Publication date: 28th June 2024
Neural implants have significantly improved the lives of individuals with spinal cord injuries, Parkinson’s disease, and hearing loss. However, current implants are often large, complex, and invasive, which limits their accessibility. To address this, we are developing injectable and wireless nanoelectrodes made from magnetoelectric materials that are less invasive and risky. These magnetoelectric nanoparticles (MENPs) convert magnetic signals into electrical signals to stimulate nerve tissue without the need for genetic modification [1]. MENPs represent an advanced technology in the field of bioelectronics and hold significant potential for applications such as deep brain stimulation (DBS). They offer a less invasive alternative to traditional methods for modulating neural activity, providing a safer option for patients.
The properties of MENPs are heavily dependent on their surface coatings, which play a crucial role in ensuring their stability, biocompatibility, and functionality. Effective coatings not only prevent the aggregation of nanoparticles, which could impair their function and cause health issues, but also enable targeted control of their interaction with surrounding tissues. Additionally, these coatings minimize immune responses, enhancing the biocompatibility of the nanoparticles and reducing potential side effects [2, 3, 4]
In our study, we coated MENPs with various polymers. Two polymers, oleic acid-polyethylene glycol (OA-PEG) and polylactide-PEG (PLA-PEG), were non-covalently bound to the nanoparticles through hydrophobic interactions. Furthermore, we covalently attached four additional polymers – mPEG, poly(acrylic acid-co-2-acrylamido-2-methyl-1-propane sulfonic acid) (P(AA/AMPS)), polyethyleneimine (PEI), and poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) – to our MENPs. We demonstrated that all coatings improve dispersion in liquids and reduce the aggregation of MENPs. Using Dynamic Light Scattering (DLS), we demonstrated that the hydrodynamic diameter of coated nanoparticles is approximately 67% smaller due to reduced aggregation. Absorption measurements of a nanoparticle solution revealed that the coated nanoparticles remain significantly longer in solution before sedimentation compared to uncoated ones. This indicates that the coatings enhance colloidal stability and significantly optimize the behavior of MENPs in liquids. NMR measurements confirmed that the coating adheres firmly to the nanoparticles, as we actively removed the coating and measured the pure polymer amount. Additionally, IR measurements further validated the stability and integrity of the coatings. These results also show that the coatings remain stable at 4°C for over 6 months. We also evaluate the biocompatibility of these coatings to assess their suitability for biomedical applications. All of our PEG-based coatings, including OA-PEG, PLA-PEG, and mPEG, showed no toxicity to human neuronal cells derived from hiPSCs in vitro. Additionally, P(AA/AMPS) and PEDOT:PSS coatings also demonstrated no toxicity at relevant concentrations in vitro. In contrast, only PEI exhibited cytotoxicity, as previously described in other studies [5, 6, 7, 8, 9]. This cytotoxicity could potentially be mitigated in future research by reducing the PEI amount, for example, through the use of a PEI-PEG mixture. These advancements in coating technology enhance the performance and safety of MENPs. By optimizing the interaction between MENPs and biological tissues, these coatings increase the potential for safer, more effective, and less invasive treatments, marking a significant advancement in the field of bioelectronics.
The authors would like to acknowledged the Department of Chemical Biology headed by Prof. U. Schepers at the Institute of Functional Interfaces (Karlsruhe Institute of Technology). In particular, that the laboratory and all the equipment could be used for analysis. The authors also thank Aleksandra Milojkovic for synthesizing the Nanoparticles. The authors also thank the Leibniz Supercomputing Centre (LRZ) for providing with the computational infrastructure. The authors would like to thank the Michael J. Fox Foundation, the Munich Institute of Robotics and Machine Intelligence, the Elite Network of Bavaria, and the European Reseach Council for funding.
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