The use of nanoparticles (NPs) in the treatment of autoimmune type 1 diabetes has shown promising results. It was found that they can effectively expand a pool of autoregulatory T cells that can block disease progression to a therapeutic level in a dose-dependent manner. The structure (or valency) of these NPs is also a factor in determining their therapeutic efficacy. In this talk, we will present a mathematical model to explore the effects of compound design parameters (NP dose and valency) on disease progression. We will show, using bifurcation analysis, that the model exhibits a “resonance”-like behavior for a given range of NP-dose and valency and present a methodology to quantify the average valency-dependent minimal/optimal dose needed for effective therapy. We will also demonstrate how the model can generalize to other autoimmune diseases and serve as a computational tool to understand and optimize NP-based therapies.