From bed to bench: How in silico medicine can help ageing research

In Silico Clinical Trial for Osteoporosis Treatments to Prevent Hip Fractures: Simulation of the Placebo Arm

Osteoporosis represents a major healthcare concern. The development of novel treatments presents challenges due to the limited cost-effectiveness of clinical trials and ethical concerns associated with placebo-controlled trials. Computational models for the design and assessment of biomedical products (In Silico Trials) are emerging as a promising alternative. In this study, a novel In Silico Trial technology (BoneStrength) was applied to replicate the placebo arms of two concluded clinical trials and its accuracy in predicting hip fracture incidence was evaluated. Two virtual cohorts (N = 1238 and 1226, respectively) were generated by sampling a statistical anatomy atlas based on CT scans of proximal femurs. Baseline characteristics were equivalent to those reported for the clinical cohorts. Fall events were sampled from a Poisson distribution. A multiscale stochastic model was implemented to estimate the impact force associated to each fall. Finite Element models were used to predict femur strength. Fracture incidence in 3 years follow-up was computed with a Markov chain approach; a patient was considered fractured if the impact force associated with a fall exceeded femur strength. Ten realizations of the stochastic process were run to reach convergence. Each realization required approximately 2500 FE simulations, solved using High-Performance Computing infrastructures. Predicted number of fractures was 12 ± 2 and 18 ± 4 for the two cohorts, respectively. The predicted incidence range consistently included the reported clinical data, although on average fracture incidence was overestimated. These findings highlight the potential of BoneStrength for future applications in drug development and assessment.

A novel framework for elucidating the effect of mechanical loading on the geometry of ovariectomized mouse tibiae using principal component analysis

Introduction

Murine models are used to test the effect of anti-osteoporosis treatments as they replicate some of the bone phenotypes observed in osteoporotic (OP) patients. The effect of disease and treatment is typically described as changes in bone geometry and microstructure over time. Conventional assessment of geometric changes relies on morphometric scalar parameters. However, being correlated with each other, these parameters do not describe separate fractions of variations and offer only a moderate insight into temporal changes.

Methods

The current study proposes a novel image-based framework that employs deformable image registration on in vivo longitudinal images of bones and Principal Component Analysis (PCA) for improved quantification of geometric effects of OP treatments. This PCA-based model and a novel post-processing of score changes provide orthogonal modes of shape variations temporally induced by a course of treatment (specifically in vivo mechanical loading).

Results and Discussion

Errors associated with the proposed framework are rigorously quantified and it is shown that the accuracy of deformable image registration in capturing the bone shapes (∼1 voxel = 10.4 μm) is of the same order of magnitude as the relevant state-of-the-art evaluation studies. Applying the framework to longitudinal image data from the midshaft section of ovariectomized mouse tibia, two mutually orthogonal mode shapes are reliably identified to be an effect of treatment. The mode shapes captured changes of the tibia geometry due to the treatment at the anterior crest (maximum of 0.103 mm) and across the tibia midshaft section and the posterior (0.030 mm) and medial (0.024 mm) aspects. These changes agree with those reported previously but are now described in a compact fashion, as a vector field of displacements on the bone surface. The proposed framework enables a more detailed investigation of the effect of disease and treatment on bones in preclinical studies and boosts the precision of such assessments.

Combining PTH(1-34) and mechanical loading has increased benefit to tibia bone mechanics in ovariectomised mice

Combined treatment with PTH(1-34) and mechanical loading confers increased structural benefits to bone than monotherapies. However, it remains unclear how this longitudinal adaptation affects the bone mechanics. This study quantified the individual and combined longitudinal effects of PTH(1-34) and mechanical loading on the bone stiffness and strength evaluated in vivo with validated micro-finite element (microFE) models. C57BL/6 mice were ovariectomised at 14-week-old and treated either with injections of PTH(1-34), compressive tibia loading or both interventions concurrently. Right tibiae were in vivo microCT-scanned every 2 weeks from 14 until 24-week-old. MicroCT images were rigidly registered to reference tibia and the cortical organ level (whole bone) and tissue level (midshaft) morphometric properties and bone mineral content were quantified. MicroCT images were converted into voxel-based homogeneous, linear elastic microFE models to estimate the bone stiffness and strength. This approach allowed us for the first time to quantify the longitudinal changes in mechanical properties induced by combined treatments in a model of accelerated bone resorption. Both changes of stiffness and strength were higher with co-treatment than with individual therapies, consistent with increased benefits with the tibia bone mineral content and cortical area, properties strongly associated with the tibia mechanics. The longitudinal data shows that the two bone anabolics, both individually and combined, had persistent benefit on estimated mechanical properties, and that benefits (increased stiffness and strength) remained after treatment was withdrawn.

