Computational biomechanical modelling to predict musculoskeletal dynamics: application for 3Rs and changing muscle-bone dynamics

预测肌肉骨骼动力学的计算生物力学模型：3R 的应用和改变肌肉骨骼动力学

• 批准号: BB/Y00180X/1
• 负责人: Peter Watson
• 金额: $ 59.83万
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FY00180X%2F1
• 关键词: Computational; biomechanical; modelling; predict; musculoskeletal

This project has three goals: 1) to measure how muscles and bone adapt when a muscle/s are no longer able to function normally (e.g. injury). This will investigate the compensatory roles muscle develop in order to maintain functional movement, how their properties adapt to facilitate this movement, and how this affects bone growth; 2) to create and validate computational models that can predict how muscles and bone adapt when there is disruption to the "normal" functioning of the musculoskeletal system; 3) investigate the quantity of experimental input data required for the computational models to deliver accurate predictions.The outputs from this project will not only help researchers understand how the musculoskeletal system adapts to changes to "normal" function, but will also generate computational models that can replicate biomedical experiments that are frequently performed on animals. Such experiments are performed to test a range of things, such as the effects of disease/injury and biomedical devices on the musculoskeletal system. These experimentations, like many in musculoskeletal research, are highly invasive, and cause pain and distress to the animals before they are euthanized. Advances in computational modelling now enable models to predict how the body reacts to the dysfunctions of the musculoskeletal system caused by such experiments. Through replicating biomedical experiments, computational modelling has the potential to reduce, or even replace, the use of animals in musculoskeletal research and medical device design. The anatomy and behaviour of a computational model can be altered and re-tested without limitation to allow, for example: a model analysis to be extended to a different species by digital modification of the anatomy/behaviour; elements of anatomy to be modified in multiple ways (e.g. removal of muscle/bone) to examine the consequences of different surgical approaches; and for implant devices to be digitally inserted, all without the need for any harmful experimentation on real animals.The application of such computational modelling is still limited, so unfortunately a large number of animals are still used in biomedical experiments. There are many reasons for this, including the fact the building these models requires in-depth knowledge, and general scepticism that modelling can predict the outcomes of experiments with a high level of accuracy. We intend to address these issues by creating computational models of the rabbit that are validated against the form of experiments they are intended to reduce, or even replace. This validation requires a large amount of experimental data about how the rabbit bone and muscles adapt to dysfunctions of the musculoskeletal system. We will therefore collect detailed in vivo data on bone motion and muscle physiology at several time periods, to inform how rabbit bone and muscles adapt when there is alteration to the "normal" functioning of another muscle. This data will used to: 1) provide input data for the computational modelling; 2) determine the accuracy of the model predictions, thus determining the model validity.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 growth studies because of their size, easy handling and relative similarities to humans in terms of bone composition and healing. However, this project also 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. osteoarithitis), dental procedures (e.g. orthodontic treatment) and injury (e.g. fracture). These human studies often estimate or predict parameters that cannot be measured directly in people, thus there is a clear need for accurate "off the self" computational models that we propose here.

该项目有三个目标：1）测量当肌肉不再能够正常发挥功能（例如受伤）时肌肉和骨骼如何适应。这将研究肌肉为维持功能性运动而发展的代偿作用，它们的特性如何适应以促进这种运动，以及这如何影响骨骼生长； 2）创建并验证计算模型，该模型可以预测当肌肉骨骼系统的“正常”功能受到干扰时肌肉和骨骼如何适应； 3）调查计算模型提供准确预测所需的实验输入数据的数量。该项目的输出不仅可以帮助研究人员了解肌肉骨骼系统如何适应“正常”功能的变化，而且还将生成计算模型可以复制经常在动物身上进行的生物医学实验。进行此类实验是为了测试一系列事物，例如疾病/损伤和生物医学设备对肌肉骨骼系统的影响。与肌肉骨骼研究中的许多实验一样，这些实验具有高度侵入性，在动物被安乐死之前会给它们带来痛苦和痛苦。计算模型的进步现在使模型能够预测身体对此类实验引起的肌肉骨骼系统功能障碍的反应。通过复制生物医学实验，计算模型有可能减少甚至取代肌肉骨骼研究和医疗设备设计中动物的使用。计算模型的解剖结构和行为可以不受限制地改变和重新测试，例如：通过解剖结构/行为的数字修改将模型分析扩展到不同的物种；以多种方式修改解剖结构元素（例如去除肌肉/骨骼）以检查不同手术方法的后果；并且植入设备可以数字化插入，所有这些都不需要在真实动物上进行任何有害的实验。这种计算模型的应用仍然有限，因此不幸的是，大量的动物仍然被用于生物医学实验。造成这种情况的原因有很多，包括构建这些模型需要深入的知识，以及人们普遍怀疑建模能否高精度地预测实验结果。我们打算通过创建兔子的计算模型来解决这些问题，这些模型根据旨在减少甚至取代的实验形式进行验证。这种验证需要大量有关兔子骨骼和肌肉如何适应肌肉骨骼系统功能障碍的实验数据。因此，我们将在几个时间段收集有关骨骼运动和肌肉生理学的详细体内数据，以了解当另一块肌肉的“正常”功能发生改变时，兔子的骨骼和肌肉如何适应。该数据将用于：1）为计算建模提供输入数据； 2）确定模型预测的准确性，从而确定模型的有效性。选择兔子是因为它们广泛应用于各种研究领域。它们是牙种植体设计和骨骼生长研究的首选实验动物，因为它们体积大、易于操作，并且在骨骼成分和愈合方面与人类相对相似。然而，该项目还具有改进人体生物力学建模的潜力。目前模型广泛用于研究健康生物力学（例如运动表现）、衰老（例如骶骨减少症）和相关疾病（例如骨关节炎）、牙科手术（例如正畸治疗）和损伤（例如骨折）。这些人类研究经常估计或预测无法直接在人体中测量的参数，因此显然需要我们在此提出的准确的“脱离自我”计算模型。