Doctor of Philosophy, Illinois Institute Of Technology (2018)
Bachelor of Science, Sharif University of Technology (2013)
We report the design, synthesis, and evaluation of polyaminocarboxylate ligand-based antibody conjugates for potential application in targeted cancer therapy and near-infrared (NIR) fluorescence imaging. We synthesized a new polyaminocarboxylate chelate (CAB-NE3TA) as a potential anticancer agent. CAB-NE3TA displayed potent inhibitory activities against various cancer cell lines. We then designed a multifunctional theranostic platform (CAB-NE3TA-PAN-IR800) constructed on an epidermal growth factor receptor (EGFR)-targeted antibody (panitumumab, PAN) labeled with a NIR fluorescent dye. We also built the first atomistic model of the EGFR-PAN complex and loaded it with the cytotoxic CAB-NE3TA and the NIR dye. The therapeutic (CAB-NE3TA-PAN) and theranostic (CAB-NE3TA-PAN-IR800) conjugates were evaluated using an EGFR-positive A431 (human skin cancer) cell xenograft mouse model. Biodistribution studies using NIR fluorescence imaging demonstrated that the CAB-NE3TA-PAN labeled with the IR800 dye selectively targeted the A431 tumors in mice and resulted in prolonged retention in the tumor tissue and displayed excellent clearance in blood and normal organs. The therapeutic conjugate was capable of significantly inhibiting tumor growth, leading to nearly complete disappearance of tumors in the mice. The results of our pilot in vivo studies support further evaluation of the novel ligand-based therapeutic and theranostic conjugates for targeted iron chelation cancer therapy and imaging applications.
View details for DOI 10.1002/cmdc.201800598
View details for PubMedID 30403833
View details for PubMedCentralID PMC6324731
Paired-agent kinetic modeling protocols provide one means of estimating cancer cell-surface receptors with in vivo molecular imaging. The protocols employ the coadministration of a control imaging agent with one or more targeted imaging agent to account for the nonspecific uptake and retention of the targeted agent. These methods require the targeted and control agent data be converted to equivalent units of concentration, typically requiring specialized equipment and calibration, and/or complex algorithms that raise the barrier to adoption. This work evaluates a kinetic model capable of correcting for targeted and control agent signal differences. This approach was compared with an existing simplified paired-agent model (SPAM), and modified SPAM that accounts for signal differences by early time point normalization of targeted and control signals (SPAMPN). The scaling factor model (SPAMSF) outperformed both SPAM and SPAMPN in terms of accuracy and precision when the scale differences between targeted and imaging agent signals (α) were not equal to 1, and it matched the performance of SPAM for α = 1. This model could have wide-reaching implications for quantitative cancer receptor imaging using any imaging modalities, or combinations of imaging modalities, capable of concurrent detection of at least two distinct imaging agents (e.g., SPECT, optical, and PET/MR).
View details for DOI 10.1117/1.JBO.23.6.066004
View details for PubMedID 29931837
View details for PubMedCentralID PMC6013418
Dynamic fluorescence imaging approaches can be used to estimate the concentration of cell surface receptors in vivo. Kinetic models are used to generate the final estimation by taking the targeted imaging agent concentration as a function of time. However, tissue absorption and scattering properties cause the final readout signal to be on a different scale than the real fluorescent agent concentration. In paired-agent imaging approaches, simultaneous injection of a suitable control imaging agent with a targeted one can account for non-specific uptake and retention of the targeted agent. Additionally, the signal from the control agent can be a normalizing factor to correct for tissue optical property differences. In this study, the kinetic model used for paired-agent imaging analysis (i.e., simplified reference tissue model) is modified and tested in simulation and experimental data in a way that accounts for the scaling correction within the kinetic model fit to the data to ultimately extract an estimate of the targeted biomarker concentration.
View details for DOI 10.1117/12.2290631
View details for PubMedID 30220772
View details for PubMedCentralID PMC6136426
New precision medicine drugs oftentimes act through binding to specific cell-surface cancer receptors, and thus their efficacy is highly dependent on the availability of those receptors and the receptor concentration per cell. Paired-agent molecular imaging can provide quantitative information on receptor status in vivo, especially in tumor tissue; however, to date, published approaches to paired-agent quantitative imaging require that only 'trace' levels of imaging agent exist compared to receptor concentration. This strict requirement may limit applicability, particularly in drug binding studies, which seek to report on a biological effect in response to saturating receptors with a drug moiety. To extend the regime over which paired-agent imaging may be used, this work presents a generalized simplified reference tissue model (GSRTM) for paired-agent imaging developed to approximate receptor concentration in both non-receptor-saturated and receptor-saturated conditions. Extensive simulation studies show that tumor receptor concentration estimates recovered using the GSRTM are more accurate in receptor-saturation conditions than the standard simple reference tissue model (SRTM) (% error (mean ± sd): GSRTM 0 ± 1 and SRTM 50 ± 1) and match the SRTM accuracy in non-saturated conditions (% error (mean ± sd): GSRTM 5 ± 5 and SRTM 0 ± 5). To further test the approach, GSRTM-estimated receptor concentration was compared to SRTM-estimated values extracted from tumor xenograft in vivo mouse model data. The GSRTM estimates were observed to deviate from the SRTM in tumors with low receptor saturation (which are likely in a saturated regime). Finally, a general 'rule-of-thumb' algorithm is presented to estimate the expected level of receptor saturation that would be achieved in a given tissue provided dose and pharmacokinetic information about the drug or imaging agent being used, and physiological information about the tissue. These studies suggest that the GSRTM is necessary when receptor saturation exceeds 20% and highlight the potential for GSRTM to accurately measure receptor concentrations under saturation conditions, such as might be required during high dose drug studies, or for imaging applications where high concentrations of imaging agent are required to optimize signal-to-noise conditions. This model can also be applied to PET and SPECT imaging studies that tend to suffer from noisier data, but require one less parameter to fit if images are converted to imaging agent concentration (quantitative PET/SPECT).
View details for DOI 10.1088/1361-6560/62/2/394
View details for PubMedID 27997381
View details for PubMedCentralID PMC5226886