Generation Of Patient-specific Models Research Articles

Preclinical to clinical translation for intervertebral disc repair: Effects of species-specific scale, metabolism, and matrix synthesis rates on cell-based regeneration.

A significant hurdle for potential cell-based therapies is the subsequent survival and regenerative capacity of implanted cells. While many exciting developments have demonstrated promise preclinically, cell-based therapies for intervertebral disc (IVD) degeneration fail to translate equivalent clinical efficacy. This work aims to ascertain the clinical relevance of both a small and large animal model by experimentally investigating and comparing these animal models to human from the perspective of anatomical scale and their cellular metabolic and regenerative potential. First, this work experimentally investigated species-specific geometrical scale, native cell density, nutrient metabolism, and matrix synthesis rates for rat, goat, and human disc cells in a 3D microspheroid configuration. Second, these parameters were employed in silico to elucidate species-specific nutrient microenvironments and predict differences in temporal regeneration between animal models. This work presents in silico models which correlate favorably to preclinical literature in terms of the capabilities of animal regeneration and predict that compromised nutrition is not a significant challenge in small animal discs. On the contrary, it highlights a very fine clinical balance between an adequate cell dose for sufficient repair, through de novo matrix deposition, without exacerbating the human microenvironmental niche. Overall, this work aims to provide a path towards understanding the effect of cell injection number on the nutrient microenvironment and the "time to regeneration" between preclinical animal models and the large human IVD. While these findings help to explain failed translation of promising preclinical data and the limited results emerging from clinical trials at present, they also enable the research field and clinicians to manage expectations on cell-based regeneration. Ultimately, this work provides a platform to inform the design of clinical trials, and as computing power and software capabilities increase in the future, it is conceivable that generation of patient-specific models could be used for patient assessment, as well as pre- and intraoperative planning.

Enhanced optimization-based method for the generation of patient-specific models of Purkinje networks

Cardiac Purkinje networks are a fundamental part of the conduction system and are known to initiate a variety of cardiac arrhythmias. However, patient-specific modeling of Purkinje networks remains a challenge due to their high morphological complexity. This work presents a novel method based on optimization principles for the generation of Purkinje networks that combines geometric and activation accuracy in branch size, bifurcation angles, and Purkinje-ventricular-junction activation times. Three biventricular meshes with increasing levels of complexity are used to evaluate the performance of our approach. Purkinje-tissue coupled monodomain simulations are executed to evaluate the generated networks in a realistic scenario using the most recent Purkinje/ventricular human cellular models and physiological values for the Purkinje-ventricular-junction characteristic delay. The results demonstrate that the new method can generate patient-specific Purkinje networks with controlled morphological metrics and specified local activation times at the Purkinje-ventricular junctions.

The Potential of Induced Pluripotent Stem Cells to Test Gene Therapy Approaches for Neuromuscular and Motor Neuron Disorders.

The reprogramming of somatic cells into induced pluripotent stem cells (iPSCs) represents a major advance for the development of human disease models. The emerging of this technique fostered the concept of “disease in a dish,” which consists into the generation of patient-specific models in vitro. Currently, iPSCs are used to study pathological molecular mechanisms caused by genetic mutations and they are considered a reliable model for high-throughput drug screenings. Importantly, precision-medicine approaches to treat monogenic disorders exploit iPSCs potential for the selection and validation of lead candidates. For example, antisense oligonucleotides (ASOs) were tested with promising results in myoblasts or motor neurons differentiated from iPSCs of patients affected by either Duchenne muscular dystrophy or Amyotrophic lateral sclerosis. However, the use of iPSCs needs additional optimization to ensure translational success of the innovative strategies based on gene delivery through adeno associated viral vectors (AAV) for these diseases. Indeed, to establish an efficient transduction of iPSCs with AAV, several aspects should be optimized, including viral vector serotype, viral concentration and timing of transduction. This review will outline the use of iPSCs as a model for the development and testing of gene therapies for neuromuscular and motor neuron disorders. It will then discuss the advantages for the use of this versatile tool for gene therapy, along with the challenges associated with the viral vector transduction of iPSCs.

