Peptides are used to control the pharmacokinetic profiles of nanoparticles due to their ability to influence tissue accumulation and cellular interactions. However, beyond the study of specific peptides, there is a lack of understanding of how peptide physicochemical properties affect nanoparticle pharmacokinetics, particularly in the context of traumatic brain injury (TBI). We engineered nanoparticle surfaces with peptides that possess a range of physicochemical properties and evaluated their distribution after two routes of administration: direct injection into a healthy mouse brain and systemic delivery in a mouse model of TBI. In both administration routes, we found that peptide-modified nanoparticle pharmacokinetics were influenced by the charge characteristics of the peptide. When peptide-modified nanoparticles are delivered directly into the brain, nanoparticles modified with positively charged peptides displayed restricted distribution from the injection site compared to nanoparticles modified with neutral, zwitterionic, or negatively charged peptides. After intravenous administration in a TBI mouse model, positively charged peptide-modified nanoparticles accumulated more in off-target organs, including the heart, lung, and kidneys, than zwitterionic, neutral, or negatively charged peptide-modified nanoparticles. The increase in off-target organ accumulation of positively charged peptide-modified nanoparticles was concomitant with a relative decrease in accumulation in the injured brain compared to zwitterionic, neutral, or negatively charged peptide-modified nanoparticles. Understanding how nanoparticle pharmacokinetics are influenced by the physicochemical properties of peptides presented on the nanoparticle surface is relevant to the development of nanoparticle-based TBI therapeutics and broadly applicable to nanotherapeutic design, including synthetic nanoparticles and viruses. Graphical abstract

Current screening and diagnostic tools for traumatic brain injury (TBI) have limitations in sensitivity and prognostication. Aberrant protease activity is a central process that drives disease progression in TBI and is associated with worsened prognosis, thus direct measurements of protease activity can provide more diagnostic information. In this study, a nanosensor is engineered to release a measurable signal into the blood and urine in response to activity from the TBI‐associated protease calpain. Readouts from the nanosensor are designed to be compatible with ELISA and lateral flow assays, clinically‐relevant assay modalities. In a mouse model of TBI, the nanosensor sensitivity is enhanced when ligands that target hyaluronic acid are added. In evaluation of mice with mild or severe injuries, the nanosensor identifies mild TBI with a higher sensitivity than the biomarker glial fibrillary acidic protein (GFAP). This nanosensor technology allows for measurement of TBI‐associated proteases without the need to directly access brain tissue and has the potential to complement existing TBI diagnostic tools.

Current screening and diagnostic tools for traumatic brain injury (TBI) have limitations in sensitivity and prognostication. Aberrant protease activity is a central process that drives disease progression in TBI and is associated with worsened prognosis; thus direct measurements of protease activity could provide more diagnostic information. In this study, we engineered a nanosensor that releases a measurable signal into the blood and urine in response to activity from the TBI-associated protease calpain. Readouts from our nanosensor were designed to be compatible with ELISA and lateral flow assays, clinically-relevant assay modalities. In a mouse model of TBI, we demonstrated greater sensitivity of the nanosensor with the addition of targeting ligands to hyaluronic acid. In evaluation of mice with mild or severe injuries, our nanosensor identified mild TBI with a higher sensitivity than the clinical biomarker GFAP. This nanosensor technology allows for measurement of TBI-associated proteases without the need to directly access brain tissue, and has the potential to complement existing TBI diagnostic tools.

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