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

Details

• Personal Author:
  Davidson SE
• Description:
  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. [Description provided by NIOSH]
• Subjects:
  Genetics Occupational Health Safety Toxicology
• Keywords:
  Analytical Models Bioinformatics Dose Response Gene Expression Genes Genomics Nanomaterials Pathogenicity
• Publisher:
  University of Cincinnati
• Document Type:
  Text
• Funding:
  Contract
• Genre:
  Dissertation
• Place as Subject:
  Ohio ; OSHA Region 5
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  1-203
• NIOSHTIC Number:
  nn:20068814
• Citation:
  Cincinnati, OH: University of Cincinnati, 2021 May; :1-203
• Federal Fiscal Year:
  2021
• Peer Reviewed:
  False
• Source Full Name:
  Alternative approach to dose-response modeling of toxicogenomic data with an application in risk assessment of engineered nanomaterials
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:915aafc9dc0cdad44886c60c794e060915cd895deae3a2fc4965422290e0b003ffd8d51bd89f609f1ec63743a9641f991491f81a1dc32a851ae037c3f3a5b872
• Download URL:
  https://stacks.cdc.gov/view/cdc/207389/cdc_207389_DS1.pdf
• File Type:
  [PDF - 10.53 MB ]