Aurélie Carlier

Current views on calcium phosphate osteogenicity and the translation into effective bone regeneration strategies

Calcium phosphate (CaP) has traditionally been used for the repair of bone defects because of its strong resemblance to the inorganic phase of bone matrix. Nowadays, a variety of natural or synthetic CaP-based biomaterials are produced and have been extensively used for dental and orthopaedic applications. This is justified by their biocompatibility, osteoconductivity and osteoinductivity (i.e. the intrinsic material property that initiates de novo bone formation), which are attributed to the chemical composition, surface topography, macro/microporosity and the dissolution kinetics. However, the exact molecular mechanism of action is unknown. This review paper first summarizes the most important aspects of bone biology in relation to CaP and the mechanisms of bone matrix mineralization. This is followed by the research findings on the effects of calcium (Ca²⁺) and phosphate (PO₄³⁻) ions on the migration, proliferation and differentiation of osteoblasts during in vivo bone formation and in vitro culture conditions. Further, the rationale of using CaP for bone regeneration is explained, focusing thereby specifically on the materials osteoinductive properties. Examples of different material forms and production techniques are given, with the emphasis on the state-of-the art in fine-tuning the physicochemical properties of CaP-based biomaterials for improved bone induction and the use of CaP as a delivery system for bone morphogenetic proteins. The use of computational models to simulate the CaP-driven osteogenesis is introduced as part of a bone tissue engineering strategy in order to facilitate the understanding of cell-material interactions and to gain further insight into the design and optimization of CaP-based bone reparative units. Finally, limitations and possible solutions related to current experimental and computational techniques are discussed.

MOSAIC: a multiscale model of osteogenesis and sprouting angiogenesis with lateral inhibition of endothelial cells.

The healing of a fracture depends largely on the development of a new blood vessel network (angiogenesis) in the callus. During angiogenesis tip cells lead the developing sprout in response to extracellular signals, amongst which vascular endothelial growth factor (VEGF) is critical. In order to ensure a correct development of the vasculature, the balance between stalk and tip cell phenotypes must be tightly controlled, which is primarily achieved by the Dll4-Notch1 signaling pathway. This study presents a novel multiscale model of osteogenesis and sprouting angiogenesis, incorporating lateral inhibition of endothelial cells (further denoted MOSAIC model) through Dll4-Notch1 signaling, and applies it to fracture healing. The MOSAIC model correctly predicted the bone regeneration process and recapitulated many experimentally observed aspects of tip cell selection: the salt and pepper pattern seen for cell fates, an increased tip cell density due to the loss of Dll4 and an excessive number of tip cells in high VEGF environments. When VEGF concentration was even further increased, the MOSAIC model predicted the absence of a vascular network and fracture healing, thereby leading to a non-union, which is a direct consequence of the mutual inhibition of neighboring cells through Dll4-Notch1 signaling. This result was not retrieved for a more phenomenological model that only considers extracellular signals for tip cell migration, which illustrates the importance of implementing the actual signaling pathway rather than phenomenological rules. Finally, the MOSAIC model demonstrated the importance of a proper criterion for tip cell selection and the need for experimental data to further explore this. In conclusion, this study demonstrates that the MOSAIC model creates enhanced capabilities for investigating the influence of molecular mechanisms on angiogenesis and its relation to bone formation in a more mechanistic way and across different time and spatial scales.