The loading direction dramatically affects the mechanical properties of the mouse tibia

Introduction: The in vivo tibial loading mouse model has been extensively used to evaluate bone adaptation in the tibia after mechanical loading treatment. However, there is a prevailing assumption that the load is applied axially to the tibia. The aim of this in silico study was to evaluate how much the apparent mechanical properties of the mouse tibia are affected by the loading direction, by using a validated micro-finite element (micro-FE) model of mice which have been ovariectomized and exposed to external mechanical loading over a two-week period. Methods: Longitudinal micro-computed tomography (micro-CT) images were taken of the tibiae of eleven ovariectomized mice at ages 18 and 20 weeks. Six of the mice underwent a mechanical loading treatment at age 19 weeks. Micro-FE models were generated, based on the segmented micro-CT images. Three models using unitary loads were linearly combined to simulate a range of loading directions, generated as a function of the angle from the inferior-superior axis (θ, 0°-30° range, 5° steps) and the angle from the anterior-posterior axis (ϕ, 0°: anterior axis, positive anticlockwise, 0°-355° range, 5° steps). The minimum principal strain was calculated and used to estimate the failure load, by linearly scaling the strain until 10% of the nodes reached the critical strain level of -14,420 με. The apparent bone stiffness was calculated as the ratio between the axial applied force and the average displacement along the longitudinal direction, for the loaded nodes. Results: The results demonstrated a high sensitivity of the mouse tibia to the loading direction across all groups and time points. Higher failure loads were found for several loading directions (θ = 10°, ϕ 205°-210°) than for the nominal axial case (θ = 0°, ϕ = 0°), highlighting adaptation of the bone for loading directions far from the nominal axial one. Conclusion: These results suggest that in studies which use mouse tibia, the loading direction can significantly impact the failure load. Thus, the magnitude and direction of the applied load should be well controlled during the experiments.

In silico assessment of the bone regeneration potential of complex porous scaffolds

Mechanical environment plays a crucial role in regulating bone regeneration in bone defects. Assessing the mechanobiological behavior of patient-specific orthopedic scaffolds in-silico could help guide optimal scaffold designs, as well as intra- and post-operative strategies to enhance bone regeneration and improve implant longevity. Additively manufactured porous scaffolds, and specifically triply periodic minimal surfaces (TPMS), have shown promising structural properties to act as bone substitutes, yet their ability to induce mechanobiologially-driven bone regeneration has not been elucidated. The aim of this study is to i) explore the bone regeneration potential of TPMS scaffolds made of different stiffness biocompatible materials, to ii) analyze the influence of pre-seeding the scaffolds and increasing the post-operative resting period, and to iii) assess the influence of patient-specific parameters, such as age and mechanosensitivity, on outcomes. To perform this study, an in silico model of a goat tibia is used. The bone ingrowth within the scaffold pores was simulated with a mechano-driven model of bone regeneration. Results showed that the scaffold's architectural properties affect cellular diffusion and strain distribution, resulting in variations in the regenerated bone volume and distribution. The softer material improved the bone ingrowth. An initial resting period improved the bone ingrowth but not enough to reach the scaffold's core. However, this was achieved with the implantation of a pre-seeded scaffold. Physiological parameters like age and health of the patient also influence the bone regeneration outcome, though to a lesser extent than the scaffold design. This analysis demonstrates the importance of the scaffold's geometry and its material, and highlights the potential of using mechanobiological patient-specific models in the design process for bone substitutes.

