Paper
9 March 2011 Quantifying receptor density in vivo using a dual probe approach with fluorescence molecular imaging
Author Affiliations +
Abstract
Molecular imaging technologies are advancing rapidly and optical techniques in particular are set to play a large role in preclinical pharmaceutical testing. These approaches, however, are generally unable to quantify the level of expression of imaging probe reporters. In this study a novel method of quantification is presented using dual-probe fluorescence imaging, where an endothelial growth factor receptor (EGFR) fluorescent probe was paired with a non-targeted probe before being injected, and tracer kinetic compartmental modeling was used to determine EGFR expression in a region of interest from the uptake curves of the two drugs in that region. The approach was tested out in a simulation experiment and then applied in an in vivo study in one mouse to investigate EGFR expression in various tissue types (pancreas, pancreas tumor, and leg). The binding potentials (a unitless correlate of target availability) of EGFR expression in the various tissue types were 8.57, 25.64, and 0.11 for the pancreas, pancreas tumor, respectively. For the pancreas and leg, these results correlate well with expected levels of EGFR expression, with the pancreas demonstrating a much higher expression than the skin and also as expected, the tumor expressed much more EGFR than either healthy tissue.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kenneth M. Tichauer, Kimberley S. Samkoe, Julia O'Hara, Kristian J. Sexton, Scott C. Davis, and Brian W. Pogue "Quantifying receptor density in vivo using a dual probe approach with fluorescence molecular imaging", Proc. SPIE 7965, Medical Imaging 2011: Biomedical Applications in Molecular, Structural, and Functional Imaging, 79650V (9 March 2011); https://doi.org/10.1117/12.891720
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Cited by 3 scholarly publications.
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KEYWORDS
Tumors

Pancreas

Receptors

Tissues

Molecular imaging

Luminescence

Data modeling

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