Automated reactive accelerated aging for rapid in vitro evaluation of neural implant performance.

Novel therapeutic applications for neural implants require miniaturized devices. Miniaturization imposes stricter requirements for reliability of materials. Pilot clinical studies suggest that rapid failure of the miniaturized neural implants in the body presents a major challenge for this type of technology. Traditional evaluations of neural implant performance over clinically relevant durations present time- and resource-intensive experiments in animals. Reactive accelerated aging (RAA) is an in vitro test platform that was developed to expedite durability testing of neural implants, as a screening technique designed to simulate the aggressive physiological environment experienced by the implants. This approach employs hydrogen peroxide, which mimics reactive oxygen species, and a high temperature to accelerate chemical reactions that lead to device degradation similar to that found with devices implanted in vivo. The original RAA system required daily manual maintenance and was prone to variability in performance. To address these limitations, this work introduces automated reactive accelerated aging (aRAA) with closed-loop monitoring components that make the system simple, robust, and scalable. The core novel technology in the aRAA is electrochemical detection for feedback control of hydrogen peroxide concentration, implemented with simple off-the-shelf components. The aRAA can run multiple parallel experiments for high-throughput device testing and optimization. For this reason, the aRAA provides a simple tool for rapid in vitro evaluation of the durability of neural implants, ultimately expediting the development of a new generation of miniaturized devices with a long functional lifespan.

Public Regulatory Databases as a Source of Insight for Neuromodulation Devices Stimulation Parameters.

OBJECTIVE: The Shannon model is often used to define an expected boundary between non-damaging and damaging modes of electrical neurostimulation. Numerous preclinical studies have been performed by manufacturers of neuromodulation devices using different animal models and a broad range of stimulation parameters while developing devices for clinical use. These studies are mostly absent from peer-reviewed literature, which may lead to this information being overlooked by the scientific community. We aimed to locate summaries of these studies accessible via public regulatory databases and to add them to a body of knowledge available to a broad scientific community. METHODS: We employed web search terms describing device type, intended use, neural target, therapeutic application, company name, and submission number to identify summaries for premarket approval (PMA) devices and 510(k) devices. We filtered these records to a subset of entries that have sufficient technical information relevant to safety of neurostimulation. RESULTS: We identified 13 product codes for 8 types of neuromodulation devices. These led us to devices that have 22 PMAs and 154 510(k)s and six transcripts of public panel meetings. We found one PMA for a brain, peripheral nerve, and spinal cord stimulator and five 510(k) spinal cord stimulators with enough information to plot in Shannon coordinates of charge and charge density per phase. CONCLUSIONS: Analysis of relevant entries from public regulatory databases reveals use of pig, sheep, monkey, dog, and goat animal models with deep brain, peripheral nerve, muscle and spinal cord electrode placement with a variety of stimulation durations (hours to years); frequencies (10-10,000 Hz) and magnitudes (Shannon k from below zero to 4.47). Data from located entries indicate that a feline cortical model that employs acute stimulation might have limitations for assessing tissue damage in diverse anatomical locations, particularly for peripheral nerve and spinal cord simulation.