Mechano-regulation of tissue differentiation during fracture healing by deviatoric strain alone is insufficient

Poster 079

Mechanical and mechanobiological influences on bone fracture repair : identifying important cellular characteristics

Fracture repair is a complex and multifactorial process, which involves a well-programmed series of cellular and molecular events that result in a combination of intramembranous and endochondral bone formation. The vast majority of fractures is treated successfully. They heal through ‘secondary healing’, a sequence of tissue differentiation processes, from initial haematoma, to connective tissues, and via cartilage to bone. However, the process can fail and this results in delayed healing or non-union, which occur in 5-10% of all cases. A better understanding of this process would enable the development of more accurate and rational strategies for fracture treatment and accelerating healing. Impaired healing has been associated with a variety of factors, related to the biological and mechanical environments. The local mechanical environment can induce fracture healing or alter its biological pathway by directing the cell and tissue differentiation pathways. The mechanical environment is usually described by global mechanical factors, such as gap size and interfragmentary movement. The relationship between global mechanical factors and the local stresses and strains that influence cell differentiation can be calculated using computational models. In this thesis, mechano-regulation algorithms are used to predict the influence of mechanical stimuli on tissue differentiation during bone healing. These models used can assist in unraveling the basic principles of cell and tissue differentiation, optimization of implant design, and investigation of treatments for non-union and other pathologies. However, this can only be accomplished after the models have been suitably validated. The aim of this thesis is to corroborate mechanoregulatory models, by comparing existing models with well characterized experimental data, identify shortcomings and develop new computational models of bone healing. The underlying hypothesis throughout this work is that the cells act as sensors of mechanical stimuli during bone healing. This directs their differentiation accordingly. Moreover, the cells respond to mechanical loading by proliferation, differentiation or apoptosis, as well as by synthesis or removal of extracellular matrix. In the first part of this work, both well-established and new potential mechano-regulation algorithms were implemented into the same computational model and their capacities to predict the general tissue distributions in normal fracture healing under cyclic axial load were compared. Several algorithms, based on different biophysical stimuli, were equally well able to predict normal fracture healing processes (Chapter 3). To corroborate the algorithms, they were compared with extensive in vivo experimental bone healing data. Healing under two distinctly different mechanical conditions was compared: axial compression or torsional rotation. None of the established algorithms properly predicted the spatial and temporal tissue distributions observed experimentally, for both loading modes and time points. Specific inadequacies with each model were identified. One algorithm, based on deviatoric strain and fluid flow, predicted the experimental results the best (Chapter 4). This algorithm was then employed in further studies of bone regeneration. By including volumetric growth of individual tissue types, it was shown to correctly predict experimentally observed spatial and temporal tissue distributions during distraction osteogenesis, as well as known perturbations due to changes in distraction rate and frequency (Chapter 5). In the second part of this work, a novel ‘mechanistic model’ of cellular activity in bone healing was developed, in which the limitations of previous models were addressed. The formulation included mechanical modulation of cell phenotype and skeletal tissue-type specific activities and rates. This model was shown to correctly predict the normal fracture healing processes, as well as delayed and non-union due to excessive loading, and also the effects of some specific biological perturbations and pathological situations. For example, alterations due to periosteal stripping or impaired cartilage remodeling (endochondral ossification) compared well with experimental observations (Chapter 6). The model requires extensive parametric data as input, which was gathered, as far as possible, from literature. Since many of the parameter magnitudes are not well established, a factorial analysis was conducted using ‘design of experiments’ methods and Taguchi orthogonal arrays. A few cellular parameters were thereby identified as key factors in the process of bone healing. These were related to bone formation, and cartilage production and degradation, which corresponded to those processes that have been suggested to be crucial biological steps in bone healing. Bone healing was found to be sensitive to parameters related to fibrous tissue and cartilage formation. These parameters had optimum values, indicating that some amounts of soft tissue production are beneficial, but too little or too much may be detrimental to the healing process (Chapter 7). The final part of this work focused on the remodeling phase of bone healing. Long bone postfracture remodeling in mice femora was characterized, including a new phenomenon described as ‘dual cortex formation’. The effect of mechanical loading modes on fracturecallus remodeling was evaluated using a bone remodeling algorithm, and it was shown that the distinct remodeling behavior observed in mice, compared to larger mammals, could be explained by a difference in major mechanical loading mode (Chapter 8). In summary, this work has further established the potential of mechanobiological computational models in developing our knowledge of cell and tissue differentiation processes during bone healing in general, and fracture healing and distraction osteogenesis in particular. The studies presented in this thesis have led to the development of more mechanistic models of cell and tissue differentiation and validation approaches have been described. These models can further assist in screening for potential treatment protocols of pathophysiological bone healing

