Computational mechanobiology to study the effect of surface geometry on peri-implant tissue differentiation

Bone ingrowth in randomly distributed porous interbody cage during lumbar spinal fusion

Porous interbody cages are often used in spinal fusion surgery since they allow bone ingrowth which facilitates long-term stability. However, the extent of bone ingrowth in and around porous interbody cages has scarcely been investigated. Moreover, tissue differentiation might not be similar around the superior and inferior cage-bone interfaces. Using mechanobiology-based numerical framework and physiologic loading conditions, the study investigates the spatial distribution of evolutionary bone ingrowth within randomly distributed porous interbody cages, having varied porosities. Finite Element (FE) microscale models, corresponding to cage porosities of 60 %, 72 %, and 83 %, were developed for the superior and inferior interfacial regions of the cage, along with the macroscale model of the implanted lumbar spine. The implant-bone relative displacements of different porosity models were mapped from macroscale to microscale model. Bone formation of 10-40 % was predicted across the porous cage models, resulting in an average Young's modulus ranging between 765 MPa and 915 MPa. Maximum bone ingrowth of ∼34 % was observed for the 83 % porous cage, which was subject to low implant-bone relative displacements (maximum 50μm). New bone formation was found to be greater at the superior interface (∼34 %) as compared to the inferior interface (∼30 %) for P83 model. Relatively greater volume of fibrous tissue was formed at the implant-bone interface for the cage with 60 % and 72 % porosities, which might lead to cage migration and eventual failure of the implant. Hence, the interbody cage with 83 % porosity appears to be most favorable for bone ingrowth, provided sufficient mechanical strength is offered.

Prediction of bone ingrowth into a porous novel hip-stem: A finite element analysis integrated with mechanoregulatory algorithm

Bone ingrowth into a porous implant is necessary for its long-term fixation. Although attempts have been made to quantify the peri-implant bone growth using finite element (FE) analysis integrated with mechanoregulatory algorithms, bone ingrowth into a porous cellular hip stem has scarcely been investigated. Using a three-dimensional (3D) FE model and mechanobiology-based numerical framework, the objective of this study was to predict the spatial distribution of evolutionary bone ingrowth into an uncemented novel porous hip stem proposed earlier by the authors. A CT-based FE macromodel of the implant-bone structure was developed. The bone material properties were assigned based on CT grey value. Peak musculoskeletal loading conditions, corresponding to level walking and stair climbing, were applied. The geometry of the implant-bone macromodel was divided into multiple submodels. A suitable mapping framework was used to transfer maximum nodal displacements from the FE macromodel to the cut boundaries of the FE submodels. CT grey value-based bone materials properties were assigned to the submodels. Thereafter, the submodels were solved and simulations of bone ingrowth were carried out using mechanoregulatory principle. A gradual increase in the average Young's modulus, from 1200 to 1500 MPa, of the bone tissue layer was observed considering all the submodels. The distal submodel exhibited 82% of bone ingrowth, whereas the proximal submodel experienced 65% bone ingrowth. Equilibrium in the bone ingrowth process was achieved in 7 weeks postoperatively, with a notable amount of bone ingrowth that should lead to biological fixation of the novel hip stem.

A mechano-regulation model to design and optimize the surface microgeometry of titanium textured devices for biomedical applications

In a technological context where, thanks to the additive manufacturing techniques, even sophisticated geometries as well as surfaces with specific micrometric features can be realized, we propose a mechano-regulation algorithm to determine the optimal microgeometric parameters of the surface of textured titanium devices for biomedical applications. A poroelastic finite element model was developed including a portion of bone, a portion of a textured titanium device and a layer of granulation tissue separating the bone from the device and occupying the space between them. The algorithm, implemented in the Matlab environment, determines the optimal values of the root mean square and the correlation length that the device surface must possess to maximize bone formation in the gap between the bone and the device. For low levels of compression load acting on the bone, the algorithm predicts low values of root mean square and high values of correlation length. Conversely, high levels of load require high values of root mean square and low values of correlation length. The optimal microgeometrical parameters were determined for various thickness values of the granulation tissue layer. Interestingly, the predictions of the proposed computational model are consistent with the experimental results reported in the literature. The proposed algorithm shows promise as a valuable tool for addressing the demands of precision medicine. In this approach, the device or prosthesis is no longer designed solely based on statistical averages but is tailored to each patient's unique anthropometric characteristics, as well as considerations related to their metabolism, sex, age, and more.