Oxygen as a critical determinant of bone fracture healing - a multiscale model

A timely restoration of the ruptured blood vessel network in order to deliver oxygen and nutrients to the fracture zone is crucial for successful bone healing. Indeed, oxygen plays a key role in the aerobic metabolism of cells, in the activity of a myriad of enzymes as well as in the regulation of several (angiogenic) genes. In this paper, a previously developed model of bone fracture healing is further improved with a detailed description of the influence of oxygen on various cellular processes that occur during bone fracture healing. Oxygen ranges of the cell-specific oxygen-dependent processes were established based on the state-of-the art experimental knowledge through a rigorous literature study. The newly developed oxygen model is compared with previously published experimental and in silico results. An extensive sensitivity analysis was also performed on the newly introduced oxygen thresholds, indicating the robustness of the oxygen model. Finally, the oxygen model was applied to the challenging clinical case of a critical sized defect (3mm) where it predicted the formation of a fracture non-union. Further model analyses showed that the harsh hypoxic conditions in the central region of the callus resulted in cell death and disrupted bone healing thereby indicating the importance of a timely vascularization for the successful healing of a large bone defect. In conclusion, this work demonstrates that the oxygen model is a powerful tool to further unravel the complex spatiotemporal interplay of oxygen delivery, diffusion and consumption with the several healing steps, each occurring at distinct, optimal oxygen tensions during the bone repair process.

Size Does Matter: An Integrative In Vivo-In Silico Approach for the Treatment of Critical Size Bone Defects

Although bone has a unique restorative capacity, i.e., it has the potential to heal scarlessly, the conditions for spontaneous bone healing are not always present, leading to a delayed union or a non-union. In this work, we use an integrative in vivo - in silico approach to investigate the occurrence of non-unions, as well as to design possible treatment strategies thereof. The gap size of the domain geometry of a previously published mathematical model was enlarged in order to study the complex interplay of blood vessel formation, oxygen supply, growth factors and cell proliferation on the final healing outcome in large bone defects. The multiscale oxygen model was not only able to capture the essential aspects of in vivo non-unions, it also assisted in understanding the underlying mechanisms of action, i.e., the delayed vascularization of the central callus region resulted in harsh hypoxic conditions, cell death and finally disrupted bone healing. Inspired by the importance of a timely vascularization, as well as by the limited biological potential of the fracture hematoma, the influence of the host environment on the bone healing process in critical size defects was explored further. Moreover, dependent on the host environment, several treatment strategies were designed and tested for effectiveness. A qualitative correspondence between the predicted outcomes of certain treatment strategies and experimental observations was obtained, clearly illustrating the models potential. In conclusion, the results of this study demonstrate that due to the complex non-linear dynamics of blood vessel formation, oxygen supply, growth factor production and cell proliferation and the interactions thereof with the host environment, an integrative in silico-in vivo approach is a crucial tool to further unravel the occurrence and treatments of challenging critical sized bone defects.

Bringing computational models of bone regeneration to the clinic

Although the field of bone regeneration has experienced great advancements in the last decades, integrating all the relevant, patient‐specific information into a personalized diagnosis and optimal treatment remains a challenging task due to the large number of variables that affect bone regeneration. Computational models have the potential to cope with this complexity and to improve the fundamental understanding of the bone regeneration processes as well as to predict and optimize the patient‐specific treatment strategies. However, the current use of computational models in daily orthopedic practice is very limited or inexistent. We have identified three key hurdles that limit the translation of computational models of bone regeneration from bench to bed side. First, there exists a clear mismatch between the scope of the existing and the clinically required models. Second, most computational models are confronted with limited quantitative information of insufficient quality thereby hampering the determination of patient‐specific parameter values. Third, current computational models are only corroborated with animal models, whereas a thorough (retrospective and prospective) assessment of the computational model will be crucial to convince the health care providers of the capabilities thereof. These challenges must be addressed so that computational models of bone regeneration can reach their true potential, resulting in the advancement of individualized care and reduction of the associated health care costs. WIREs Syst Biol Med 2015, 7:183–194. doi: 10.1002/wsbm.1299

Computational model-informed design and bioprinting of cell-patterned constructs for bone tissue engineering

