A new framework for computational biomechanical models and 3Rs in musculoskeletal research.

该项目有两个首要目标：（1）调查肌肉骨骼计算机模型提供准确预测所需的实验输入数据的类型和数量，以及（2）在此过程中提供有关模型当前和未来潜力的定量数据，以减少，替代和改进（3R）科学研究中的动物实验。为了做到这一点，我们将开发和验证新的计算生物力学模型，使用兔咀嚼作为我们的案例研究。验证计算机模型需要大量关于兔子解剖学和进食力学（例如肌肉和咬合力）的实验数据。这一数据不存在于兔子或任何其他实验动物身上。因此，系统的解剖学和生物力学研究兔喂养是必要的，其中所有的主要决定因素的喂养力学是从一个队列的兔子测量。然后，根据这些兔子的医学成像数据构建的计算模型可以直接和立即用于改进和验证计算机模拟。只有这样才能真正验证模型，并证明其在未来研究中实现3R的潜力。因此，我们的具体目标是收集：在兔子的骨骼和肌肉形态的解剖和图像数据;在体内的骨骼运动和肌肉生理数据，因为他们吃各种食物类型;和联合收割机这些数据建立和验证新的计算机模型的兔子喂养biomechanics.Rabbits已被选中，因为它们被广泛用于各种研究领域。它们是牙科种植体设计和骨（再）生长研究的首选实验动物，因为它们的大小，易于操作，在骨组成，愈合和解剖学方面与人类相对相似。这些实验，就像许多肌肉骨骼研究一样，是高度侵入性的，在动物被安乐死之前会给它们带来疼痛和痛苦。数字模型有可能完全取代（或最大限度地减少）肌肉骨骼研究和/或医疗器械设计中的动物使用。数字模型的解剖结构和行为可以无限制地改变和重新测试，并且对真实的动物没有任何伤害或痛苦。这也可以允许，例如：将模型分析扩展到同一物种的不同品系/品种（或类似物种）通过解剖结构/行为的数字修改;以多种方式修改解剖结构的元素（例如牙齿/骨头的切除）以检查不同手术方法的后果;以及将植入装置数字化地插入模型中，并检查它们对性能的影响，所有这些都不需要在真实的动物上进行任何有害的实验。但是，改进生物力学模型不仅可以减少研究中的动物使用，而且有可能改进人体生物力学模型。目前，模型被广泛用于研究健康的生物力学（如运动表现），衰老（如骶骨）和相关疾病（如膝盖骨关节炎），牙科手术（如正畸治疗）和损伤（如髋部骨折）。在这些人体研究中，它们用于估计或预测无法在人体中直接测量的参数，因此其准确性本质上难以评估。因此，我们在这里提出的研究类型显然是必要的。首先，我们将使用我们详尽的和最先进的实验数据集生成迄今为止最全面的生物力学模型。这将为模型准确性提供最佳情况。然后，我们将逐步降低模型输入数据的分辨率，并观察对准确性的影响。这将告诉我们单个输入参数如何影响精度，并帮助肌肉骨骼研究界确定哪些参数不需要通过在真实的动物中进行实验来测量，以达到必要的精度。

This project has two overarching goals: (1) to investigate the type and amount of experimental input data required for musculoskeletal computer models to deliver accurate predictions, and (2) in doing so provide quantitative data on the current and future potential of models to contribute to the reduction, replacement and refinement (3Rs) of animal experiments in scientific research. To do this we will develop and validate new computational biomechanical models using mastication in rabbits as our case study. Validating computer models requires a large amount of experimental data about rabbit anatomy and feeding mechanics (e.g. muscle and bite forces). This data does not exist for rabbits, or indeed any other experimental animal. Therefore a systematic anatomical and biomechanical investigation of rabbit feeding is required in which all the primary determinants of feeding mechanics are measured from a cohort of rabbits. Computational models constructed from medical imaging data of those same rabbits can then be directly and immediately used to improve and validate computer simulations. Only in this way can models be truly validated and their potential for achieving 3Rs in future studies be demonstrated. Our specific objectives are therefore to collect: anatomical and image data on bone and muscle morphology in rabbits; in vivo data on bone motion and muscle physiology as they eat various food types; and combine these data to build and validate new computer models of rabbit feeding biomechanics.Rabbits have been chosen because they are widely used in a variety of research areas. They are the first-choice experimental animal for dental implant design and bone (re)growth studies because of their size, easy handling and relative similarities to humans in terms of bone composition, healing and anatomy. These experiments, like many in musculoskeletal research, are highly invasive, causing pain and distress to the animals before they are euthanized. A digital model has the potential to completely replace (or maximally reduce) the use of animals in musculoskeletal research and/or medical device design. The anatomy and behaviour of a digital model can be altered and re-tested without limitation and without any harm or distress to a real animal. This can also allow, for example: a model analysis to be extended to a different strain/breed of the same species (or a similar species) by digital modification of the anatomy/behaviour; elements of anatomy to be modified in multiple ways (e.g. removal of teeth/bone) to examine the consequences of different surgical approaches; and for implant devices to be digitally inserted into the models, and their impact on performance examined, all without the need for any harmful experimentation on real animals. But improving biomechanical models will not only reduce animal use in research, but has the potential to improve modelling of human biomechanics. Currently models are used widely to study healthy biomechanics (e.g. sports performance), ageing (e.g. sacropenia) and related diseases (e.g. knee osteoarithitis), dental procedures (e.g. orthodontic treatment) and injury (e.g. hip fracture). In these human studies they are used to estimate or predict parameters that cannot be measured directly in people, thus their accuracy is inherently difficult to assess. Thus there is clear need for the type of study we propose here.In the first instance we will generate the most comprehensive biomechanical models produced to-date using our exhaustive and state-of-the-art experimental dataset. This will provide a best-case scenario for model accuracy. We will then incrementally reduce the resolution of input data given to the model and observe the effects on accuracy. This will tell us how individual input parameters effect accuracy and help the musculoskeletal research community identify which parameters do not need to be measured through experimentation in real animals to achieve the necessary accuracy.