Virtual Mouse and Human Twins for optimising Treatments for Osteoporosis
Lead Research Organisation:
University of Sheffield
Department Name: Oncology and Metabolism
Abstract
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 tested
in 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.
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 tested
in 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.
Organisations
People |
ORCID iD |
| Enrico Dall'Ara (Principal Investigator) |