PCL strut-like scaffolds appear superior to gyroid in terms of bone regeneration within a long bone large defect: An in silico study

The treatment of large bone defects represents a major clinical challenge. 3D printed scaffolds appear as a promising strategy to support bone defect regeneration. The 3D design of such scaffolds impacts the healing path and thus defect regeneration potential. Among others, scaffold architecture has been shown to influence the healing outcome. Gyroid architecture, characterized by a zero mean surface curvature, has been discussed as a promising scaffold design for bone regeneration. However, whether gyroid scaffolds are favourable for bone regeneration in large bone defects over traditional strut-like architecture scaffolds remains unknown. Therefore, the aim of this study was to investigate whether gyroid scaffolds present advantages over more traditional strut-like scaffolds in terms of their bone regeneration potential. Validated bone defect regeneration principles were applied in an in silico modeling approach that allows to predict bone formation in defect regeneration. Towards this aim, the mechano-biological bone regeneration principles were adapted to allow simulating bone regeneration within both gyroid and strut-like scaffolds. We found that the large surface curvatures of the gyroid scaffold led to a slower tissue formation dynamic and conclusively reduced bone regeneration. The initial claim, that an overall reduced zero mean surface curvature would enhance bone formation, could not be confirmed. The here presented approach illustrates the potential of in silico tools to evaluate in pre-clinical studies scaffold designs and eventually lead to optimized architectures of 3D printed implants for bone regeneration.

Reproducibility of Densitometric and Biomechanical Assessment of the Mouse Tibia From In Vivo Micro-CT Images

Interventions for bone diseases (e.g. osteoporosis) require testing in animal models before clinical translation and the mouse tibia is among the most common tested anatomical sites. In vivo micro-Computed Tomography (microCT) based measurements of the geometrical and densitometric properties are non-invasive and therefore constitute an important tool in preclinical studies. Moreover, validated micro-Finite Element (microFE) models can be used for predicting the bone mechanical properties non-invasively. However, considering that the image processing pipeline requires operator-dependant steps, the reproducibility of these measurements has to be assessed. The aim of this study was to evaluate the intra- and inter-operator reproducibility of several bone parameters measured from microCT images. Ten in vivo microCT images of the right tibia of five mice (at 18 and 22 weeks of age) were processed. One experienced operator (intra-operator analysis) and three different operators (inter-operator) aligned each image to a reference through a rigid registration and selected a volume of interest below the growth plate. From each image the following parameters were measured: total bone mineral content (BMC) and density (BMD), BMC in 40 subregions (ten longitudinal sections, four quadrants), microFE-based stiffness and failure load. Intra-operator reproducibility was acceptable for all parameters (precision error, PE < 3.71%), with lowest reproducibility for stiffness (3.06% at week 18, 3.71% at week 22). The inter-operator reproducibility was slightly lower (PE < 4.25%), although still acceptable for assessing the properties of most interventions. The lowest reproducibility was found for BMC in the lateral sector at the midshaft (PE = 4.25%). Densitometric parameters were more reproducible than most standard morphometric parameters calculated in the proximal trabecular bone. In conclusion, microCT and microFE models provide reproducible measurements for non-invasive assessment of the mouse tibia properties.

Positive interactions of mechanical loading and PTH treatments on spatio-temporal bone remodelling

Osteoporosis is one of the most common skeletal diseases, but current therapies are limited to generalized antiresorptive or anabolic interventions, which do not target regions that would benefit from improvements to skeletal health. To improve the evaluation of treatment plans, we used a spatio-temporal multiscale approach that combines longitudinal in vivo micro-computed tomography (micro-CT) and in silico subject-specific finite element modeling to quantitatively map bone adaptation changes due to disease and treatment at high resolution. Our findings show time and region-dependent modifications in bone remodelling following one and two sets of mechanical loading and/or pharmacological interventions. The multiscale results highlighted that the distal section was unaffected by mechanical loading alone but the proximal tibia had the greatest gain from positive interactions of combined therapies. Mechanical loading abated the catabolic effect of PTH, but the main benefit of combined treatments occurred from the additive interactions of the two therapies in periosteal apposition. These results provide detailed insight into the efficacy of combined treatments, facilitating the optimisation of dosage and treatment duration in preclinical mouse studies, and the development of novel interventions for skeletal diseases. STATEMENT OF SIGNIFICANCE: Combined mechanical loading and pharmacotherapy have the potential to slow osteoporosis-induced bone loss but current therapies do not target the regions in need of strengthening. We show for the first time spatial region-dependant interactions between PTH and mechanical loading treatment in OVX mouse tibiae, highlighting local regions in the tibia that benefitted from separate and combined treatments. Combined experimental-computational analysis also detailed the lasting period of each treatment per location in the tibia, the extent of positive (or negative) interactions of the combined therapies, and the impact of each treatment on the regulation of bone adaptation spatio-temporally. This approach can be used to create hypothesis about the interactions of different treatments to optimise the design of biomaterials and medical interventions.