Assessment of bone ingrowth around beaded coated tibial implant for total ankle replacement using mechanoregulatory algorithm

The long-term performance of porous coated tibial implants for total ankle replacement (TAR) primarily depends on the extent of bone ingrowth at the bone-implant interface. Although attempts were made for primary fixation for immediate post-operative stability, no investigation was conducted on secondary fixation. The aim of this study is to assess bone ingrowth around the porous beaded coated tibial implant for TAR using a mechanoregulatory algorithm. A realistic macroscale finite element (FE) model of the implanted tibia was developed based on computer tomography (CT) data to assess implant-bone micromotions and coupled with microscale FE models of the implant-bone interface to predict bone ingrowth around tibial implant for TAR. The macroscale FE model was subjected to three near physiological loading conditions to evaluate the site-specific implant-bone micromotion, which were then incorporated into the corresponding microscale model to mimic the near physiological loading conditions. Results of the study demonstrated that the implant experienced tangential micromotion ranged from 0 to 71 μm with a mean of 3.871 μm. Tissue differentiation results revealed that bone ingrowth across the implant ranged from 44 to 96 %, with a mean of around 70 %. The average Young's modulus of the inter-bead tissue layer varied from 1444 to 4180 MPa around the different regions of the implant. The analysis postulates that when peak micromotion touches 30 μm around different regions of the implant, it leads to pronounced fibrous tissues on the implant surface. The highest amount of bone ingrowth was observed in the central regions, and poor bone ingrowth was seen in the anterior parts of the implant, which indicate improper osseointegration around this region. This macro-micro mechanical FE framework can be extended to improve the implant design to enhance the bone ingrowth and in future to develop porous lattice-structured implants to predict and enhance osseointegration around the implant.

A macro-micro FE and ANN framework to assess site-specific bone ingrowth around the porous beaded-coated implant: an example with BOX® tibial implant for total ankle replacement

The use of mechanoregulatory schemes based on finite element (FE) analysis for the evaluation of bone ingrowth around porous surfaces is a viable approach but requires significant computational time and effort. The aim of this study is to develop a combined macro-micro FE and artificial neural network (ANN) framework for rapid and accurate prediction of the site-specific bone ingrowth around the porous beaded-coated tibial implant for total ankle replacement (TAR). A macroscale FE model of the implanted tibia was developed based on CT data. Subsequently, a microscale FE model of the implant-bone interface was created for performing bone ingrowth simulations using mechanoregulatory algorithms. An ANN was trained for rapid and accurate prediction of bone ingrowth. The results predicted by ANN are well comparable to FE-predicted results. Predicted site-specific bone ingrowth using ANN around the implant ranges from 43.04 to 98.24%, with a mean bone ingrowth of around 74.24%. Results suggested that the central region exhibited the highest bone ingrowth, which is also well corroborated with the recent explanted study on BOX®. The proposed methodology has the potential to simulate bone ingrowth rapidly and effectively at any given site over any implant surface.

Computational assessment of growth of connective tissues around textured hip stem subjected to daily activities after THA

Longer-term stability of uncemented femoral stem depends on ossification at bone-implant interface. Although attempts have been made to assess the amount of bone growth using finite element (FE) analysis in combination with a mechanoregulatory algorithm, there has been little research on tissue differentiation patterns on hip stems with proximal macro-textures. The primary goal of this investigation is to qualitatively compare the formation of connective tissues around a femoral implant with/without macro-textures on its proximal surfaces. This study also predicts formation of different tissue phenotypes and their spatio-temporal distribution around a macro-textured femoral stem under routine activities. Results from the study show that non-textured implants (80 to 94%) encourage fibroplasia compared to that in textured implants (71 to 85.38%) under similar routine activity, which might trigger aseptic loosening of implant. Formation of bone was more on medio-lateral sides and towards proximal regions of Gruen zones 2 and 6, which was found to be in line with clinical observations. Fibroplasia was higher under stair climbing (85 to 91%) compared to that under normal walking (71 to 85.38%). This study suggests that stair climbing, although falls under recommended activity, might be detrimental to patient compared to normal walking in the initial rehabilitation period.

