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The healing of bone defects is a challenge for both tissue engineering and modern orthopaedics. This problem has been addressed through the study of scaffold constructs combined with mechanoregulatory theories, disregarding the influence of chemical factors and their respective delivery devices. Of the chemical factors involved in the bone healing process, bone morphogenetic protein-2 (BMP-2) has been identified as one of the most powerful osteoinductive proteins. The aim of this work is to develop and validate a mechano-chemical regulatory model to study the effect of BMP-2 on the healing of large bone defects in silico. We first collected a range of quantitative experimental data from the literature concerning the effects of BMP-2 on cellular activity, specifically proliferation, migration, differentiation, maturation and extracellular matrix production. These data were then used to define a model governed by mechano-chemical stimuli to simulate the healing of large bone defects under the following conditions: natural healing, an empty hydrogel implanted in the defect and a hydrogel soaked with BMP-2 implanted in the defect. For the latter condition, successful defect healing was predicted, in agreement with previous in vivo experiments. Further in vivo comparisons showed the potential of the model, which accurately predicted bone tissue formation during healing, bone tissue distribution across the defect and the quantity of bone inside the defect. The proposed mechano-chemical model also estimated the effect of BMP-2 on cells and the evolution of healing in large bone defects. This novel in silico tool provides valuable insight for bone tissue regeneration strategies.

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The main goal of this numerical study is to assess the impact of geometric design perturbations on the performance of a representative coronary stent platform. In this context, first, a design parameterization model was defined for the stent under study. After, a set of metrics characterizing stent performance, namely, vessel injury, radial recoil, bending resistance, longitudinal resistance, radial strength, the risk of fracture, prolapse index, and dogboning were evaluated within the context of a finite element analysis. Afterwards, accurate surrogate models were developed, using the efficient global optimization algorithm, as predictive tools in the execution of tasks that normally require a high number of model evaluations, such as global sensitivity analysis and visualization. In the end, the dependence of the output response surfaces on the geometric parameters was mechanically interpreted, which allowed us to understand the complex interplay that exists between the considered design variables and the defined performance metrics.

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