The rebirth of isolated organ contraction studies for drug discovery and repositioning

Smooth muscle contraction is a basic homeostatic mechanism and, when dysfunctional, it is directly responsible for severe diseases like asthma and arterial hypertension. For decades, the standard technique to study contraction and evaluate the action of drugs has involved the use of isolated organ baths. However, the high cost of the dedicated personnel has led to their progressive replacement by techniques compatible with HTS. Nevertheless, preclinical evaluation of vasodilator or bronchodilator activity still requires direct evaluation of a drug's effects. The multi-well organ bath (MuWOB) combines the possibility of using a robot to perform computer-controlled contraction and relaxation assays on arterial and tracheal tissue (rings) in largescale parallel analyses.

Non-invasive prediction of the mouse tibia mechanical properties from microCT images: comparison between different finite element models

New treatments for bone diseases require testing in animal models before clinical translation, and the mouse tibia is among the most common models. In vivo micro-Computed Tomography (microCT)-based micro-Finite Element (microFE) models can be used for predicting the bone strength non-invasively, after proper validation against experimental data. Different modelling techniques can be used to estimate the bone properties, and the accuracy associated with each is unclear. The aim of this study was to evaluate the ability of different microCT-based microFE models to predict the mechanical properties of the mouse tibia under compressive load. Twenty tibiae were microCT scanned at 10.4 µm voxel size and subsequently compressed at 0.03 mm/s until failure. Stiffness and failure load were measured from the load-displacement curves. Different microFE models were generated from each microCT image, with hexahedral or tetrahedral mesh, and homogeneous or heterogeneous material properties. Prediction accuracy was comparable among models. The best correlations between experimental and predicted mechanical properties, as well as lower errors, were obtained for hexahedral models with homogeneous material properties. Experimental stiffness and predicted stiffness were reasonably well correlated (R2 = 0.53-0.65, average error of 13-17%). A lower correlation was found for failure load (R2 = 0.21-0.48, average error of 9-15%). Experimental and predicted mechanical properties normalized by the total bone mass were strongly correlated (R2 = 0.75-0.80 for stiffness, R2 = 0.55-0.81 for failure load). In conclusion, hexahedral models with homogeneous material properties based on in vivo microCT images were shown to best predict the mechanical properties of the mouse tibia.

Optimization of the failure criterion in micro-Finite Element models of the mouse tibia for the non-invasive prediction of its failure load in preclinical applications

New treatments against osteoporosis require testing in animal models and the mouse tibia is among the most common studied anatomical sites. In vivo micro-Computed Tomography (microCT) based micro-Finite Element (microFE) models can be used for predicting the bone strength non-invasively, after proper validation against experiments. The aim of this study was to evaluate the ability of different microCT-based bone parameters and microFE models to predict tibial structural mechanical properties in compression. Twenty tibiae were scanned at 10.4 μm voxel size and subsequently tested in uniaxial compression at 0.03 mm/s until failure. Stiffness and failure load were measured from the load-displacement curves. Standard morphometric parameters were measured from the microCT images. The spatial distribution of bone mineral content (BMC) was evaluated by dividing the tibia into 40 regions. MicroFE models were generated by converting each microCT image into a voxel-based mesh with homogeneous isotropic material properties. Failure load was estimated by using different failure criteria, and the optimized parameters were selected by minimising the errors with respect to experimental measurements. Experimental and predicted stiffness were moderately correlated (R² = 0.65, error = 14% ± 8%). Normalized failure load was best predicted by microFE models (R² = 0.81, error = 9% ± 6%). Failure load was not correlated to the morphometric parameters and weakly correlated with some geometrical parameters (R² < 0.37). In conclusion, microFE models can improve the current estimation of the mouse tibia structural properties and in this study an optimal failure criterion has been defined. Since it is a non-invasive method, this approach can be applied longitudinally for evaluating temporal changes in the bone strength.

