Development and calibration of biochemical models for testing dental restorations

Abstract

Currently, resin composites are the most popular materials for dental restoration in clinical practice. Although the properties of such materials have been improved significantly, together with better clinical techniques used for their placement, early restoration failure still occurs too frequently. As clinical studies take years to complete, and new resin composites are being produced at ever increasing pace, laboratory assessment using accelerated but representative tests is necessary. The main types of failure in resin-composite restoration are tooth/restoration fracture and secondary caries, which are caused by a combination of mechanical and biochemical challenges. In this study, a biofilm model (S. mutans) and a chemical model (lactic-acid buffer) for producing artificial caries in bovine dentin are developed and calibrated against in situ data. Using a power law relationship between the demineralization depth and challenge duration, scale factors that convert the in vitro durations to the equivalent clinical durations are determined for different pH values for each model. The scale factors will allow the synchronization of biochemical and mechanical challenges in terms of their rates of action to potentially test resin-composite restoration in an accelerated but clinically representative manner. STATEMENT OF SIGNIFICANCE: Although the properties of resin composites for dental restoration have been improved significantly, early restoration failure still occurs too frequently. As clinical studies take years to complete, accelerated laboratory testing is necessary. Resin-composite restoration fail mainly through fracture and secondary caries, caused by a combination of mechanical and biochemical challenges. In this study, a biofilm and a chemical model for producing artificial caries in bovine dentin are calibrated against in situ data. Using a power law relationship between demineralization depth and challenge duration, scale factors are determined for different pH for each model. The scale factors will allow the synchronization of biochemical and mechanical challenges in testing resin-composite restoration in an accelerated but clinically representative manner.

Keywords: Biofilm model; Chemical model; Demineralization; Dentin.

Figure 1.

Determination of the mean total demineralization determination depth. (a) An example of Gaussian fit of the background (inner-hole region) grey value histogram of a dentin disc sample. (b) The region of interest isolated from a dentin disc used for demineralization depth calculation. (c) Schematic diagram showing the effective radius of the inner-hole. (d) Top view of a dentin disc where the background was isolated by applying a threshold of $\overline{G}+3\sigma$. (e) Zoo-min view of the background-dentin boundary. (f) Zoom-in view of the demineralized dentin-sound dentin boundary. (g) Top view of the dentin disc where the sound dentin was isolated by applying a threshold of $\overline{G}-3\sigma$.

Figure 2.

pH data over test duration for (a) biofilm model and (b) chemical model

Figure 3.

Relationships between demineralization depth and duration for (a) biofilm challenge and (b) chemical challenge.

Figure 4.

(a) Fluorescent image showing penetration of protein-labeled S. mutans into bovine dentin disc. (b) Penetration depth as a function of time and pH. (c) SEM image showing the presence of bacteria on dentin disc surface and within the tubules.

Figure 5.

Linear regression of ln(δ) vs. ln(t) data for (a) biofilm model and (b) chemical model.