Longitudinal radiological monitoring of biomedical devices is increasingly important, driven by risk of device failure following implantation. Polymeric devices are poorly visualized with clinical imaging, hampering efforts to use diagnostic imaging to predict failure and enable intervention. Introducing nanoparticle contrast agents into polymers is a potential method for creating radiopaque materials that can be monitored via computed tomography. However, properties of composites may be altered with nanoparticle addition, jeopardizing device functionality. This, we investigated material and biomechanical response of model nanoparticle-doped biomedical devices (phantoms), created from 0-40wt% TaO x nanoparticles in polycaprolactone, poly(lactide-co-glycolide) 85:15 and 50:50, representing non-, slow and fast degrading systems, respectively. Phantoms degraded over 20 weeks in vitro, in simulated physiological environments: healthy tissue (pH 7.4), inflammation (pH 6.5), and lysosomal conditions (pH 5.5), while radiopacity, structural stability, mechanical strength and mass loss were monitored. The polymer matrix determined overall degradation kinetics, which increased with lower pH and higher TaO x content. Importantly, all radiopaque phantoms could be monitored for a full 20-weeks. Phantoms implanted in vivo and serially imaged, demonstrated similar results. An optimal range of 5-20wt% TaO x nanoparticles balanced radiopacity requirements with implant properties, facilitating next-generation biomedical devices.

Longitudinal radiological monitoring of biomedical devices is increasingly important, driven by the risk of device failure following implantation. Polymeric devices are poorly visualized with clinical imaging, hampering efforts to use diagnostic imaging to predict failure and enable intervention. Introducing nanoparticle contrast agents into polymers is a potential method for creating radiopaque materials that can be monitored via computed tomography. However, the properties of composites may be altered with nanoparticle addition, jeopardizing device functionality. Thus, the material and biomechanical responses of model nanoparticle-doped biomedical devices (phantoms), created from 0-40 wt% tantalum oxide (TaOx ) nanoparticles in polycaprolactone and poly(lactide-co-glycolide) 85:15 and 50:50, representing non, slow, and fast degrading systems, respectively, are investigated. Phantoms degrade over 20 weeks in vitro in simulated physiological environments: healthy tissue (pH 7.4), inflammation (pH 6.5), and lysosomal conditions (pH 5.5), while radiopacity, structural stability, mechanical strength, and mass loss are monitored. The polymer matrix determines overall degradation kinetics, which increases with lower pH and higher TaOx content. Importantly, all radiopaque phantoms could be monitored for a full 20 weeks. Phantoms implanted in vivo and serially imaged demonstrate similar results. An optimal range of 5-20 wt% TaOx nanoparticles balances radiopacity requirements with implant properties, facilitating next-generation biomedical devices.

Magnetic particle imaging (MPI) is an exciting new biomedical imaging technology that uses superparamagnetic nanoparticles as an imaging tracer. MPI is touted as a quantitative imaging modality but MPI signal properties have never been characterized for nanoparticles undergoing biodegradation. Here we characterize the nature of the MPI signal properties as a function of degradation of various magnetic particle formulations. We show that MPI signal properties can increase or decrease as a function of nanoparticle formulation and chemical environment and that long-term in vitro experiments only roughly approximate long-term in vivo MPI signal properties. Data are supported by electron microscopy of nanoparticle degradation. Knowledge of MPI signal property changes during nanoparticle degradation will be critical in design and interpretation of all MPI experiments. Further, we demonstrate for the first time, an environmentally sensitive MPI contrast mechanism opening the door to smart contrast paradigms in MPI.