Bone remodelling in the mouse tibia is spatio-temporally modulated by oestrogen deficiency and external mechanical loading: A combined in vivo/in silico study

Osteoporosis disrupts the healthy remodelling process in bone and affects its mechanical properties. Mechanical loading has been shown to be effective in stimulating bone formation to mitigate initial bone loss. However, no study has investigated the effects of repeated mechanical loading, with a pause of one week in between, in the mouse tibia with oestrogen deficiency. This study uses a combined experimental and computational approach, through longitudinal monitoring with micro-computed tomography, to evaluate the effects of loading on bone adaptation in the tibiae of ovariectomised (OVX) C57BL/6 mice from 14 to 22 weeks of age. Micro-FE models coupled with bone adaptation algorithms were used to estimate changes in local tissue strains due to OVX and mechanical loading, and to quantify the relationship between local strain and remodelling. The first in vivo mechanical loading increased apposition, by 50-150%, while resorption decreased by 50-60%. Both endosteal and periosteal resorption increased despite the second mechanical loading, and periosteal resorption was up to 70% higher than that after the first loading. This was found to correlate with an initial decrease in average strain energy density after the first loading, which was lower and more localised after the second loading. Predictions of bone adaptation showed that between 50 and 90% of the load-induced bone apposition is linearly strain driven at the organ-level, but resorption is more biologically driven at the local level. The results imply that a systematic increase in peak load or loading rate may be required to achieve a similar bone adaptation rate in specific regions of interests.

PTH(1-34) treatment and/or mechanical loading have different osteogenic effects on the trabecular and cortical bone in the ovariectomized C57BL/6 mouse

In preclinical mouse models, a synergistic anabolic response to PTH(1–34) and tibia loading was shown. Whether combined treatment improves bone properties with oestrogen deficiency, a cardinal feature of osteoporosis, remains unknown. This study quantified the individual and combined longitudinal effects of PTH(1–34) and loading on the bone morphometric and densitometric properties in ovariectomised mice. C57BL/6 mice were ovariectomised at 14-weeks-old and treated either with injections of PTH(1–34); compressive loading of the right tibia; both interventions concurrently; or both interventions on alternating weeks. Right tibiae were microCT-scanned from 14 until 24-weeks-old. Trabecular metaphyseal and cortical midshaft morphometric properties, and bone mineral content (BMC) in 40 different regions of the tibia were measured. Mice treated only with loading showed the highest trabecular bone volume fraction at week 22. Cortical thickness was higher with co-treatment than in the mice treated with PTH alone. In the mid-diaphysis, increases in BMC were significantly higher with loading than PTH. In ovariectomised mice, the osteogenic benefits of co-treatment on the trabecular bone were lower than loading alone. However, combined interventions had increased, albeit regionally-dependent, benefits to cortical bone. Increased benefits were largest in the mid-diaphysis and postero-laterally, regions subjected to higher strains under compressive loads.

Regional Nanoindentation Properties in Different Locations on the Mouse Tibia From C57BL/6 and Balb/C Female Mice

The local spatial heterogeneity of the material properties of the cortical and trabecular bone extracted from the mouse tibia is not well-known. Nevertheless, its characterization is fundamental to be able to study comprehensively the effect of interventions and to generate computational models to predict the bone strength preclinically. The goal of this study was to evaluate the nanoindentation properties of bone tissue extracted from two different mouse strains across the tibia length and in different sectors. Left tibiae were collected from four female mice, two C57BL/6, and two Balb/C mice. Nanoindentations with maximum 6 mN load were performed on different microstructures, regions along the axis of the tibiae, and sectors (379 in total). Reduced modulus (Er) and hardness (H) were computed for each indentation. Trabecular bone of Balb/C mice was 21% stiffer than that of C57BL/6 mice (20.8 ± 4.1 GPa vs. 16.5 ± 7.1 GPa). Moreover, the proximal regions of the bones were 13-36% less stiff than the mid-shaft and distal regions of the same bones. No significant differences were found for the different sectors for E _r and H for Balb/C mice. The bone in the medial sector was found to be 8-14% harder and stiffer than the bone in the anterior or posterior sectors for C57BL/6 mice. In conclusion, this study showed that the nanoindentation properties of the mouse tibia are heterogeneous across the tibia length and the trabecular bone properties are different between Balb/C and C57BL/6 mice. These results will help the research community to identify regions where to characterize the mechanical properties of the bone during preclinical optimisation of treatments for skeletal diseases.

