Development of Halofluorochromic Polymer Nanoassemblies for the Potential Detection of Liver Metastatic Colorectal Cancer Tumors Using Experimental and Computational Approaches

Abstract

Purpose: To develop polymer nanoassemblies (PNAs) modified with halofluorochromic dyes to allow for the detection of liver metastatic colorectal cancer (CRC) to improve therapeutic outcomes.

Methods: We combine experimental and computational approaches to evaluate macroscopic and microscopic PNA distributions in patient-derived xenograft primary and orthotropic liver metastatic CRC tumors. Halofluorochromic and non-halofluorochromic PNAs (hfPNAs and n-hfPNAs) were prepared from poly(ethylene glycol), fluorescent dyes (Nile blue, Alexa546, and IR820), and hydrophobic groups (palmitate), all of which were covalently tethered to a cationic polymer scaffold [poly(ethylene imine) or poly(lysine)] forming particles with an average diameter < 30 nm.

Results: Dye-conjugated PNAs showed no aggregation under opsonizing conditions for 24 h and displayed low tissue diffusion and cellular uptake. Both hfPNAs and n-hfPNAs accumulated in primary and liver metastatic CRC tumors within 12 h post intravenous injection. In comparison to n-hfPNAs, hfPNAs fluoresced strongly only in the acidic tumor microenvironment (pH < 7.0) and distinguished small metastatic CRC tumors from healthy liver stroma. Computational simulations revealed that PNAs would steadily accumulate mainly in acidic (hypoxic) interstitium of metastatic tumors, independently of the vascularization degree of the tissue surrounding the lesions.

Conclusion: The combined experimental and computational data confirms that hfPNAs detecting acidic tumor tissue can be used to identify small liver metastatic CRC tumors with improved accuracy.

Keywords: computational tumor simulation; nanoparticle distribution simulation; theranostics; tissue acidity; tumor microenvironment.

Figure 1

Conceptual description of approaches to detect liver metastatic CRC tumors by using halofluorochromic PNAs and computational simulations.

Figure 2

Synthesis scheme for halofluorochromic and non-halofluorochromic PNAs (hfPNAs and n-hfPNAs).

Figure 3

Diffusion of n-hfPNA/IR820 into 0.5% and 1% gels with and without effects of arterial pressure at pH 7.4, 7.0 and 6.0.

Figure 4

Halofluorochromism of hfPNA/NB diffused into gels for 24 hours (A). Fluorescence increases significantly below pH 7.4 for gels at high and low density (B).

Figure 5

Aggregation of PNAs measured by absorbance changes (A). Particle size changes of PNAs after 24 h under aggregating conditions (B).

Figure 6

Cellular uptake imaging of hfPNA^#/NB (A), hfPNA/NB (B) and MG (C). Uptake of PNAs and MG were compared up to 6 h (D). Uptake of hfPNA^#/NB and hfPNA/NB was compared for 0.5 h (E).

Figure 7

Fluorescence imaging of PDX tumors, liver, spleen, kidney and lung from mice that intravenously received n-hfPNA/Alexa546.

Figure 8

Microscopic fluorescence imaging of PDX tumors from mice. Blue represents cell nuclei (DAPI) and red represents n-hfPNA/Alexa546 (A). Tissue samples were also treated with either EpCAM (B) or CD31 antibodies (C), and green represents cancer and endothelial cells in respective images. (Bar = 100 μm)

Figure 9

Fluorescence images of liver, kidney and lung tissue from mice bearing orthotopic liver metastatic CRC tumors treated with n-hfPNA/Alexa546 (A). Fluorescence imaging of liver from healthy mice or mice bearing orthotopic liver metastatic CRC tumors treated with hfPNA/NB (B).

Figure 10

Microscopic fluorescence imaging of liver tissue and liver metastatic tumors treated with n-hfPNA/Alexa546. Blue, green, and red correspond to nucleus, CD31, and PNAs, respectively. (Bar = 100 μm)

Figure 11

Computational simulations of PNA distribution in metastatic tumors at 1.8 h (A) and 12 h (B) after systemic injection of the nanoparticles for three varying degrees of vascularization (low, medium, high) in the surroundings of the tumor. Viable tumor tissue (red) is shown enclosing hypoxic (blue) and necrotic (brown) regions. Capillary network is represented by rectangular grid, with irregular sprouts simulating blood vessel growth via angiogenesis, driven by release of tumor angiogenic factors from the hypoxic tissue within the tumor. PNA concentration is normalized to the maximum value in the vessels. (Bar = 250 μm)

Figure 12

PNA accumulation per vasculature area (μm², based on vessel length and circumference) for various tumor regions (proliferating, hypoxic, necrotic, and blood vessels) simulated for 2.5 days after systemic injection of the nanoparticles. Panels show cases of low (A), medium (B), and high (C) vascularization in the surrounding tissue.

Figure 13

PNA accumulation per vasculature area (μm²) obtained via computational simulation at 1.8 h (A) and 12 h (B) after treatment as functions of tumor region (proliferating, hypoxic, necrotic, blood vessels) and tumor vascularization degree in the surrounding tissue.