VPHOP
VPHOP or the Osteoporotic Virtual Physiological Human is a European osteoporosis research project within the framework of the Virtual Physiological Human initiative.
Until August 2012, the VPHOP research project will develop, validate and deploy the next generation of technology to predict the absolute risk of fracture in patients with low bone mass, thereby enabling clinicians to provide better prognoses and implement more effective treatment strategies (both pharmacological and interventional).
Why?
Approximately four million osteoporotic bone fractures cost the European health system more than 30 billion Euro per year. This figure could double by 2050. After the first fracture, the chances of having another one increase by 86%. With current technology osteoporotic fractures can be predicted with an accuracy of 60-70% or less (tossing a coin would give 50%). Better ways to prevent osteoporotic fractures are needed.
What?
Current fracture predictions are based on historical, fracture-patient data sets to identify key factors which contribute to the increased probability of an osteoporotic fracture. This approach oversimplifies the mechanisms leading to an osteoporotic fracture and fail to take into account numerous, hierarchical factors which are unique to the individual. These factors span cell-level to body-level functions, i.e.:
- Body: Musculoskeletal anatomy and neuromotor control define the daily loading spectrum, including paraphysiological overloading events
- Organ: Fracture events occur at organ level and are influenced by the elasticity and geometry of bone
- Tissue: Bone elasticity and geometry are determined by tissue morphology
- Cell: Cell activity changes tissue morphology and composition over time
- Constituents: Constituents of the extracellular matrix are the prime determinants of tissue strength
Accuracy could be dramatically improved if a more deterministic approach was used which accounts for those factors and their variation between individuals.
How?
By creating a patient-specific hypermodel, a model composed by many sub-models, each describing the relevant phenomena taking place at one of the many dimensional scales involved, this incredibly complex problem may be solved.
The aim of VPHOP is to develop a multiscale modelling technology based on conventional diagnostic imaging methods that makes it possible, in a clinical setting, to predict for each patient the strength of his/her bones, how this strength is likely to change over time, and the probability that he/she will overload his/her bones during daily life. With these three predictions, the evaluation of the absolute risk of bone fracture will be much more accurate than any prediction based on external and indirect determinants, as is current clinical practice. These predictions will be used to:
- Improve the diagnostic accuracy of the current clinical standards;
- Provide the basis for an evidence-based prognosis with respect to the natural evolution of the disease, to pharmacological treatments, and/or to preventive interventional treatments aimed to selectively strengthen particularly weak regions of the skeleton.
For patients at high risk of fracture, and for which the pharmacological treatment appears insufficient, the VPHOP system will also assist the interventional radiologist in planning the augmentation procedure.
The various modelling technologies developed during the project will be validated not only in vitro, on animal models, or against retrospective clinical outcomes, but will also be assessed in term of clinical impact and safety on small cohorts of patients enrolled at four different clinical institutions, providing the factual basis for effective clinical and industrial exploitation.
So what?
VPHOP will realize "P2 medicine" for osteoporosis patients:
- Predictive: Multiscale models, representing skeletal mechanobiology, from the whole body down to the molecular constituents, to simulate skeletal loading in various conditions and predict bone failure
- Personalised: The multiscale model is personalised using information which is to the patient. The more information which is available the more personalised becomes the model.