* Murine Model of Progressive Orthopedic Wear Particle-Induced Chronic Inflammation and Osteolysis.

Periprosthetic osteolysis and subsequent aseptic loosening of total joint replacements are driven by byproducts of wear released from the implant. Wear particles cause macrophage-mediated inflammation that culminates with periprosthetic bone loss. Most current animal models of particle-induced osteolysis are based on the acute inflammatory reaction induced by wear debris, which is distinct from the slowly progressive clinical scenario. To address this limitation, we previously developed a murine model of periprosthetic osteolysis that is based on slow continuous delivery of wear particles into the murine distal femur over a period of 4 weeks. The particle delivery was accomplished by using subcutaneously implanted osmotic pumps and tubing, and a hollow titanium rod press-fit into the distal femur. In this study, we report a modification of our prior model in which particle delivery is extended to 8 weeks to better mimic the progressive development of periprosthetic osteolysis and allow the assessment of interventions in a setting where the chronic particle-induced osteolysis is already present at the initiation of the treatment. Compared to 4-week samples, extending the particle delivery to 8 weeks significantly exacerbated the local bone loss observed with μCT and the amount of both peri-implant F4/80+ macrophages and tartrate-resistant acid phosphatase-positive osteoclasts detected with immunohistochemical and histochemical staining. Furthermore, systemic recruitment of reporter macrophages to peri-implant tissues observed with bioluminescence imaging continued even at the later stages of particle-induced inflammation. This modified model system could provide new insights into the mechanisms of chronic inflammatory bone loss and be particularly useful in assessing the efficacy of treatments in a setting that resembles the clinical scenario of developing periprosthetic osteolysis more closely than currently existing model systems.