Magnesium (Mg) and its alloys are promising materials for temporary bone implants due to their mechanical properties and biocompatibility. The most challenging aspect of Mg-based implants involves adapting the degradation rate to the human body, which requires extensive in vitro and in vivo testing. Given that in vivo tests are significantly more labour-intensive than in vitro and ethics prohibit direct experiments on animals or humans, attempts are commonly undertaken to infer conclusions on in vivo degradation behavior from in vitro experiments. However, there is a wide gap between these tests, and in vitro testing is often a poor predictor of in vivo outcomes. In the development of biodegradable Mg-based implants, considerable efforts are being made to reduce the overall time and cost of in vitro and in vivo testing. Finding a suitable alternative to predict the degradation of Mg alloys, however, remains challenging. We present computational modelling as a possible alternative to bridge the gap between in vitro and in vivo testing, thus reducing overall cost, duration and number of experiments. However, traditional modelling approaches for complex biodegradable systems are still rather time-consuming and require a clear definition of the relations between input parameters and the model result. In this study, Kriging surrogate models based on the peridynamic in vitro degradation model were developed to simulate the degradation behavior for two main alloys, Mg-5Gd and Mg-10Gd, for both in vitro and in vivo cases. Using Kriging surrogate models, the simulation parameters were calibrated to the volume loss data from in vitro and in vivo experiments. In vivo degradation of magnesium has one order of magnitude higher apparent diffusion coefficients than in vitro degradation, thus yielding the higher volume loss observed in vivo than in vitro. On the basis of the diffusivity of the Mg${}^{2+}$ ions modeled under in vitro degradation, Kriging surrogate models were able to simulate the in vivo degradation behavior of Mg-xGd with a ratio between 0.46 and 0.5, indicating that the surrogate-modelling approach is able to bridge the gap between in vitro and in vivo degradation rates for Mg-xGd implants.

The immune system plays an important role in fracture healing, by modulating the pro-inflammatory and anti-inflammatory responses occurring instantly upon injury. An imbalance in these responses can lead to adverse outcomes, such as non-union of fractures. Implants are used to support and stabilize complex fractures. Biodegradable metallic implants offer the potential to avoid a second surgery for implant removal, unlike non-degradable implants. However, considering our dynamic immune system it is important to conduct in-depth studies on the immune response to these implants in living systems. In this study, we investigated the immune response to Mg and Mg-10Gd in vivo in a rat femur fracture model with external fixation. In vivo imaging using liposomal formulations was used to monitor the fluorescence-related inflammation over time. We combine ex vivo methods with our in vivo study to evaluate and understand the systemic and local effects of the implants on the immune response. We observed no significant local or systemic effects in the Mg-10Gd implanted group compared to the SHAM and Mg implanted groups over time. Our findings suggest that Mg-10Gd is a more compatible implant material than Mg, with no adverse effects observed in the early phase of fracture healing during our 4-week study. STATEMENT OF SIGNIFICANCE: Degradable metallic implants in form of Mg and Mg-10Gd intramedullary pins were assessed in a rat femur fracture model, alongside a non-implanted SHAM group with special respect to the potential to induce an inflammatory response. This pre-clinical study combines innovative non-invasive in vivo imaging techniques associated with multimodal, ex vivo cellular and molecular analytics. The study contributes to the development and evaluation of degradable biometals and their clinical application potential. The study results indicate that Mg-10Gd did not exhibit any significant harmful effects compared to the SHAM and Mg groups.

Magnesium alloys are some of the most convenient biodegradable materials for bone fracture treatment due to their tailorable degradation rate, biocompatibility, and mechanical properties resembling those of bone. Despite the fact that magnesium-based implants and ZX00 (Mg-0.45Zn-0.45Ca in wt.%), in particular, have been shown to have suitable degradation rates and good osseointegration, knowledge gaps remain in our understanding of the impact of their degradation properties on the bone’s ultrastructure. Bone is a hierarchically structured material, where not only the microstructure but also the ultrastructure are important as properties like the local mechanical response are determined by it. This study presents the first comparative analysis of bone ultrastructure parameters with high spatial resolution around ZX00 and Ti implants after 6, 12, and 24 weeks of healing. The mineralization was investigated, revealing a significant decrease in the lattice spacing of the (002) Bragg’s peak closer to the ZX00 implant in comparison to Ti, while no significant difference in the crystallite size was observed. The hydroxyapatite platelet thickness and osteon density demonstrated a decrease closer to the ZX00 implant interface. Correlative indentation and strain maps obtained by scanning X-ray diffraction measurements revealed a higher stiffness and faster mechanical adaptation of the bone surrounding Ti implants as compared to the ZX00 ones. Thus, the results suggest the incorporation of Mg²⁺ ions into the bone ultrastructure, as well as a lower degree of remodeling and stiffness of the bone in the presence of ZX00 implants than Ti.

Computational models of electrochemical biodegradation of magnesium (Mg)‐based implants are uncertain. To quantify the model uncertainty, iterative evaluations are needed. This presents a challenge, especially for complex, multiscale models as is the case here. Approximating high‐cost and complex models with easy‐to‐evaluate surrogate models can reduce the computational burden. However, the application of this approach to complex degradation models remains limited and understudied. This work provides a workflow to quantify different types of uncertainty within biodegradation models. Three surrogate models—Kriging, polynomial chaos expansion, and polynomial chaos Kriging—are compared based on the minimum number of samples required for surrogate model construction, surrogate model accuracy, and computational time. The surrogate models are tested for three computational models representing Mg‐based implant biodegradation. Global sensitivity analysis and uncertainty propagation are used to analyze the uncertainties associated with the different models. The findings indicate that Kriging proves effective for calibrating diverse computational models with minimal computational time and cost, while polynomial chaos expansion and polynomial chaos Kriging exhibit greater capability in predicting propagated uncertainties within the computational models.