Mechanobiological modeling can explain orthodontic tooth movement: three case studies.
Van Schepdael, An ; ; Geris, Liesbet
in Journal of Biomechanics (2013), 46(3), 470-7
Progress in medicine and higher expectation of quality of life has led to a higher demand for several dental and medical treatments. This increases the occurrence of situations in which orthodontic ... [more ▼]
Progress in medicine and higher expectation of quality of life has led to a higher demand for several dental and medical treatments. This increases the occurrence of situations in which orthodontic treatment is complicated by pathological conditions, medical therapies and drugs. Together with experiments, computer models might lead to a better understanding of the effect of pathologies and medical treatment on tooth movement. This study uses a previously presented mechanobiological model of orthodontic tooth displacement to investigate the effect of pathologies and (medical) therapies on the result of orthodontic treatment by means of three clinically relevant case studies looking at the effect of estrogen deficiency, the effect of OPG injections and the influence of fluoride intake. When less estrogen was available, the model predicted bone loss and a rise in the number of osteoclasts present at the compression side, and a faster bone resorption. These effects were also observed experimentally. Experiments disagreed on the effect of estrogen deficiency on bone formation, while the mechanobiological model predicted very little difference between the pathological and the non-pathological case at formation sites. The model predicted a decrease in tooth movement after OPG injections or fluoride intake, which was also observed in experiments. Although more experiments and model analysis is needed to quantitatively validate the mechanobiological model used in this study, its ability to conceptually describe several pathological conditions is an important measure for its validity. [less ▲]Detailed reference viewed: 13 (1 ULg)
A mechanobiological model of orthodontic tooth movement.
; ; Geris, Liesbet
in Biomechanics & Modeling in Mechanobiology (2012)
Orthodontic tooth movement is achieved by the process of repeated alveolar bone resorption on the pressure side and new bone formation on the tension side. In order to optimize orthodontic treatment, it ... [more ▼]
Orthodontic tooth movement is achieved by the process of repeated alveolar bone resorption on the pressure side and new bone formation on the tension side. In order to optimize orthodontic treatment, it is important to identify and study the biological processes involved. This article presents a mechanobiological model using partial differential equations to describe cell densities, growth factor concentrations, and matrix densities occurring during orthodontic tooth movement. We hypothesize that such a model can predict tooth movement based on the mechanobiological activity of cells in the PDL. The developed model consists of nine coupled non-linear partial differential equations, and two distinct signaling pathways were modeled: the RANKL-RANK-OPG pathway regulating the communication between osteoblasts and osteoclasts and the TGF-beta pathway mediating the differentiation of mesenchymal stem cells into osteoblasts. The predicted concentrations and densities were qualitatively validated by comparing the results to experiments reported in the literature. In the current form, the model supports our hypothesis, as it is capable of conceptually simulating important features of the biological interactions in the alveolar bone-PDL complex during orthodontic tooth movement. [less ▲]Detailed reference viewed: 22 (6 ULg)
Computational modelling of biomaterial surface interactions with blood platelets and osteoblastic cells for the prediction of contact osteogenesis.
; Geris, Liesbet ; et al
in Acta Biomaterialia (2011), 7(2), 779-90
Surface microroughness can induce contact osteogenesis (bone formation initiated at the implant surface) around oral implants, which may result from different mechanisms, such as blood platelet ... [more ▼]
Surface microroughness can induce contact osteogenesis (bone formation initiated at the implant surface) around oral implants, which may result from different mechanisms, such as blood platelet-biomaterial interactions and/or interaction with (pre-)osteoblast cells. We have developed a computational model of implant endosseous healing that takes into account these interactions. We hypothesized that the initial attachment and growth factor release from activated platelets is crucial in achieving contact osteogenesis. In order to investigate this, a computational model was applied to an animal experiment  that looked at the effect of surface microroughness on endosseous healing. Surface-specific model parameters were implemented based on in vitro data (Lincks et al. Biomaterials 1998;19:2219-32). The predicted spatio-temporal patterns of bone formation correlated with the histological data. It was found that contact osteogenesis could not be predicted if only the osteogenic response of cells was up-regulated by surface microroughness. This could only be achieved if platelet-biomaterial interactions were sufficiently up-regulated as well. These results confirmed our hypothesis and demonstrate the added value of the computational model to study the importance of surface-mediated events for peri-implant endosseous healing. [less ▲]Detailed reference viewed: 9 (2 ULg)
Numerical simulation of bone regeneration in a bone chamber.
Geris, Liesbet ; ; et al
in Journal of Dental Research (2009), 88(2), 158-63
While mathematical models are able to capture essential aspects of biological processes like fracture healing and distraction osteogenesis, their predictive capacity in peri-implant osteogenesis remains ... [more ▼]
While mathematical models are able to capture essential aspects of biological processes like fracture healing and distraction osteogenesis, their predictive capacity in peri-implant osteogenesis remains uninvestigated. We tested the hypothesis that a mechano-regulatory model has the potential to predict bone regeneration around implants. In an in vivo bone chamber set-up allowing for controlled implant loading (up to 90 microm axial displacement), bone tissue formation was simulated and compared qualitatively and quantitatively with histology. Furthermore, the model was applied to simulate excessive loading conditions. Corresponding to literature data, implant displacement magnitudes larger than 90 microm predicted the formation of fibrous tissue encapsulation of the implant. In contradiction to findings in orthopedic implant osseointegration, implant displacement frequencies higher than 1 Hz did not favor the formation of peri-implant bone in the chamber. Additional bone chamber experiments are needed to test these numerical predictions. [less ▲]Detailed reference viewed: 11 (0 ULg)
In silico biology of bone modelling and remodelling: regeneration.