Bone Ingrowth Around an Uncemented Femoral Implant Using Mechanoregulatory Algorithm: A Multiscale Finite Element Analysis

The primary fixation and long-term stability of a cementless femoral implant depend on bone ingrowth within the porous coating. Although attempts were made to quantify the peri-implant bone ingrowth using the finite element (FE) analysis and mechanoregulatory principles, the tissue differentiation patterns on a porous-coated hip stem have scarcely been investigated. The objective of this study is to predict the spatial distribution of evolutionary bone ingrowth around an uncemented hip stem, using a three-dimensional (3D) multiscale mechanobiology-based numerical framework. Multiple load cases representing a variety of daily living activities, including walking, stair climbing, sitting down, and standing up from a chair, were used as applied loading conditions. The study accounted for the local variations in host bone material properties and implant-bone relative displacements of the macroscale implanted FE model, in order to predict bone ingrowth in microscale representative volume elements (RVEs) of 12 interfacial regions. In majority RVEs, 20-70% bone tissue (immature and mature) was predicted after 2 months, contributing toward a progressive increase in average Young's modulus (1200-3000 MPa) of the interbead tissue layer. Higher bone ingrowth (mostly greater than 60%) was predicted in the anterolateral regions of the implant, as compared to the posteromedial side (20-50%). New bone tissue was formed deeper inside the interbead spacing, adhering to the implant surface. The study helps to gain an insight into the degree of osseointegration of a porous-coated femoral implant.

Mechanobiological model to study the influence of screw design and surface treatment on osseointegration

This study aims at suggesting a new approach to peri-implant healing models, providing a set of taxis-diffusion-reaction equations under the combined influence of mechanical and biochemical factors. Early events of osseointegration were simulated for titanium screw implants inserted into a pre-drilled trabecular bone environment, up to 12 weeks of peri-implant bone healing. Simulations showed the ability of the model to reproduce biological events occurring at the implant interface through osteogenesis. Implants with shallow healing chamber showed higher proportions of lamellar bone, enhanced by the increase of mechanical stimulation. Osteoconduction was observed through the surface treatment model and similar bone healing patterns compared to in vivo studies.

Influence of sequential opening/closing of interface gaps and texture density on bone growth over macro-textured implant surfaces using FE based mechanoregulatory algorithm

Intramedullary implant fixation is achieved through a press-fit between the implant and the host bone. A stronger press-fit between the bone and the prosthesis often introduces damage to the bone canal creating micro-gaps. The aim of the present investigation is to study the influences of simultaneous opening/closing of gaps on bone growth over macro-textured implant surfaces. Models based on textures available on CORAIL and SP-CL hip stems have been considered and 3D finite element (FE) analysis has been carried out in conjunction with mechanoregulation based tissue differentiation algorithm. Additionally, using a full-factorial approach, different combinations (between 5 µm to 15 µm) of sliding and gap distances at the bone-implant interface were considered to understand their combined influences on bone growth. All designs show an elevated fibrous tissue formation (10.96% at 5 µm to 29.38% at 40 µm for CORAIL based textured model; 11.45% at 5 µm to 32.25% at 40 µm for SP-CL based textured model) and inhibition of soft cartilaginous tissue (75.64% at 5 µm to 53.94% at 40 µm for CORAIL based model; 76.02% at 5 µm to 53.60% at 40 µm SP-CL based model) at progressively higher levels of normal micromotion, leading to a fragile bone-implant interface. These results highlight the importance of minimizing both sliding and gap distances simultaneously to enhance bone growth and implant stability. Further, results from the studies with differential texture density over CORAIL based implant reveal a non-linear complex relationship between tissue growth and texture density which might be investigated in a machine learning framework.

