Abstract
Nanoparticle (NP) contrast agents targeted to cancer biomarkers are increasingly being engineered for the early detection of cancer, guidance of therapy, and monitoring of response. There have been recent efforts to topically apply biomarker-targeted NPs on tissue surfaces to image the expression of cell-surface receptors over large surface areas as a means of evaluating tumor margins to guide wide local excision surgeries. However, diffusion and nonspecific binding of the NPs present challenges for relating NP retention on the tissue surface with the expression of cancer cell receptors. Paired-agent methods that employ a secondary 'control' NP to account for these nonspecific effects can improve cancer detection. Yet these paired-agent methods introduce multidimensional complexity (with tissue staining, rinsing, imaging, and data analysis protocols all being subject to alteration), and could be greatly simplified with accurate, predictive in silico models of NP binding and diffusion. Here, we outline and validate such a model to predict the diffusion, as well as specific and nonspecific binding, of targeted and control NPs topically applied on tissue surfaces. In order to inform the model, in vitro experiments were performed to determine relevant NP diffusion and binding rate constants in tissues. The predictive capacity of the model was validated by comparing simulated distributions of various sizes of NPs in comparison with experimental results. The regression of predicted and experimentally measured concentration-depth profiles yielded <15% error (compared to ~70% error obtained using a previous model of NP diffusion and binding).