Back pain affects eight out of ten adults during their lifetime. It a huge economic burden on society, estimated to cost as much as 1-2% of gross national product in several European countries. Treatments for back pain have lower levels of success and are not as technologically mature as those for other musculoskeletal disorders such as hip and knee replacement. This application proposes to tackle one of the major barriers to the development of better surgical treatments for back pain.At present, new spinal devices are commonly assessed in isolation in the laboratory under standardised conditions that do not represent the variation across the patient population. Consequently many interventions have failed during clinical trials or have proved to have poor long term success rates.Using a combination of computational and experimental models, a new testing methodology will be developed that will enable the variation between patients to be simulated for the first time. This will enable spinal implants and therapies to be more robustly evaluated across a virtual patient population prior to clinical trial. The tools developed will be used in collaboration with clinicians and basic scientists to develop and, crucially, optimise new treatments that reduce back pain whilst preserving the unique functions of the spine.If successful, this approach could be translated to evaluate and optimise emerging minimally invasive treatments in other joints such as the hip and knee. Research in the spine could then, for the first time, lead rather than follow that undertaken in other branches of orthopaedics.