ChronoMID-Cross-modal neural networks for 3-D temporal medical imaging data

ChronoMID-neural networks for temporally-varying, hence Chrono, Medical Imaging Data-makes the novel application of cross-modal convolutional neural networks (X-CNNs) to the medical domain. In this paper, we present multiple approaches for incorporating temporal information into X-CNNs and compare their performance in a case study on the classification of abnormal bone remodelling in mice. Previous work developing medical models has predominantly focused on either spatial or temporal aspects, but rarely both. Our models seek to unify these complementary sources of information and derive insights in a bottom-up, data-driven approach. As with many medical datasets, the case study herein exhibits deep rather than wide data; we apply various techniques, including extensive regularisation, to account for this. After training on a balanced set of approximately 70000 images, two of the models-those using difference maps from known reference points-outperformed a state-of-the-art convolutional neural network baseline by over 30pp (> 99% vs. 68.26%) on an unseen, balanced validation set comprising around 20000 images. These models are expected to perform well with sparse data sets based on both previous findings with X-CNNs and the representations of time used, which permit arbitrarily large and irregular gaps between data points. Our results highlight the importance of identifying a suitable description of time for a problem domain, as unsuitable descriptors may not only fail to improve a model, they may in fact confound it.

In silico trials: Verification, validation and uncertainty quantification of predictive models used in the regulatory evaluation of biomedical products

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Regulators now consider also evidences produced in silico.

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We need accepted methods to evaluate the credibility of models.

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In this paper we describe the use of the ASME V&V-40 technical standard.

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We also discuss its application to various types of modelling methods.

Historically, the evidences of safety and efficacy that companies provide to regulatory agencies as support to the request for marketing authorization of a new medical product have been produced experimentally, either in vitro or in vivo. More recently, regulatory agencies started receiving and accepting evidences obtained in silico, i.e. through modelling and simulation. However, before any method (experimental or computational) can be acceptable for regulatory submission, the method itself must be considered “qualified” by the regulatory agency. This involves the assessment of the overall “credibility” that such a method has in providing specific evidence for a given regulatory procedure. In this paper, we describe a methodological framework for the credibility assessment of computational models built using mechanistic knowledge of physical and chemical phenomena, in addition to available biological and physiological knowledge; these are sometimes referred to as “biophysical” models. Using guiding examples, we explore the definition of the context of use, the risk analysis for the definition of the acceptability thresholds, and the various steps of a comprehensive verification, validation and uncertainty quantification process, to conclude with considerations on the credibility of a prediction for a specific context of use. While this paper does not provide a guideline for the formal qualification process, which only the regulatory agencies can provide, we expect it to help researchers to better appreciate the extent of scrutiny required, which should be considered early on in the development/use of any (new) in silico evidence.

Highly integrated workflows for exploring cardiovascular conditions: Exemplars of precision medicine in Alzheimer's disease and aortic dissection