Qualitative predictions of bone growth over optimally designed macro-textured implant surfaces obtained using NN-GA based machine learning framework

The surface features on implant surface can improve biologic fixation of the implant with the host bone leading to improved secondary (biological) implant stability. Application of finite element (FE) based mechanoregulatory schemes to estimate the amount of bone growth for a wide range of implant surface features is either manually intensive or computationally expensive. This study adopts an integrated approach combining FE, back-propagation neural network (BPNN) and genetic algorithm (GA) based search to evaluate optimum surface macro-textures from three representative implant models so as to enhance bone growth. Initial surface textures chosen for the implant models were based on an earlier investigation. Based on FE predicted dataset, a BPNN was formulated for faster prediction of bone growth. Using the BPNN predicted output, a GA-based search was carried out to maximize bone growth subject to clinically admissible micromotion at the bone-implant interface. The results from FE analysis and bone growth predictions from the BPNN were found to have strong correlation. The optimal osseointegration-maximized-textures (OMTs) obtained were found to offer enhanced biological fixation, as compared to that offered by the textures in the initial models. Results from the present study reveal that certain reduction in the dimension of ribs/grooves promotes bone growth. However, periodic patterns of ribs with higher and lower rib dimensions provide uniform stress environment at the interface thus promoting osseointegration.

The influence of macro-textural designs over implant surface on bone on-growth: A computational mechanobiology based study

The longerterm secondary stability of an uncemented implant depends primarily on the quality and extent of bone in-growth or on-growth at the bone-implant interface. Investigations are warranted to predict the influences of implant macro-textures on bone on-growth pattern. Mechanoregulatory tissue differentiation algorithms can predict such patterns effectively. There is, however, a dearth of volumetric in silico study to assess the influence of macro-textures on bone growth. The present study investigated the influence of macro-textural grooves/ribs on changes in tissue formation at the bone-implant interface by carrying out a 3D finite element (FE) analysis. Three distinct macro-textures, loosely based on commercially viable hip stem models, were comparatively assessed for varying levels of interfacial micromotion. The study predicted elevated fibrogenesis and chondrogenesis, followed by a suppressed osteogenesis for higher levels of micromotion (60 μm and 100 μm), resulting in weak bone-implant interface strength. However, small judicious modifications in implant surface texture may enhance bone growth to a considerable extent. The numerical scheme can further be used as a template for more rigorous parametric and multi-scale studies.

Influence of Implant Surface Texture Design on Peri-Acetabular Bone Ingrowth: A Mechanobiology Based Finite Element Analysis

The fixation of uncemented acetabular components largely depends on the amount of bone ingrowth, which is influenced by the design of the implant surface texture. The objective of this numerical study is to evaluate the effect of these implant texture design factors on bone ingrowth around an acetabular component. The novelty of this study lies in comparative finite element (FE) analysis of 3D microscale models of the implant-bone interface, considering patient-specific mechanical environment, host bone material property and implant-bone relative displacement, in combination with sequential mechanoregulatory algorithm and design of experiment (DOE) based statistical framework. Results indicated that the bone ingrowth process was inhibited due to an increase in interbead spacing from 200 μm to 600 μm and bead diameter from 1000 μm to 1500 μm and a reduction in bead height from 900 μm to 600 μm. Bead height, a main effect, was found to have a predominant influence on bone ingrowth. Among the interaction effects, the combination of bead height and bead diameter was found to have a pronounced influence on bone ingrowth process. A combination of low interbead spacing (P = 200 μm), low bead diameter (D = 1000 μm), and high bead height (H = 900 μm) facilitated peri-acetabular bone ingrowth and an increase in average Young's modulus of newly formed tissue layer. Hence, such a surface texture design seemed to provide improved fixation of the acetabular component.

Diffusion model to describe osteogenesis within a porous titanium scaffold

In this study, we develop a two-dimensional finite element model, which is derived from an animal experiment and allows simulating osteogenesis within a porous titanium scaffold implanted in ewe's hemi-mandible during 12 weeks. The cell activity is described through diffusion equations and regulated by the stress state of the structure. We compare our model to (i) histological observations and (ii) experimental data obtained from a mechanical test done on sacrificed animal. We show that our mechano-biological approach provides consistent numerical results and constitutes a useful tool to predict osteogenesis pattern.