[The Future of Vascular Medicine - Role of 3D Printing].

Digitalisation is one of the key challenges in current surgery and will impact the future of surgical care as well as upcoming generations of surgeons. 3D printing is a technology that has recently been transferred from industrial prototyping into cardiovascular medicine. The digital model of the anatomical structure which needs to be engineered represents the inherent link of 3D printing to digital medicine. 3D printing technology is able to provide the surgeon with patient-specific models of anatomy and disease for surgical planning and patient informed consent as well as training templates for students and residents, surgical templates and even ready-to-use surgical implants. In our service, we have established a full-inhouse workflow for 3D printing and we currently use this technology for the generation of patient-specific models, training templates and for patient education, as will be presented in this article. Future advances in software solutions, printing polymers and easy-to-handle printers will further propagate and expand the applicability of this technology in cardiovascular medicine.

From Finite Element Meshes to Clouds of Points: A Review of Methods for Generation of Computational Biomechanics Models for Patient-Specific Applications.

It has been envisaged that advances in computing and engineering technologies could extend surgeons' ability to plan and carry out surgical interventions more accurately and with less trauma. The progress in this area depends crucially on the ability to create robustly and rapidly patient-specific biomechanical models. We focus on methods for generation of patient-specific computational grids used for solving partial differential equations governing the mechanics of the body organs. We review state-of-the-art in this area and provide suggestions for future research. To provide a complete picture of the field of patient-specific model generation, we also discuss methods for identifying and assigning patient-specific material properties of tissues and boundary conditions.

Computed tomography landmark-based semi-automated mesh morphing and mapping techniques: Generation of patient specific models of the human pelvis without segmentation

Current methods for the development of pelvic finite element (FE) models generally are based upon specimen specific computed tomography (CT) data. This approach has traditionally required segmentation of CT data sets, which is time consuming and necessitates high levels of user intervention due to the complex pelvic anatomy. The purpose of this research was to develop and assess CT landmark-based semi-automated mesh morphing and mapping techniques to aid the generation and mechanical analysis of specimen-specific FE models of the pelvis without the need for segmentation. A specimen-specific pelvic FE model (source) was created using traditional segmentation methods and morphed onto a CT scan of a different (target) pelvis using a landmark-based method. The morphed model was then refined through mesh mapping by moving the nodes to the bone boundary. A second target model was created using traditional segmentation techniques. CT intensity based material properties were assigned to the morphed/mapped model and to the traditionally segmented target models. Models were analyzed to evaluate their geometric concurrency and strain patterns. Strains generated in a double-leg stance configuration were compared to experimental strain gauge data generated from the same target cadaver pelvis. CT landmark-based morphing and mapping techniques were efficiently applied to create a geometrically multifaceted specimen-specific pelvic FE model, which was similar to the traditionally segmented target model and better replicated the experimental strain results (R2=0.873). This study has shown that mesh morphing and mapping represents an efficient validated approach for pelvic FE model generation without the need for segmentation.

Accounting for patient variability in finite element analysis of the intact and implanted hip and knee: A review

It is becoming increasingly difficult to differentiate the performance of new joint replacement designs using available preclinical test methods. Finite element analysis is commonly used and the majority of published studies are performed on representative anatomy, assuming optimal implant placement, subjected to idealised loading conditions. There are significant differences between patients and accounting for this variability will lead to better assessment of the risk of failure. This review paper provides a comprehensive overview of the techniques available to account for patient variability. There is a brief overview of patient-specific model generation techniques, followed by a review of multisubject patient-specific studies performed on the intact and implanted femur and tibia. In particular, the challenges and limitations of manually generating models for such studies are discussed. To efficiently account for patient variability, the application of statistical shape and intensity models (SSIM) are being developed. Such models have the potential to synthetically generate thousands of representative models generated from a much smaller training set. Combined with the automation of the prosthesis implantation process, SSIM provides a potentially powerful tool for assessing the next generation of implant designs. The potential application of SSIM are discussed along with their limitations.