Numerical and Experimental Characterisation of Articular Cartilage – A Study on Biomechanics and Biotribology, Osteoarthritis and Tissue Engineering Solutions
Creators: name: Accardi, Mario Alberto
Publisher: name: Imperial College London
Year: 2012

description: Articular Cartilage (AC) is a soft tissue covering the articulating surface of human and animal joints. The tissue has remarkable and highly complex mechanical and wear properties allowing the joint to undergo complex kinematics and function correctly for several decades. However, trauma and degenerative joint diseases such as osteoarthritis (OA) can cause damage and excessive wear of the tissue and due to its limited regenerative capabilities, can severely compromise joint movement and impair the quality of life. OA is the most common type of degenerative joint disease and the primary cause of joint replacement surgery leading to high associated healthcare costs. Although the exact cause of this pathology remains unknown, it is thought to be mechanically induced via excessive and abnormal stresses and strains in AC which cause altered biochemical properties and a gradual decrease in the mechanical quality of the tissue. There is currently no available cure for OA and the disease is currently being diagnosed only via imaging techniques which are based upon morphological changes of the tissue, when the pathology is already in its advanced stages and has caused irreversible changes to the AC. In this respect, one of the greatest challenges to now remains the early diagnosis of OA, potentially by assessing biochemical and mechanical changes, allowing early treatments and prevention of disability thus improving the patient’s life. Hence, there is a need to apply fundamental engineering principles to the medical world in order to shed light on the pathogenesis and progression of OA. Furthermore, the need for artificial substitutes of AC has called for a deep understanding of the mechanical behaviour of the tissue in order to design and mimic the response of the real tissue in the most accurate manner. In this research a combination of numerical (finite element) and experimental techniques involving mechanical and tribological tests were used to fully characterise the mechanical behaviour of the tissue. Selective degradation of the AC constituents was then induced to simulate OA (OA-like AC) and the effect of different stages of degradation on the mechanical and tribological response as well as the wear properties of the tissue was investigated. The mechanical properties of osteoarthritic AC were then evaluated and compared to the OA-like AC in order to correlate similarities in the variations to the structure and the mechanical response as a result of degradation. Quantifying the mechanical response of the tissue at different stages of OA and different levels of degradation was done to ensure both a thorough understanding of the effect of the pathology’s progression on AC as well as to provide a potential map of mechanical quality and degradation, contributing to the potential future diagnosis of OA via mechanical parameters rather than morphological alone. Having investigated structural and mechanical variation in early OA, a promising solution to treat localised early OA and AC defects was also investigated as part of this research. In particular, novel micro fibrous tissue engineered scaffolds have been mechanically and tribologically assessed and compared to AC demonstrating the strong potential of matrix-assisted autologous chondrocyte implantation (MACI). Finally, the numerical models developed to characterise the AC using numerical – experimental methods, namely advanced biphasic models incorporating fine material descriptions such as intrinsic viscoelasticity as well as transverse isotropy, were applied to a patient specific 3D menisectomised tibio-femoral joint contact model in order to demonstrate the implications that the implementation of different AC models have for the prediction of the joint response to repeated walking cycles. The results obtained from the models were then used to predict the most likely location for the origin of mechanical damage and OA.