Functional interface micromechanics of 11 en-bloc retrieved cemented femoral hip replacements

BACKGROUND AND PURPOSE

Despite the longstanding use of micromotion as a measure of implant stability, direct measurement of the micromechanics of implant/bone interfaces from en bloc human retrievals has not been performed. The purpose of this study was to determine the stem-cement and cement-bone micromechanics of functionally loaded, en-bloc retrieved, cemented femoral hip components.

METHODS

11 fresh frozen proximal femurs with cemented implants were retrieved at autopsy. Specimens were sectioned transversely into 10-mm slabs and fixed to a loading device where functional torsional loads were applied to the stem. A digital image correlation technique was used to document micromotions at stem-cement and cement-bone interfaces during loading.

RESULTS

There was a wide range of responses with stem-cement micromotions ranging from 0.0006 mm to 0.83 mm (mean 0.17 mm, SD 0.29) and cement-bone micromotions ranging from 0.0022 mm to 0.73 mm (mean 0.092 mm, SD 0.22). There was a strong (linear-log) inverse correlation between apposition fraction and micromotion at the stem-cement interface (r(2) = 0.71, p < 0.001). There was a strong inverse log-log correlation between apposition fraction at the cement-bone interface and micromotion (r(2) = 0.85, p < 0.001). Components that were radiographically well-fixed had a relatively narrow range of micromotions at the stem-cement (0.0006-0.057 mm) and cement-bone (0.0022-0.029 mm) interfaces.

INTERPRETATION

Minimizing gaps at the stem-cement interface and encouraging bony apposition at the cement-bone interface would be clinically desirable. The cement-bone interface does not act as a bonded interface in actual use, even in radiographically well-fixed components. Rather, the interface is quite compliant, with sliding and opening motions between the cement and bone surfaces.

Evaluation of functional dynamics during osseointegration and regeneration associated with oral implants

OBJECTIVES

The aim of this paper is to review current investigations on functional assessments of osseointegration and assess correlations to the peri-implant structure.

MATERIAL AND METHODS

The literature was electronically searched for studies of promoting dental implant osseointegration, functional assessments of implant stability, and finite element (FE) analyses in the field of implant dentistry, and any references regarding biological events during osseointegration were also cited as background information.

RESULTS

Osseointegration involves a cascade of protein and cell apposition, vascular invasion, de novo bone formation and maturation to achieve the primary and secondary dental implant stability. This process may be accelerated by alteration of the implant surface roughness, developing a biomimetric interface, or local delivery of growth-promoting factors. The current available pre-clinical and clinical biomechanical assessments demonstrated a variety of correlations to the peri-implant structural parameters, and functionally integrated peri-implant structure through FE optimization can offer strong correlation to the interfacial biomechanics.

CONCLUSIONS

The progression of osseointegration may be accelerated by alteration of the implant interface as well as growth factor applications, and functional integration of peri-implant structure may be feasible to predict the implant function during osseointegration. More research in this field is still needed.

A mathematical approach to bone tissue engineering

Tissue engineering is becoming consolidated in the biomedical field as one of the most promising strategies in tissue repair and regenerative medicine. Within this discipline, bone tissue engineering involves the use of cell-loaded porous biomaterials, i.e. bioscaffolds, to promote bone tissue regeneration in bone defects or diseases such as osteoporosis, although it has not yet been incorporated into daily clinical practice. The overall success of a particular bone tissue engineering application depends strongly on scaffold design parameters, which do away with long and expensive clinical protocols. Computer simulation is a useful tool that may reduce animal experiments and help to identify optimal patient-specific designs after concise model validation. In this paper, we present a novel mathematical approach to bone regeneration within scaffolds, based on a multiscale framework. Results are presented over an actual scaffold microstructure, showing the potential of computer simulation, and how it can aid in the task of making bone tissue engineering a reality in clinical practice.