In Vivo Quantitative Detection of PEGylated Macromolecules by Magnetic Resonance Spectroscopy

Area of Science:

• Biomaterials Science
• Biomedical Engineering
• Analytical Chemistry

Background:

• Poly(ethylene) glycol (PEG) conjugation enhances biomolecule stability and biodistribution for therapeutics and drug delivery.
• PEG's unique properties allow its use as a noninvasive reporter for tracking biologics in vivo via magnetic resonance spectroscopy (MRS).
• Filtering PEG signals from water is crucial for effective in vivo tracking using MRS.

Purpose of the Study:

• To compare the efficacy of two magnetic resonance spectroscopy (MRS) techniques for tracking poly(ethylene) glycol (PEG)-conjugated constructs in vivo.
• To evaluate the biodistribution and clearance rates of different PEGylated biomolecules and nanoparticles.
• To determine the optimal PEG chain size for in vivo tracking using diffusion-edited MRS.

Main Methods:

• Developed and compared ¹³C-edited ¹H MRS and stimulated echo acquisition mode (STEAM) MRS for tracking PEGylated constructs.
• Prepared and intravenously administered ¹³C-enriched PEG alone, PEG-bovine serum albumin (BSA), and PEG-poly(lactic acid) (PLA) nanoparticles to rat models.
• Utilized diffusion-edited ¹H MRS with varying PEG chain lengths (2, 20, and 40 kDa) to assess sensitivity and water suppression.

Main Results:

• ¹³C-edited ¹H MRS successfully monitored PEGylated constructs in vivo, revealing distinct clearance phases.
• Half-lives were determined: 38.6 min for PEG alone, 23.4 h for PEG-BSA, and 11.9 h for PEG-PLA nanoparticles.
• Larger molecular weight PEG chains (40 kDa) demonstrated superior performance in diffusion-edited ¹H MRS due to reduced signal inhomogeneity and longer T₂ relaxation times.

Conclusions:

• Both ¹³C-edited ¹H MRS and STEAM-MRS are viable methods for in vivo tracking of PEGylated constructs.
• PEG-PLA nanoparticle clearance is faster than PEG-BSA, likely due to PLA degradation.
• Larger PEG chains are more effective reporters for in vivo tracking applications using diffusion-edited MRS.