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Alternative Approach to Dose-Response Modeling of Toxicogenomic Data with an Application in Risk Assessment of Engineered Nanomaterials

Davidson, Sarah E
http://orcid.org/0000-0002-2891-9380
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627666554729205

Abstract Details

2021, PhD, University of Cincinnati, Medicine: Biostatistics (Environmental Health).
Increased use of genomic data in dose-response (DR) modeling for quantitative risk assessment necessitates the development of new methods which better account for the biological underpinnings leading to adverse health effects or disease. Current genomic dose-response (GDR) modeling methods use parametric models traditionally used for evaluating in vivo endpoints. However, assumptions in the current models may be inappropriate (e.g. monotonic response) at the level of gene expression. Additionally, these GDR methods do not take into account other biological phenomenons such as a shared transcription factors, upstream signaling, and feed-back mechanisms which may lead to coordination expression of multiple genes. Coordinated changes in gene expression may result in correlated DR patterns which can be leveraged to better understand the development of adverse health effects and better estimate a dose related to minimal biological response, or benchmark dose (BMD), which can be used as an interim point-of-departure (POD) for risk assessment in the absence of in vivo data. The aim of this dissertation is to develop an alternative GDR method which couples shape-constrained spline models and Bayesian clustering models to obtain biologically relevant gene sets sharing similar DR patterns. Here, it is proposed this approach will help to better evaluate the biological mechanisms after an exposure leading to adverse health effects and obtain more cohesive BMDs which can be used as PODs in efficient interim risk assessments. Finally, we demonstrate the utility of the developed method in an evaluation of rodent lung tissue samples after exposure to a set of well-studied engineered nanomaterial exposure and compare our results with those from in vivo toxicology endpoints measuring pulmonary inflammation and fibrosis typically used in risk assessment of these exposures.
Mario Medvedovic, Ph.D. (Committee Chair)
Michael Borchers, Ph.D. (Committee Member)
Eileen Kuempel, Ph.D. (Committee Member)
Siva Sivaganesan, Ph.D. (Committee Member)
Matthew Wheeler, Ph.D. (Committee Member)
210 p.
Biostatistics
Genomic Benchmark Dose; Gene Clustering; Engineered nanomaterials; Bayesian; ALOHA

Recommended Citations

Citations

  • Davidson, S. E. (2021). Alternative Approach to Dose-Response Modeling of Toxicogenomic Data with an Application in Risk Assessment of Engineered Nanomaterials [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627666554729205

    APA Style (7th edition)

  • Davidson, Sarah. Alternative Approach to Dose-Response Modeling of Toxicogenomic Data with an Application in Risk Assessment of Engineered Nanomaterials. 2021. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627666554729205.

    MLA Style (8th edition)

  • Davidson, Sarah. "Alternative Approach to Dose-Response Modeling of Toxicogenomic Data with an Application in Risk Assessment of Engineered Nanomaterials." Doctoral dissertation, University of Cincinnati, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627666554729205

    Chicago Manual of Style (17th edition)

ucin1627666554729205
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© 2021, some rights reserved.Creative Commons License Alternative Approach to Dose-Response Modeling of Toxicogenomic Data with an Application in Risk Assessment of Engineered Nanomaterials by Sarah E Davidson is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Cincinnati and OhioLINK.