Geris, Liesbet ; ;
in Philosophical Transactions : Mathematical, Physical & Engineering Sciences (2009), 367(1895), 2031-53
Bone regeneration is the process whereby bone is able to (scarlessly) repair itself from trauma, such as fractures or implant placement. Despite extensive experimental research, many of the mechanisms ... [more ▼]
Bone regeneration is the process whereby bone is able to (scarlessly) repair itself from trauma, such as fractures or implant placement. Despite extensive experimental research, many of the mechanisms involved still remain to be elucidated. Over the last decade, many mathematical models have been established to investigate the regeneration process in silico. The first models considered only the influence of the mechanical environment as a regulator of the healing process. These models were followed by the development of bioregulatory models where mechanics was neglected and regeneration was regulated only by biological stimuli such as growth factors. The most recent mathematical models couple the influences of both biological and mechanical stimuli. Examples are given to illustrate the added value of mathematical regeneration research, specifically in the in silico design of treatment strategies for non-unions. Drawbacks of the current continuum-type models, together with possible solutions in extending the models towards other time and length scales are discussed. Finally, the demands for dedicated and more quantitative experimental research are presented. [less ▲]Detailed reference viewed: 17 (0 ULg)
Modelling the early phases of bone regeneration around an endosseous oral implant
; Geris, Liesbet ; et al
in Computer Methods in Biomechanics & Biomedical Engineering (2009), 12(4), 459-468
The objective of this study was to see whether a mathematical model of fracture healing was able to mimic bone formation around an unloaded screw-shaped titanium implant as it is well-believed that both ... [more ▼]
The objective of this study was to see whether a mathematical model of fracture healing was able to mimic bone formation around an unloaded screw-shaped titanium implant as it is well-believed that both processes exhibit many biological similarities. This model describes the spatio-temporal evolution of cellular activities, ranging from mesenchymal stem cell migration, proliferation, differentiation to bone formation, which are initiated and regulated by the growth factors present at the peri-implant site. For the simulations, a finite volume code was used and adequate initial and boundary conditions were applied. Two sets of analyses have been performed, in which either initial and boundary condition or model parameter values were changed with respect to the fracture healing model parameter values. For a number of combinations, the spatio-temporal evolution of bone density was well-predicted. However reducing cell proliferation rate and increasing osteoblast differentiation and osteogenic growth factor synthesis rates, the simulation results were in agreement with the experimental data. [less ▲]Detailed reference viewed: 12 (2 ULg)
Assessment of mechanobiological models for the numerical simulation of tissue differentiation around immediately loaded implants.
Geris, Liesbet ; ; et al
in Computer Methods in Biomechanics & Biomedical Engineering (2003), 6(5-6), 277-88
Nowadays, there is a growing consensus on the impact of mechanical loading on bone biology. A bone chamber provides a mechanically isolated in vivo environment in which the influence of different ... [more ▼]
Nowadays, there is a growing consensus on the impact of mechanical loading on bone biology. A bone chamber provides a mechanically isolated in vivo environment in which the influence of different parameters on the tissue response around loaded implants can be investigated. This also provides data to assess the feasibility of different mechanobiological models that mathematically describe the mechanoregulation of tissue differentiation. Before comparing numerical results to animal experimental results, it is necessary to investigate the influence of the different model parameters on the outcome of the simulations. A 2D finite element model of the tissue inside the bone chamber was created. The differentiation models developed by Prendergast, et al. ["Biophysical stimuli on cells during tissue differentiation at implant interfaces", Journal of Biomechanics, 30(6), (1997), 539-548], Huiskes et al. ["A biomechanical regulatory model for periprosthetic fibrous-tissue differentiation", Journal of Material Science: Materials in Medicine, 8 (1997) 785-788] and by Claes and Heigele ["Magnitudes of local stress and strain along bony surfaces predict the course and type of fracture healing", Journal of Biomechanics, 32(3), (1999) 255-266] were implemented and integrated in the finite element code. The fluid component in the first model has an important effect on the predicted differentiation patterns. It has a direct effect on the predicted degree of maturation of bone and a substantial indirect effect on the simulated deformations and hence the predicted phenotypes of the tissue in the chamber. Finally, the presence of fluid also causes time-dependent behavior. Both models lead to qualitative and quantitative differences in predicted differentiation patterns. Because of the different nature of the tissue phenotypes used to describe the differentiation processes, it is however hard to compare both models in terms of their validity. [less ▲]Detailed reference viewed: 10 (2 ULg)