Targeted drug delivery in cancer treatment has been a major area of development in the past decades. However, there is a great need in preclinical studies to not only assess the drug distribution but also monitor and quantify target engagement in vivo to ensure maximal drug delivery efficacy. Macroscopic Fluorescence Lifetime Imaging of Förster Resonance Energy Transfer (MFLI-FRET) is a unique non-invasive imaging methodology to monitor in vivo receptor-target interactions and directly discriminate between unbound and internalized ligands in pre-clinical studies. In this study, we capitalized on the homodimeric nature of the transferrin receptor (TfR) to quantify transferrin (Tf) internalization into cancer cells by measuring FRET between receptor-bound Tf-labeled donor and acceptor near-infrared (NIR) fluorophore pairs. We found a strong correlation between FRET levels and Tf internalization into tumor cells despite the significant heterogeneity of tumors regarding their size and cellular density. In contrast, no correlation between MFLI-FRET and TfR levels was observed, underscoring the insufficient link between receptor density and intracellular drug delivery. Additionally, we compared results of in vivo MFLI-FRET imaging with intensity-based FRET (using the IVIS pre-clinical optical imaging system). Intensity-based imaging failed to provide reliable and consistent results, showing significantly higher FRET quantification in negative control samples. Overall, we demonstrated that MFLI-FRET enables real time in vivo information on receptor ligand engagement in deep tissues, conversely to current commercial systems. Hence, MFLI-FRET is well positioned to play a critical role in accelerating the optimization of targeted drug delivery efficacy in pre-clinical studies.
Margarida Barroso, Xavier Intes, Nattawut Sinsuebphon, and Alena Rudkouskaya, "Deep tissue imaging of target engagement in live animals (Conference Presentation)," Proc. SPIE 10475, Visualizing and Quantifying Drug Distribution in Tissue II, 104750A (Presented at SPIE BiOS: January 27, 2018; Published: 14 March 2018); https://doi.org/10.1117/12.2290807.5751476054001.
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