For precision medicine to be implemented through the lens of in silico technology, it is imperative that biophysical research workflows offer insight into treatments that are specific to a particular illness and to a particular subject. The boundaries of precision medicine can be extended using multiscale, biophysics-centred workflows that consider the fundamental underpinnings of the constituents of cells and tissues and their dynamic environments. Utilising numerical techniques that can capture the broad spectrum of biological flows within complex, deformable and permeable organs and tissues is of paramount importance when considering the core prerequisites of any state-of-the-art precision medicine pipeline. In this work, a succinct breakdown of two precision medicine pipelines developed within two Virtual Physiological Human (VPH) projects are given. The first workflow is targeted on the trajectory of Alzheimer's Disease, and caters for novel hypothesis testing through a multicompartmental poroelastic model which is integrated with a high throughput imaging workflow and subject-specific blood flow variability model. The second workflow gives rise to the patient specific exploration of Aortic Dissections via a multi-scale and compliant model, harnessing imaging, computational fluid-dynamics (CFD) and dynamic boundary conditions. Results relating to the first workflow include some core outputs of the multiporoelastic modelling framework, and the representation of peri-arterial swelling and peri-venous drainage solution fields. The latter solution fields were statistically analysed for a cohort of thirty-five subjects (stratified with respect to disease status, gender and activity level). The second workflow allowed for a better understanding of complex aortic dissection cases utilising both a rigid-wall model informed by minimal and clinically common datasets as well as a moving-wall model informed by rich datasets.

The longitudinal effects of ovariectomy on the morphometric, densitometric and mechanical properties in the murine tibia: A comparison between two mouse strains

Oestrogen deficiency-related bone loss in the ovariectomized (OVX) mouse is a common model for osteoporosis. However, a comprehensive in vivo assessment of intervention-related changes in multiple bone properties, and in multiple mouse strains, is required in order to identify an appropriate model for future evaluation of novel anti-osteoporotic therapies. The aim of this study was to evaluate the effect of OVX on the morphometric and densitometric properties measured in the microCT images and the mechanical properties estimated with finite element models of the tibia in two mouse strains, C57BL/6 and BALB/c. 14-weeks-old female C57BL/6 and BALB/c mice were divided into two groups per strain: (1) ovariectomized, (2) non-operated control. The right tibia was scanned at baseline (14 weeks) and then every two weeks thereafter, until 24-weeks-old, using in vivo microCT. Changes in trabecular and cortical bone morphometry, spatiotemporal changes in densitometric properties and in mechanical properties (from micro-finite element (μFE) analysis) were computed. Differences between OVX and non-operated controls were evaluated by ANCOVA, adjusted for 14-weeks baseline. In morphometry, trabecular bone mass was significantly reduced in both C57BL/6 and BALB/c from four weeks following surgery. Though the OVX-effect was transient in BALB/c as bone mass reached skeletal homeostasis. OVX inhibited the age-related thickening of cortical bone only in C57BL/6. In both strains, increments in bone mineral content were significantly lower with OVX only in the proximal tibia, with intervention-related differences increasing with time. OVX had no effect on μFE estimates of stiffness nor failure load in either strain. The results of this study show strain-, time- and region-(trabecular or cortical) dependent changes in morphometric and densitometric properties. These findings highlight the importance of choosing an appropriate mouse model and time points for research of treatments against accelerated bone resorption.

A new method to monitor bone geometry changes at different spatial scales in the longitudinal in vivo μCT studies of mice bones

Longitudinal studies of bone adaptation in mice using in vivo micro-computed tomography (μCT) have been commonly used for pre-clinical evaluation of physical and pharmacological interventions. The main advantage of this approach is to use each mouse as its own control, reducing considerably the sample size required by statistical power analysis. To date, multi-scale estimation of bone adaptations become essential since the bone activity that takes place at different scales may be associated with different bone mechanisms. Measures of bone adaptations at different time scales have been attempted in a previous study. This paper extends quantification of bone activity at different spatial scales with a proposition of a novel framework. The method involves applying level-set method (LSM) to track the geometric changes from the longitudinal in vivo μCT scans of mice tibia. Bone low- and high-spatial frequency patterns are then estimated using multi-resolution analysis. The accuracy of the framework is quantified by applying it to two times separated scanned images with synthetically manipulated global and/or local activity. The Root Mean Square Deviation (RMSD) was approximately 1.5 voxels or 0.7 voxels for the global low-spatial frequency or local high-spatial frequency changes, respectively. The framework is further applied to the study of bone changes in longitudinal datasets of wild-type mice tibiae over time and space. The results demonstrate the ability for the spatio-temporal quantification and visualisation of bone activity at different spatial scales in longitudinal studies thus providing further insight into bone adaptation mechanisms.