Machine learning-based optimization of cytotoxicity testing for assessing Zn-based biodegradable metals

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Zinc (Zn)-based biodegradable metals are emerging as promising candidates for biomedical implants. Nonetheless, discrepancies between in vitro and in vivo biocompatibility findings for these metals often complicate their evaluation. This study aims to optimize cytotoxicity testing for Zn-based metals using machine learning techniques. Data from 51 cytotoxicity experiments on pure Zn are utilized to train and refine five predictive models, i.e., decision tree (DT), random forest, gradient boosted decision tree, support vector machine, and multilayer perceptron. In addition, the impact of biodegradable pure Zn samples on the viability of bone-related cells, endothelial cells, and fibroblasts are assessed. The models are optimized for comparable performance, with the MLP model revealing that bone-related cells had a lower probability of non-toxicity compared to vascular cells across all zinc extract concentrations. Importantly, at concentrations below 40%, all cell types demonstrate a high probability of non-toxicity. The “Extract concentration” by the DT model as a critical predictive factor. Cytotoxicity tests confirm that the cell survival rates remain high at Zn extract concentrations up to 40%, beyond which cell activity significantly declines. This research offers innovative insights into the cytotoxicity testing protocols for Zn-based biomaterials, elucidating key factors that affect cytotoxicity assessments and defining the limits of in vitro evaluations. Lastly, this study enhances the reliability of toxicity assessments and supports the development of a standardized framework for evaluation metrics. • Machine learning can improve cytotoxicity testing for Zn-based biometals. • MLP model indicates lower cytotoxicity responses in bone-related cells. • Decision Tree identifies “Extract concentration” as a critical factor. • A reliable cytotoxic threshold for Zn extracts is established at 40%. • Findings support revisions to ISO standards for Zn-based biometals.