Virtual Mouse and Human Twins for optimising Treatments for Osteoporosis

虚拟小鼠和人类双胞胎用于优化骨质疏松症的治疗

• 批准号: EP/Z000203/1
• 负责人: Enrico Dall'Ara
• 金额: $ 221.76万
• 依托单位: University of Sheffield
• 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=EP%2FZ000203%2F1
• 关键词: Virtual; Mouse; Human; Twins; optimising

Eighty per cent of pharmaceutical interventions fail in patients even after being successful in animal studies. Musculoskeletal (MSK) diseases such as osteoporosis (OP) reduce dramatically the quality of life of millions of affected patients. Mice are the most common animal model to test new treatments. Nevertheless, the extrapolation of their effect onto patients and the identification of which new treatments should be tested in clinical studies is based on simple scaling approaches.In this project I will develop a new mechanistic computational framework that bridges between mouse and human, informed by in vivo experiments in mice, to discover optimal treatments in patients. I will create two parallel virtual mouse and human twins (VMHTs-OP), based on similar inputs (biomedical images, cell data, gait data) that will predict bone adaptation in function of biomechanical and/or biochemical stimuli. Each virtual twin will be based on advanced multi-scale computational models (multi-body dynamics, finite element and cell-population models) to predict bone adaptation over time and space due to OP and to new biomechanical and pharmacological treatments, identifying in silico the new combined treatments that are likely to be effective in patients, to be testedin future clinical trials.The models will be going through a comprehensive verification, validation and uncertainties quantification process in order to provide the required credibility for future preclinical applications. The model predictions will be validated against longitudinal mouse experiments and available longitudinal clinical data from known biomechanical or pharmacological interventions. Finally, the validated framework will be used to test in silico several combinations of treatments regimens (overlap, intermitted, drug holidays) and different interventions (microgravity, high strain exercises) that would not be ethically nor economically testable in animal and clinical trials.

80%的药物干预即使在动物实验中取得成功后，在病人身上也失败了。骨质疏松症（OP）等肌肉骨骼（MSK）疾病极大地降低了数百万受影响患者的生活质量。老鼠是测试新疗法最常见的动物模型。然而，它们对患者的影响的外推和确定哪些新疗法应该在临床研究中进行测试是基于简单的缩放方法。在这个项目中，我将开发一个新的机械计算框架，在小鼠和人类之间架起桥梁，通过小鼠体内实验来发现患者的最佳治疗方法。我将创建两个并行的虚拟小鼠和人类双胞胎（VMHTs-OP），基于类似的输入（生物医学图像，细胞数据，步态数据），预测骨骼在生物力学和/或生化刺激下的适应功能。每个虚拟双胞胎将基于先进的多尺度计算模型（多体动力学，有限元和细胞群模型）来预测由于OP和新的生物力学和药物治疗而导致的骨随时间和空间的适应，通过计算机识别可能对患者有效的新的联合治疗，以便在未来的临床试验中进行测试。这些模型将经过全面的验证、确认和不确定度量化过程，以便为未来的临床前应用提供所需的可信度。模型预测将通过纵向小鼠实验和已知生物力学或药理学干预的可用纵向临床数据进行验证。最后，经过验证的框架将用于在计算机上测试几种治疗方案的组合（重叠、间断、药物假期）和不同的干预措施（微重力、高应变练习），这些干预措施在动物和临床试验中既不符合伦理也不符合经济标准。