Though it is well known that tobacco related products can cause prenatal maldevelopment, very little is known on how tobacco products affect bone tissue as it develops in the embryo. Identifying which chemicals can induce the greatest harm to the prenatal skeletal system is an improbable task as there are over 7,000 chemicals in tobacco smoke alone. We hypothesized that the Toxicological Priority Index (ToxPI) program can be used to rank osteogenic cytotoxicity potential to aid in the assessment of what chemicals out of the thousands can cause osteogenic differentiation inhibition. ToxPI aggregates information from various assays and incorporates them into visual “pie charts” which allow chemicals to be ranked against each other by given parameters. The larger the pie chart the greater likelihood of potential effects and vice versa. Seventeen tobacco chemical constituents were ranked using ToxPI and those chemicals with pie charts (0
To assess the ability of ToxPI to correctly predict maldevelopment in silico eight compounds were then tested in vitro: four of them being ToxPi positive and the other four having null predicted effects. To verify the predictions, human embryonic stem cells were differentiated into osteoblasts and exposed to various concentrations of each compound. Cell viability was measured via MTT assay in conjunction with a calcium assay to measure osteogenic differentiation. In addition, adult human feeder fibroblasts cell viability in response to exposure was measured. ToxPI positive predictions (xin vitro, caused differentiation inhibition. Together our data suggests that ToxPi might be useful to identify strongly inhibitory chemicals based on their cytotoxicity but might also give false negative results for chemicals that cause differentiation inhibition at sub-toxic levels.
| Identifer | oai:union.ndltd.org:csusb.edu/oai:scholarworks.lib.csusb.edu:etd-1848 |
| Date | 01 December 2018 |
| Creators | Madrid, Joseph |
| Publisher | CSUSB ScholarWorks |
| Source Sets | California State University San Bernardino |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses, Projects, and Dissertations |
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