug testing are investigated with the intention of improving current preclinical models in order to drive drugs to market faster and more efficiently. We found that embryonic rat hippocampal neurons, commonly used to study neurodegenerative disease mechanisms in vitro, take 3-4 weeks to achieve similar, critical ion-channel expression profiles as seen in adult rat hippocampal cultures. We also characterized a newly-available commercial cell line of human induced pluripotent stem cell-derived neurons for their applicability in long-term studies, and used them to develop a more pathologically relevant model of early Alzheimer's Disease in vitro. Finally, we attempted to create an engineered, layered neural network of human neurons to study drug responses and synaptic mechanisms. Utilization of the results and methods described herein will help push forward the development of better model systems for translation of laboratory research to successful clinical human drug trials.
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Doctor of Philosophy (Ph.D.)
College of Sciences
Length of Campus-only Access
Doctoral Dissertation (Open Access)
Berry, Bonnie, "Development of human and rodent based in vitro systems toward better translation of bench to bedside in vivo results" (2015). Electronic Theses and Dissertations, 2004-2019. 5155.