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

Qi Wang1, Changzhong Chen1, Qian Liu2

  • 1School and Hospital of Stomatology, Guangdong Engineering Research Center of Oral Restoration and Reconstruction, Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Guangzhou Medical University, Guangzhou, China.

Materials Today. Bio
|June 9, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning optimizes cytotoxicity testing for zinc (Zn)-based biomaterials. This improves the reliability of toxicity assessments for Zn-based metals, crucial for developing safe biomedical implants.

Keywords:
CytotoxicityDecision treeMachine learningMultilayer perceptronZn-based metals

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Area of Science:

  • Biomaterials Science
  • Computational Biology
  • Toxicology

Background:

  • Zinc (Zn)-based biodegradable metals show promise for biomedical implants.
  • Discrepancies between in vitro and in vivo biocompatibility data complicate Zn-based metal evaluation.

Purpose of the Study:

  • To optimize cytotoxicity testing protocols for Zn-based metals using machine learning.
  • To enhance the reliability of toxicity assessments for Zn-based biomaterials.

Main Methods:

  • Utilized data from 51 cytotoxicity experiments on pure Zn.
  • Trained and refined five predictive models: decision tree (DT), random forest, gradient boosted decision tree, support vector machine, and multilayer perceptron (MLP).
  • Assessed the impact of pure Zn on bone-related cells, endothelial cells, and fibroblasts.

Main Results:

  • Optimized predictive models showed comparable performance.
  • The multilayer perceptron (MLP) model indicated high non-toxicity probability for all cell types below 40% Zn concentration.
  • Extract concentration identified by the decision tree (DT) model was a critical predictive factor.
  • Cytotoxicity tests confirmed high cell survival rates up to 40% Zn extract concentration, with significant decline thereafter.

Conclusions:

  • The study provides innovative insights into cytotoxicity testing for Zn-based biomaterials.
  • Identified key factors influencing cytotoxicity assessments and defined limits for in vitro evaluations.
  • Enhances the reliability of toxicity assessments and supports a standardized framework for evaluation metrics for Zn-based biodegradable metals.