Endotoxin-Imprinted Polymers: A Novel Theranostic Solution to Gram Negative Sepsis

Overview

Endotoxins, or lipopolysaccharides (LPS), are harmful biomolecules excreted by gram-negative bacteria. They are characterized by Lipid A, a toxic component that interacts with LBP (LPS-binding protein) to initiate septic cascade, causing possibly fatal inflammation. Past efforts to detect and extract LPS from blood and pharmaceutical solutions have been hampered by the interference of certain chemicals, high synthesis costs, or slow processing times. In this research, the lock-and-key mechanism of molecularly imprinted polymers was utilized to design selective fluorophore-conjugated nanoparticles that are not degraded by their environment, inhibit the formation of the LPS-LBP complex, and are capable of simultaneous LPS detection and neutralization. A novel polymer matrix was designed by utilizing GROMACS (molecular dynamics) and Autodock software to optimize LPS affinity and simulate several template-monomer interactions. Analysis of system stability, binding energy, and the number of non-bonding interactions indicate that itaconic acid copolymerizes with EGDMA to form the most effective molecular imprints for LPS. These computational results were tested experimentally through the synthesis of MIP nanoparticles by precipitation polymerization. Characterization using FTIR, SEM, and fluorescence spectroscopy shows the formation of LPS-specific bonds between the template and functional monomers, as well as a correlation between fluorescence and LPS concentration (p<0.05). Based on procedural comparisons to the standard LAL assay, these nanoparticles are able to detect and bind to endotoxins for a fraction of the cost, showing strong potential for application as theranostic agents in vivo and selective separation in pharmaceutical solutions to decrease the effects of sepsis and LPS contamination worldwide. Ongoing investigations include nanoparticle rebinding efficiency and biocompatibility testing.

Competition history

• AJAS 2017 Category not listed

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