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Surface roughness and energy significantly influence protein adsorption on to biomaterials, which, in turn, controls select cellular adhesion to determine the success and longevity of an implant. To understand these relationships at a fundamental level, a model was originally proposed by Khang et al to correlate nanoscale surface properties (specifically, nanoscale roughness and energy) to protein adsorption, which explained the greater cellular responses on nanostructured surfaces commonly reported in the literature today. To test this model for different surfaces from what was previously used to develop that model, in this study we synthesized highly ordered poly(lactic-co-glycolic acid) surfaces of identical chemistry but altered nanoscale surface roughness and energy using poly(dimethylsiloxane) molds of polystyrene beads. Fibronectin and collagen type IV adsorption studies showed a linear adsorption behavior as the surface nanoroughness increased. This supported the general trends observed by Khang et al. However, when fitting such data to the mathematical model established by Khang et al, a strong correlation did not result. Thus, this study demonstrated that the equation proposed by Khang et al to predict protein adsorption should be modified to accommodate for additional nanoscale surface property contributions (ie, surface charge) to make the model more accurate. In summary, results from this study provided an important step in developing future mathematical models that can correlate surface properties (such as nanoscale roughness and surface energy) to initial protein adsorption events important to promote select cellular adhesion. These criteria are critical for the fundamental understanding of the now well-documented increased tissue growth on nanoscale materials.

Using mathematical models to understand the effect of nanoscale roughness on protein adsorption for improving medical devices