Virtual Mouse and Human Twins for optimising Treatments for Osteoporosis

80%的药物干预在患者身上失败，即使在动物实验中取得成功后也是如此。肌肉骨骼(MSK)疾病，如骨质疏松症(OP)，极大地降低了数百万受影响患者的生活质量。小鼠是测试新疗法的最常见的动物模型。然而，它们对患者的影响的推断和确定哪些新的治疗方法应该在临床研究中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，通过在小鼠体内的实验在老鼠和人之间架起桥梁，以发现患者的最佳治疗方法。我将创建两个平行的虚拟小鼠和人类双胞胎(VMHTs-OP)，基于相似的输入(生物医学图像、细胞数据、步态数据)，预测骨在生物力学和/或生化刺激作用下的适应。每个虚拟双胞胎都将基于先进的多尺度计算模型(多体动力学、有限元和细胞群体模型)来预测由于OP和新的生物力学和药物治疗而导致的骨适应随时间和空间的变化，在电子计算机中识别可能对患者有效的新的联合治疗方法，并在未来的临床试验中进行测试。这些模型将经历全面的验证、验证和不确定性量化过程，以便为未来的临床前应用提供所需的可信度。模型预测将根据小鼠纵向实验和已知生物力学或药物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电子计算机中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、高应变运动)，这些在动物和临床试验中既不符合伦理也不经济。

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.