Mechanical and mechanobiological influences on bone fracture repair : identifying important cellular characteristics

Fracture repair is a complex and multifactorial process, which involves a well-programmed series of cellular and molecular events that result in a combination of intramembranous and endochondral bone formation. The vast majority of fractures is treated successfully. They heal through ‘secondary healing’, a sequence of tissue differentiation processes, from initial haematoma, to connective tissues, and via cartilage to bone. However, the process can fail and this results in delayed healing or non-union, which occur in 5-10% of all cases. A better understanding of this process would enable the development of more accurate and rational strategies for fracture treatment and accelerating healing. Impaired healing has been associated with a variety of factors, related to the biological and mechanical environments. The local mechanical environment can induce fracture healing or alter its biological pathway by directing the cell and tissue differentiation pathways. The mechanical environment is usually described by global mechanical factors, such as gap size and interfragmentary movement. The relationship between global mechanical factors and the local stresses and strains that influence cell differentiation can be calculated using computational models. In this thesis, mechano-regulation algorithms are used to predict the influence of mechanical stimuli on tissue differentiation during bone healing. These models used can assist in unraveling the basic principles of cell and tissue differentiation, optimization of implant design, and investigation of treatments for non-union and other pathologies. However, this can only be accomplished after the models have been suitably validated. The aim of this thesis is to corroborate mechanoregulatory models, by comparing existing models with well characterized experimental data, identify shortcomings and develop new computational models of bone healing. The underlying hypothesis throughout this work is that the cells act as sensors of mechanical stimuli during bone healing. This directs their differentiation accordingly. Moreover, the cells respond to mechanical loading by proliferation, differentiation or apoptosis, as well as by synthesis or removal of extracellular matrix. In the first part of this work, both well-established and new potential mechano-regulation algorithms were implemented into the same computational model and their capacities to predict the general tissue distributions in normal fracture healing under cyclic axial load were compared. Several algorithms, based on different biophysical stimuli, were equally well able to predict normal fracture healing processes (Chapter 3). To corroborate the algorithms, they were compared with extensive in vivo experimental bone healing data. Healing under two distinctly different mechanical conditions was compared: axial compression or torsional rotation. None of the established algorithms properly predicted the spatial and temporal tissue distributions observed experimentally, for both loading modes and time points. Specific inadequacies with each model were identified. One algorithm, based on deviatoric strain and fluid flow, predicted the experimental results the best (Chapter 4). This algorithm was then employed in further studies of bone regeneration. By including volumetric growth of individual tissue types, it was shown to correctly predict experimentally observed spatial and temporal tissue distributions during distraction osteogenesis, as well as known perturbations due to changes in distraction rate and frequency (Chapter 5). In the second part of this work, a novel ‘mechanistic model’ of cellular activity in bone healing was developed, in which the limitations of previous models were addressed. The formulation included mechanical modulation of cell phenotype and skeletal tissue-type specific activities and rates. This model was shown to correctly predict the normal fracture healing processes, as well as delayed and non-union due to excessive loading, and also the effects of some specific biological perturbations and pathological situations. For example, alterations due to periosteal stripping or impaired cartilage remodeling (endochondral ossification) compared well with experimental observations (Chapter 6). The model requires extensive parametric data as input, which was gathered, as far as possible, from literature. Since many of the parameter magnitudes are not well established, a factorial analysis was conducted using ‘design of experiments’ methods and Taguchi orthogonal arrays. A few cellular parameters were thereby identified as key factors in the process of bone healing. These were related to bone formation, and cartilage production and degradation, which corresponded to those processes that have been suggested to be crucial biological steps in bone healing. Bone healing was found to be sensitive to parameters related to fibrous tissue and cartilage formation. These parameters had optimum values, indicating that some amounts of soft tissue production are beneficial, but too little or too much may be detrimental to the healing process (Chapter 7). The final part of this work focused on the remodeling phase of bone healing. Long bone postfracture remodeling in mice femora was characterized, including a new phenomenon described as ‘dual cortex formation’. The effect of mechanical loading modes on fracturecallus remodeling was evaluated using a bone remodeling algorithm, and it was shown that the distinct remodeling behavior observed in mice, compared to larger mammals, could be explained by a difference in major mechanical loading mode (Chapter 8). In summary, this work has further established the potential of mechanobiological computational models in developing our knowledge of cell and tissue differentiation processes during bone healing in general, and fracture healing and distraction osteogenesis in particular. The studies presented in this thesis have led to the development of more mechanistic models of cell and tissue differentiation and validation approaches have been described. These models can further assist in screening for potential treatment protocols of pathophysiological bone healing