Three-dimensional (3D) bioprinting is a rapidly advancing tissue engineering technology that holds great promise for the regeneration of several tissues, including bone. However, to generate a successful 3D bone tissue engineering construct, additional complexities should be taken into account such as nutrient and oxygen delivery, which is often insufficient after implantation in large bone defects. We propose that a well-designed tissue engineering construct, that is, an implant with a specific spatial pattern of cells in a matrix, will improve the healing outcome. By using a computational model of bone regeneration we show that particular cell patterns in tissue engineering constructs are able to enhance bone regeneration compared to uniform ones. We successfully bioprinted one of the most promising cell-gradient patterns by using cell-laden hydrogels with varying cell densities and observed a high cell viability for three days following the bioprinting process. In summary, we present a novel strategy for the biofabrication of bone tissue engineering constructs by designing cell-gradient patterns based on a computational model of bone regeneration, and successfully bioprinting the chosen design. This integrated approach may increase the success rate of implanted tissue engineering constructs for critical size bone defects and also can find a wider application in the biofabrication of other types of tissue engineering constructs.

Sensitivity Analysis by Design of Experiments

The design of experiments (DOE) is a valuable method for studying the influence of one or more factors on the outcome of computer experiments. There is no limit to the number of times a computer experiment can be run, but they are often time-consuming. Moreover, the number of parameters in a computer model is often very large and the range of variation for each of these parameters is often quite extensive. The DOE provides the statistical tools necessary for choosing a minimum amount of parameter combinations resulting in as much information as possible about the computer model. In this chapter, several designs and analysing methods are explained. At the end of the chapter, these designs and methods are applied to a mechanobiological model describing tooth movement.

Capturing the wide variety of impaired fracture healing phenotypes in Neurofibromatosis Type 1 with eight key factors: a computational study

Congenital pseudarthrosis of the tibia (CPT) is a rare disease which normally presents itself during early childhood by anterolateral bowing of the tibia and spontaneous tibial fractures. Although the exact etiology of CPT is highly debated, 40–80% of CPT patients are carriers of a mutation in the Neurofibromatosis Type 1 (NF1) gene, which can potentially result in an altered phenotype of the skeletal cells and impaired bone healing. In this study we use a computational model of bone regeneration to examine the effect of the Nf1 mutation on bone fracture healing by altering the parameter values of eight key factors which describe the aberrant cellular behaviour of Nf1 haploinsufficient and Nf1 bi-allelically inactivated cells. We show that the computational model is able to predict the formation of a hamartoma as well as a wide variety of CPT phenotypes through different combinations of altered parameter values. A sensitivity analysis by “Design of Experiments” identified the impaired endochondral ossification process and increased infiltration of fibroblastic cells as key contributors to the degree of severity of CPT. Hence, the computational model results have added credibility to the experimental hypothesis of a genetic cause (i.e. Nf1 mutation) for CPT.

How Not To Drown in Data: A Guide for Biomaterial Engineers

High-throughput assays that produce hundreds of measurements per sample are powerful tools for quantifying cell-material interactions. With advances in automation and miniaturization in material fabrication, hundreds of biomaterial samples can be rapidly produced, which can then be characterized using these assays. However, the resulting deluge of data can be overwhelming. To the rescue are computational methods that are well suited to these problems. Machine learning techniques provide a vast array of tools to make predictions about cell-material interactions and to find patterns in cellular responses. Computational simulations allow researchers to pose and test hypotheses and perform experiments in silico. This review describes approaches from these two domains that can be brought to bear on the problem of analyzing biomaterial screening data.

In silico clinical trials for pediatric orphan diseases

To date poor treatment options are available for patients with congenital pseudarthrosis of the tibia (CPT), a pediatric orphan disease. In this study we have performed an in silico clinical trial on 200 virtual subjects, generated from a previously established model of murine bone regeneration, to tackle the challenges associated with the small, pediatric patient population. Each virtual subject was simulated to receive no treatment and bone morphogenetic protein (BMP) treatment. We have shown that the degree of severity of CPT is significantly reduced with BMP treatment, although the effect is highly subject-specific. Using machine learning techniques we were also able to stratify the virtual subject population in adverse responders, non-responders, responders and asymptomatic. In summary, this study shows the potential of in silico medicine technologies as well as their implications for other orphan diseases.