In Silico Prediction of the Mechanobiological Response of Arterial Tissue: Application to Angioplasty and Stenting

One way to restore physiological blood flow to occluded arteries involves the deformation of plaque using an intravascular balloon and preventing elastic recoil using a stent. Angioplasty and stent implantation cause unphysiological loading of the arterial tissue, which may lead to tissue in-growth and reblockage; termed “restenosis.” In this paper, a computational methodology for predicting the time-course of restenosis is presented. Stress-induced damage, computed using a remaining life approach, stimulates inflammation (production of matrix degrading factors and growth stimuli). This, in turn, induces a change in smooth muscle cell phenotype from contractile (as exists in the quiescent tissue) to synthetic (as exists in the growing tissue). In this paper, smooth muscle cell activity (migration, proliferation, and differentiation) is simulated in a lattice using a stochastic approach to model individual cell activity. The inflammation equations are examined under simplified loading cases. The mechanobiological parameters of the model were estimated by calibrating the model response to the results of a balloon angioplasty study in humans. The simulation method was then used to simulate restenosis in a two dimensional model of a stented artery. Cell activity predictions were similar to those observed during neointimal hyperplasia, culminating in the growth of restenosis. Similar to experiment, the amount of neointima produced increased with the degree of expansion of the stent, and this relationship was found to be highly dependant on the prescribed inflammatory response. It was found that the duration of inflammation affected the amount of restenosis produced, and that this effect was most pronounced with large stent expansions. In conclusion, the paper shows that the arterial tissue response to mechanical stimulation can be predicted using a stochastic cell modeling approach, and that the simulation captures features of restenosis development observed with real stents. The modeling approach is proposed for application in three dimensional models of cardiovascular stenting procedures.

How can cells sense the elasticity of a substrate? An analysis using a cell tensegrity model

A eukaryotic cell attaches and spreads on substrates, whether it is the extracellular matrix naturally produced by the cell itself, or artificial materials, such as tissue-engineered scaffolds. Attachment and spreading require the cell to apply forces in the nN range to the substrate via adhesion sites, and these forces are balanced by the elastic response of the substrate. This mechanical interaction is one determinant of cell morphology and, ultimately, cell phenotype. In this paper we use a finite element model of a cell, with a tensegrity structure to model the cytoskeleton of actin filaments and microtubules, to explore the way cells sense the stiffness of the substrate and thereby adapt to it. To support the computational results, an analytical 1D model is developed for comparison. We find that (i) the tensegrity hypothesis of the cytoskeleton is sufficient to explain the matrix-elasticity sensing, (ii) cell sensitivity is not constant but has a bell-shaped distribution over the physiological matrix-elasticity range, and (iii) the position of the sensitivity peak over the matrix-elasticity range depends on the cytoskeletal structure and in particular on the F-actin organisation. Our model suggests that F-actin reorganisation observed in mesenchymal stem cells (MSCs) in response to change of matrix elasticity is a structural-remodelling process that shifts the sensitivity peak towards the new value of matrix elasticity. This finding discloses a potential regulatory role of scaffold stiffness for cell differentiation.

A probabilistic modelling scheme for analysis of long-term failure of cemented femoral joint replacements

Reliable prediction of long-term medical device performance using computer simulation requires consideration of variability in surgical procedure, as well as patient-specific factors. However, even deterministic simulation of long-term failure processes for such devices is time and resource consuming so that including variability can lead to excessive time to achieve useful predictions. This study investigates the use of an accelerated probabilistic framework for predicting the likely performance envelope of a device and applies it to femoral prosthesis loosening in cemented hip arthroplasty.
A creep and fatigue damage failure model for bone cement, in conjunction with an interfacial fatigue model for the implant–cement interface, was used to simulate loosening of a prosthesis within a cement mantle. A deterministic set of trial simulations was used to account for variability of a set of surgical and patient factors, and a response surface method was used to perform and accelerate a Monte Carlo simulation to achieve an estimate of the likely range of prosthesis loosening. The proposed framework was used to conceptually investigate the influence of prosthesis selection and surgical placement on prosthesis migration.
Results demonstrate that the response surface method is capable of dramatically reducing the time to achieve convergence in mean and variance of predicted response variables. A critical requirement for realistic predictions is the size and quality of the initial training dataset used to generate the response surface and further work is required to determine the recommendations for a minimum number of initial trials. Results of this conceptual application predicted that loosening was sensitive to the implant size and femoral width. Furthermore, different rankings of implant performance were predicted when only individual simulations (e.g. an average condition) were used to rank implants, compared with when stochastic simulations were used. In conclusion, the proposed framework provides a viable approach to predicting realistic ranges of loosening behaviour for orthopaedic implants in reduced timeframes compared with conventional Monte Carlo simulations.

Application of a mechanobiological simulation technique to stents used clinically

Many cardiovascular diseases are characterised by the restriction of blood flow through arteries. Stents can be expanded within arteries to remove such restrictions; however, tissue in-growth into the stent can lead to restenosis. In order to predict the long-term efficacy of stenting, a mechanobiological model of the arterial tissue reaction to stress is required. In this study, a computational model of arterial tissue response to stenting is applied to three clinically relevant stent designs. We ask the question whether such a mechanobiological model can differentiate between stents used clinically, and we compare these predictions to a purely mechanical analysis. In doing so, we are testing the hypothesis that a mechanobiological model of arterial tissue response to injury could predict the long-term outcomes of stent design. Finite element analysis of the expansion of three different stent types was performed in an idealised, 3D artery. Injury was calculated in the arterial tissue using a remaining-life damage mechanics approach. The inflammatory response to this initial injury was modelled using equations governing variables which represented tissue-degrading species and growth factors. Three levels of inflammation response were modelled to account for inter-patient variability. A lattice-based model of smooth muscle cell behaviour was implemented, treating cells as discrete agents governed by local rules. The simulations predicted differences between stent designs similar to those found in vivo. It showed that the volume of neointima produced could be quantified, providing a quantitative comparison of stents. In contrast, the differences between stents based on stress alone were highly dependent on the choice of comparison criteria. These results show that the choice of stress criteria for stent comparisons is critical. This study shows that mechanobiological modelling may provide a valuable tool in stent design, allowing predictions of their long-term efficacy. The level of inflammation was shown to affect the sensitivity of the model to stent design. If this finding was verified in patients, this could suggest that high-inflammation patients may require alternative treatments to stenting.