Mind the gap: Sex-specific computational modeling reveals distinct mechanisms in bone fracture healing

Abstract This study presents the first sex-specific computational model of bone fracture healing, addressing the limited and often contradictory understanding of sex differences in this process. An existing inflammation-repair model was adapted using literature-derived sex-specific parameter values. Independent global sensitivity analysis identified several of these parameters among the main drivers of model dynamics. Due to the critical role of inflammation in fracture repair, a non-dimensional timescale analysis was used to characterize the temporal organization of early fracture healing. Two characteristic timescales common to females and males were identified, capturing fast cytokine regulation and slower cellular processes. Although this analysis did not reveal sex-dependent timescales, it provided an interpretative framework within which quantitative sex differences emerged in full-model predictions. These differences included faster macrophage activation and enhanced phagocytosis in females, leading to quicker debris clearance and stronger, more sustained inflammatory responses with elevated anti-inflammatory cytokine production. In contrast, males exhibited higher pro-inflammatory cytokine levels, a shorter inflammatory phase, and higher peaks in skeletal stem/progenitor cell and fibrocartilage densities. Despite these mechanistic differences, the model predicted comparable healing outcomes in females and males. Validation against independent {\it in vivo} data showed good agreement, including the predicted sex-specific differences in the fibrocartilage compartment and similar osteoblast numbers and ossification patterns between sexes. By integrating sex-specific data from diverse experimental sources, often focused on isolated subprocesses, into a unified computational framework, this work reproduces emergent, sex-specific healing dynamics consistent with experimental observations, highlighting the importance of incorporating sex-specific biology into computational models and supporting their use to advance personalized treatment strategies.