Search

The use of implanted biomaterials to augment the human body in the treatment of musculoskeletal conditions is one of the great successes of modern medicine. However implanted biomaterials create a unique ecological niche for bacteria to exploit; often resulting in recalcitrant infections that are refractory to conventional antimicrobial drug therapies. Compounding this complex clinical problem is the loss of treatment options through the global rise in antimicrobial resistance, which threaten to halt elective orthopaedic biomaterial-associated procedures. An evaluation of Staphylococcus aureus screening and eradication programmes revealed that the use of molecular diagnostics or pre-enrichment culture improved detection of MSSA colonisation. Adoption of these screening techniques was estimated to prevent ~1200 cases of prosthetic joint infections annually (potentially saving £160 million in treatment costs). A novel in vitro biofilm model was developed and shown to be reproducible and suitably responsive to clinically-relevant antimicrobial challenges. It was found that the bacteriostatic antimicrobials commonly used in PJI management antagonised gentamicin action when used against staphylococcal biofilms. A clinically-acceptable concentration of acetic acid (5%) and a bacterial endopeptidase, lysostaphin (100μg/mL), were shown to be effective in the eradication of S. aureus biofilms. The use of a machine learning segmentation algorithm to perform quantitative image analysis of biofilms was shown to be feasible. Low intensity pulsed ultrasound therapy potentiated the eradication effect of gentamicin on S. aureus biofilms, bringing it to within clinically achievable levels. Metabolic pathways critical in the reversal of gentamicin tolerance within biofilm-associated S. aureus were identified using a metabolomic approach. This thesis identified shortcomings in current preventative and therapeutic approaches to S. aureus biomaterial-associated infections. It also validated a novel in vitro biofilm model and demonstrated the feasibility of machine learning image analysis in biofilm studies. Finally, novel non-drug therapies were shown to have potential in the management of biomaterial-associated S. aureus infections.