Bioactive glasses as reactive biomaterials: dissolution-driven structure-function relationships and predictive...

Area of Science:

• Biomaterials Science
• Materials Chemistry
• Biomineralization

Background:

• Bioactive glasses (BGs) are reactive biomaterials whose biological effects are driven by dissolution processes, including ion exchange, pH changes, and surface layer formation.
• BG performance is influenced by a complex interplay of factors beyond nominal composition, such as glass structure, processing methods, crystallinity, surface area, and experimental conditions.

Purpose of the Study:

• To critically review and reframe the bioactivity of BGs as a time-resolved problem involving structure, dissolution, and function.
• To examine the current justification for predictive claims regarding BG performance.
• To provide guidance on experimental reporting and validation for advancing BG research.

Main Methods:

• Narrative review integrating direct BG evidence with relevant studies on non-BG glasses and modeling examples from related fields.
• Analysis of how glass structural features (e.g., network connectivity, Qⁿ speciation, modifier type) influence dissolution behavior and ion release.
• Comparison of different BG systems (silicate, borate, phosphate, sol-gel) based on their dissolution characteristics.

Main Results:

• BG bioactivity is governed by a dynamic dissolution process influenced by structure, surface area, and experimental protocols, rather than a fixed composition.
• Current modeling is most robust for predicting physicochemical outputs like dissolution and ion release under defined conditions.
• Accurate prediction of biological responses (pH, mineralization, cell interactions) requires comprehensive experimental data, including exposure variables and biological metadata.

Conclusions:

• Progress in BG research necessitates improved experimental reporting and validation, rather than solely relying on more complex models.
• Predictive claims for BGs must acknowledge uncertainties and clearly define boundaries, especially regarding in vivo performance.
• A hierarchical evidence approach is recommended, distinguishing direct BG data from related studies and modeling to avoid overstating relevance.