Temporal and spatial molecular imaging of disease biomarkers using non-invasive MRI with high resolution is largely limited by lack of MRI contrast agents with high sensitivity, high specificity, optimized biodistribution and pharmacokinetics. In this dissertation, I report my Ph. D. work on the development of protein-based MRI contrast agents (ProCAs) specifically targeting different cancer biomarkers, such as grastrin-releasing peptide receptor (GRPR), prostate specific membrane antigen (PSMA), and vascular endothelial growth factor receptor-2 (VEGFR-2). Similar to non-targeted ProCAs, these biomarker-targeted ProCAs exhibit 5 - 10 times higher r1 and r2 relaxivites than that of clinical MRI contrast agents. In addition, these biomarker-targeted ProCAs have high Gd3+ binding affinities and metal selectivities. The highest binding affinity of the three GRPR-targeted contrast reagents obtained by grafting a GRPR ligand binding moiety into ProCA32 for GRPR is 2.7 x 10-9 M. We further demonstrate that GRPR-targeted ProCAs were able to semi-quantitatively evaluate GRPR expression levels in xenograft mice model by MRI. In addition, we have also created a PSMA-targeted ProCA which has a binding affinity to PSMA biomarker of 5.2 x 10-7 M. Further, we developed VEGFR-targeted contrast agent which is able to image VEGFR2 in mice models using T1-weighted and T2-weighted sequences. Moreover, the relaxivities and coordination water numbers of ProCAs can be tuned by protein design of ProCA4. Since disease biomarkers are expressed in various tumors and diseases, our results may have strong preclinical and clinical implications for the diagnosis and therapeutics of cancer and other type of diseases.
| Identifer | oai:union.ndltd.org:GEORGIA/oai:scholarworks.gsu.edu:chemistry_diss-1110 |
| Date | 18 December 2014 |
| Creators | Pu, Fan |
| Publisher | ScholarWorks @ Georgia State University |
| Source Sets | Georgia State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Chemistry Dissertations |
Page generated in 0.0017 seconds