Modeling of In Vivo Electrochemical Noise: A Computational Framework to Optimize the Corrosion Monitoring of Biodegradable Magnesium Implants
Abstract
Biodegradable magnesium implants offer significant clinical promise, but their safe use requires reliable real-time in vivo monitoring of coating integrity. Existing methods lack sufficient sensitivity and temporal resolution to detect degradation at early stages, and there are no computational tools able to predict the success of a given sensor design before animal experiments. In the present paper, we present BioElectroSynth—a digital simulator of an implantable zero-resistance ammetry (ZRA) corrosion sensor in a mouse model. The simulator combines electrochemical noise, cardiac and muscular bioelectric interference, and instrumental limitations into a unified model, enabling virtual experiments, which mimic the complexity of the in vivo system. Using Monte Carlo analysis, we establish that a 2% breach in a chitosan coating on an AZ91 magnesium alloy electrode is statistically detectable from approximately 30 recordings of 30 s each, and quantify how electrode area, its location, sampling rate, and coating quality jointly determine detection sensitivity. The framework provides the first quantitative tool for predicting in vivo experiment feasibility from standard in vitro electrochemical data alone. By identifying instrument and design configurations that are statistically underpowered before any animal use, the approach directly supports the 3R principles of humane research.
How strong is the evidence?
Evidence Hub reads this as a simulation / modelling, done in animal, not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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