Biomedical Engineering Reference
In-Depth Information
a less definitive outcome with complex and hetergeneous response in both
assays.
The bone marrow study provided a more straightforward system for evaluation
and validation. The flow data subset analysis information was developed using
a well-characterized panel of mouse antibodies and was confirmed with a secondary
test using microscopic cytology. The rhEpo treatment was known to trigger erythro-
blast production. The bone marrow subset data trended with similar end points
between flow and cytology; however, more statistically significant variations were
observed in the flow cytometry subsets.
Looking at multiple physiological end points in a single cell in the oxidative stress
assay (lipid peroxidation, glutathione concentration, mitochrondial membrane
potential, and finally apoptosis) provides a time-based look into a complex system.
The observation propagation or abrogation of the stress effects to other compartments
provides information on the mechanism of toxicity.
Ironically, the availability and complexitiy of the technology itself provide the
least obstacle. Benchtop flow cytometers/analyzers are readily available in hospitals
and clinics and used for a variety of routine diagnostic and monitoring applications.
The platforms have evolved to provide robust, easy to operate systems with ever-
expanding color capabilities. The more complex cell sorting systems do require
a trained operator.
Proof of the value-added aspects of the implementation of flow cytometric
biomarker-based analysis in preclinical toxicity testing will be the key. Reduced
time, reagent, and animal costs are one form of value addition to the pharmaceutical
company. Clinicians will require evidence of the robustness of the assay and assay
outcomes. However, because there is a routine collection of a variety of tissue types
amenable to flow cytometry during any study, once an assay design is establised, data
could be generated from logged samples for statistical analysis within a fairly reduced
time frame.
ACKNOWLEDGMENTS
The research described in this chapter was the result of the efforts of many scientists,
technicians, and statisticians. The authors wish to acknowledge the following
individuals for their contributions to this work: Rogely Boyce, Ryan Boyle, Yifeng
Chen, Steve Clark, Harris Cohen, Molly Cool-Bainter, Deidre Dalmas, Cynthia
Eberly, Kendall Frazier, Barry Gailliard, Tracy Gales, Vicki Gallagher, Jim
Greenwood, Catherine Hu, Jim Huffnagle, Steven Hughes, Angela Hughes-Earle,
Amy Landis, Michelle Lee, Nancy Lee Karen Lynch, Michal Magid-Slav, Beverly
Maleeff, Chrisee Mignot, Rosanna Mirabile, Eric Moore, David Mullins, Padma
Narayanan, Georgina Paulazzo, Dana Pietrzak, Robert Ray, Kate Rhodes, Kim
Roland, Kelly Ross, Lester Schwartz, Marshall Scicchitano, Stacie Sink, Krista
Stayer, Laura Storck, Kay Tatsuoka, Pattie Terreson, Bharath Thippireddy, Heath
Thomas, Roberta Thomas, Doug Thudium, PatrickWarren, Mark Watkins, Gang Xu,
Tiffany Yarnall, and Cindy Zhang.
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