Dynamic release kinetics and biological impact of leachables from 3D-printed oral devices: An integrated in vitro and computational exposure model

Abstract

Research has highlighted the release of monomers and leachables from additively manufactured (AM) oral devices, raising concerns about their potential biological impact. The oral cavity's dynamic epithelial system necessitates exposure models that accurately reflect real-world conditions. Traditional static models often overestimate or underestimate patient exposure, failing to predict in vivo risks effectively. To address this gap, we developed an advanced dynamic oral tissue exposure model that simulates the release kinetics of leachables, saliva flow, and gingival tissue perfusion. This dynamic approach, integrated with an in vitro human gingival keratinocyte (HGK) model, was applied for the first time in this study. We quantified urethane dimethacrylate (UDMA) release from AM biomaterials through extraction experiments, generating data for computational modeling. The model revealed that dynamic in vivo monomer exposure peaks at specific time points before declining, a pattern not captured by static calculations. In vitro analysis showed that UDMA exposure inhibited metabolic activity and reduced Ki-67 expression in HGK cultures at micromolar concentrations. While inhibitory in vitro concentrations exceeded predicted in vivo estimates, low-dose effects on Ki-67 expression were still observed. These findings suggest that although calculated UDMA exposure remains sub-cytotoxic, it may still induce sensitizing effects. Overall, the dynamic exposure model introduced in this study represents a significant advancement in risk assessment, offering more accurate predictions of the biological effects of leachables and contributing to the safety evaluation of AM biomaterials.

Statement of significance

Additively manufactured (AM) oral devices are a significant source of monomer release into the oral cavity, raising concerns about tissue exposure. Traditional static models provide limited or inaccurate risk estimates due to the cavity’s dynamic nature. In this study, we developed a dynamic oral tissue exposure model that estimates in vivo-relevant monomer and leachable concentrations in saliva and oral mucosa while integrating an in vitro gingival keratinocyte model to assess biological effects. The model provides key insights into predicted in vivo exposure to monomers and leachables, improving in vitro evaluations of biological effects. Overall, it serves as a valuable risk assessment tool for the research community by enhancing predictions of patient exposure to potential monomers and leachables, thereby supporting AM biomaterial safety.