Material Design of Porous Hydroxyapatite Ceramics via Inverse Analysis of an Estimation Model for Bone-Forming Ability Based on Machine Learning and Experimental Validation of Biological Hard Tissue Responses

Author:

Horikawa Shota1,Suzuki Kitaru1ORCID,Motojima Kohei1,Nakano Kazuaki2,Nagaya Masaki2ORCID,Nagashima Hiroshi23ORCID,Kaneko Hiromasa14,Aizawa Mamoru14ORCID

Affiliation:

1. Department of Applied Chemistry, School of Science and Technology, Meiji University, 1-1-1 Higashimita, Tama-ku, Kawasaki 214-8571, Kanagawa, Japan

2. Meiji University International Institute for Bio-Resource Research, 1-1-1 Higashimita, Tama-ku, Kawasaki 214-8571, Kanagawa, Japan

3. Department of Life Sciences, School of Agriculture, Meiji University, 1-1-1 Higashimita, Tama-ku, Kawasaki 214-8571, Kanagawa, Japan

4. Meiji University International Institute for Materials with Life Functions, 1-1-1, Higashimita, Tama-ku, Kawasaki 214-8571, Kanagawa, Japan

Abstract

Hydroxyapatite and β-tricalcium phosphate have been clinically applied as artificial bone materials due to their high biocompatibility. The development of artificial bones requires the verification of safety and efficacy through animal experiments; however, from the viewpoint of animal welfare, it is necessary to reduce the number of animal experiments. In this study, we utilized machine learning to construct a model that estimates the bone-forming ability of bioceramics from material fabrication conditions, material properties, and in vivo experimental conditions. We succeeded in constructing two models: ‘Model 1′, which predicts material properties from their fabrication conditions, and ‘Model 2′, which predicts the bone-formation rate from material properties and in vivo experimental conditions. The inclusion of full width at half maximum (FWHM) in the feature of Model 2 showed an improvement in accuracy. Furthermore, the results of the feature importance showed that the FWHMs were the most important. By an inverse analysis of the two models, we proposed candidates for material fabrication conditions to achieve target values of the bone-formation rate. Under the proposed conditions, the material properties of the fabricated material were consistent with the estimated material properties. Furthermore, a comparison between bone-formation rates after 12 weeks of implantation in the porcine tibia and the estimated bone-formation rate. This result showed that the actual bone-formation rates existed within the error range of the estimated bone-formation rates, indicating that machine learning consistently predicts the results of animal experiments using material fabrication conditions. We believe that these findings will lead to the establishment of alternative animal experiments to replace animal experiments in the development of artificial bones.

Funder

Japan Society for the Promotion of Science

Publisher

MDPI AG

Subject

General Materials Science

Reference33 articles.

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