Sadeghian Dehkord Ehsan, Kerckhofs Greet, Compère Philippe, Lambert France, Geris Liesbet
GIGA In Silico Medicine, Biomechanics Research Unit (Biomech), University of Liège, 4000 Liège, Belgium.
Prometheus, Division for Skeletal Tissue Engineering, Katholieke Universiteit Leuven, 3000 Leuven, Belgium.
J Funct Biomater. 2023 Jul 22;14(7):388. doi: 10.3390/jfb14070388.
Facial trauma, bone resection due to cancer, periodontal diseases, and bone atrophy following tooth extraction often leads to alveolar bone defects that require bone regeneration in order to restore dental function. Guided bone regeneration using synthetic biomaterials has been suggested as an alternative approach to autologous bone grafts. The efficiency of bone substitute materials seems to be influenced by their physico-chemical characteristics; however, the debate is still ongoing on what constitutes optimal biomaterial characteristics. The purpose of this study was to develop an empirical model allowing the assessment of the bone regeneration potential of new biomaterials on the basis of their physico-chemical characteristics, potentially giving directions for the design of a new generation of dental biomaterials. A quantitative data set was built composed of physico-chemical characteristics of seven commercially available intra-oral bone biomaterials and their response. This empirical model allowed the identification of the construct parameters driving optimized bone formation. The presented model provides a better understanding of the influence of driving biomaterial properties in the bone healing process and can be used as a tool to design bone biomaterials with a more controlled and custom-made composition and structure, thereby facilitating and improving the clinical translation.
面部创伤、因癌症进行的骨切除、牙周疾病以及拔牙后的骨萎缩常常导致牙槽骨缺损，而这需要进行骨再生以恢复牙齿功能。使用合成生物材料的引导性骨再生已被提议作为自体骨移植的替代方法。骨替代材料的效率似乎受其物理化学特性的影响；然而，关于何种特性构成最佳生物材料特性的争论仍在继续。本研究的目的是建立一个经验模型，以便根据新生物材料的物理化学特性评估其骨再生潜力，这可能为新一代牙科生物材料的设计提供指导。构建了一个定量数据集，该数据集由七种市售口腔内骨生物材料的物理化学特性及其反应组成。这个经验模型能够识别驱动优化骨形成的构建参数。所提出的模型能更好地理解在骨愈合过程中驱动生物材料特性的影响，并且可用作一种工具来设计具有更可控和定制化组成及结构的骨生物材料，从而促进和改进临床转化。
