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dc.rights.licenseRestricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.
dc.contributorDordick, Jonathan S.
dc.contributorTessier, Peter M.
dc.contributorKarande, Pankaj
dc.contributorGilbert, Ryan
dc.contributor.authorNierode, Gregory James
dc.date.accessioned2021-11-03T08:56:51Z
dc.date.available2021-11-03T08:56:51Z
dc.date.created2018-02-22T16:05:25Z
dc.date.issued2017-12
dc.identifier.urihttps://hdl.handle.net/20.500.13015/2141
dc.descriptionDecember 2017
dc.descriptionSchool of Engineering
dc.description.abstractDevelopment of accurate in vitro-in vivo correlations (IVIVC) for predicting the Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) of drugs and chemicals is a grand challenge in toxicology, drug discovery and precision medicine. Such correlations would facilitate rapid and efficient toxicological evaluation of commercial chemicals and drug candidates, which is otherwise inefficient, expensive and laborious. Traditional toxicological characterization of drugs and chemicals is performed using animal models, but these are poorly predictive of human toxicity. For example, one-third of phase I clinical trials fail because of toxicity in humans despite all drugs entering these trials having been predicted to be safe based on toxicity results of animal models. This poor translation of predictive toxicity between animals and humans results in significant losses of time and money. Thus, there is considerable interest in developing alternative methods to predict human toxicity, such as correlations based on in vitro models that use human cells. Effective and highly predictive alternative models will use physiologically relevant conditions (e.g., three-dimensional culture) and be high-throughput to characterize the large compound libraries and multitude of end-points that require evaluation. Importantly, it is poorly understood how various types of human cells respond to different chemical challenges.
dc.description.abstractThis thesis is directed toward addressing both the poor understanding of how specific cell types respond to chemical exposure and the need to create new in vitro models that will be useful for predicting human toxicity. We hypothesize that undifferentiated human neural progenitor cells (hNPCs) and their differentiated neural progeny have different toxic sensitivities (i.e., different IC50 values) in response to chemical or drug exposure. To assess this, we have developed a microarray-based three-dimensional (3D) cell culture platform for high-throughput, high-content screening of toxicity and differentiation outcomes. Using the platform, we have grown and differentiated hNPCs in nanoscale 3D cultures and subsequently screened chemicals for cytotoxic effects. In doing so, we have begun to investigate how different types of human cells, such as neural progenitor cells and their differentiated progeny, respond differently to chemical stimuli. Furthermore, we have also used the platform to investigate the combinatorial effects of media additives on optimization of hNPC differentiation outcomes within 3D culture environments. Through these efforts, the impact of a 3D culture environment on stem/progenitor cell differentiation outcomes has begun to be evaluated, which will improve the future development of stem cell derived 3D culture models.
dc.language.isoENG
dc.publisherRensselaer Polytechnic Institute, Troy, NY
dc.relation.ispartofRensselaer Theses and Dissertations Online Collection
dc.subjectChemical engineering
dc.titleThree-dimensional cell culture platform for high-throughput, high-content toxicity and differentiation screening of human neural progenitor cells
dc.typeElectronic thesis
dc.typeThesis
dc.digitool.pid178837
dc.digitool.pid178838
dc.digitool.pid178839
dc.rights.holderThis electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
dc.description.degreePhD
dc.relation.departmentDept. of Chemical and Biological Engineering


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