Sensitivity of tissue differentiation and bone healing predictions to tissue properties

Computational models are employed as tools to investigate possible mechano-regulation pathways for tissue differentiation and bone healing. However, current models do not account for the uncertainty in input parameters, and often include assumptions about parameter values that are not yet established. The aim was to clarify the importance of the assumed tissue material properties in a computational model of tissue differentiation during bone healing. An established mechano-biological model was employed together with a statistical approach. The model included an adaptive 2D finite element model of a fractured long bone. Four outcome criteria were quantified: (1) ability to predict sequential healing events, (2) amount of bone formation at specific time points, (3) total time until healing, and (4) mechanical stability at specific time points. Statistical analysis based on fractional factorial designs first involved a screening experiment to identify the most significant tissue material properties. These seven properties were studied further with response surface methodology in a three-level Box-Behnken design. Generally, the sequential events were not significantly influenced by any properties, whereas rate-dependent outcome criteria and mechanical stability were significantly influenced by Young's modulus and permeability. Poisson's ratio and porosity had minor effects. The amount of bone formation at early, mid and late phases of healing, the time until complete healing and the mechanical stability were all mostly dependent on three material properties; permeability of granulation tissue, Young's modulus of cartilage and permeability of immature bone. The consistency between effects of the most influential parameters was high. To increase accuracy and predictive capacity of computational models of bone healing, the most influential tissue mechanical properties should be accurately quantified. © 2009 Elsevier Ltd. All rights reserved

A mechano-regulatory bone-healing model incorporating cell-phenotype specific activity

Phenomenological computational models of tissue regeneration and bone healing have been only partially successful in predicting experimental observations. This may be a result of simplistic modeling of cellular activity. Furthermore, phenomenological models are limited when considering the effects of combined physical and biological interventions. In this study, a new model of cell and tissue differentiation, using a more mechanistic approach, is presented and applied to fracture repair. The model directly couples cellular mechanisms to mechanical stimulation during bone healing and is based on the belief that the cells act as transducers during tissue regeneration. In the model, the cells within the matrix proliferate, differentiate, migrate, and produce extracellular matrix, all at cell-phenotype specific rates, based on the mechanical stimulation they experience. The model is assembled from coupled partial differentiation equations, which are solved using a newly developed finite element formulation. The evolution of four cell types, i.e. mesenchymal stem cells, fibroblasts, chondrocytes and osteoblasts, and the production of extracellular matrices of fibrous tissue, cartilage and bone are calculated. The material properties of the tissues are iteratively updated based on actual amounts of extracellular matrix in material elements at progressive time points. A two-dimensional finite element model of a long bone osteotomy was used to evaluate the model's potential. The additional value of the presented model and the importance of including cell-phenotype specific activities when modeling tissue differentiation and bone healing, were demonstrated by comparing the predictions with phenomenological models. The model's capacity was established by showing that it can correctly predict several aspects of bone healing, including cell and tissue distributions during normal fracture healing. Furthermore, it was able to predict experimentally established alterations due to excessive mechanical stimulation, periosteal stripping and impaired effects of cartilage remodeling