Abstract

A stochastic variation in size and electrical parameters is common in nanoparticles. Synthesizing gold nanoparticles with a varying range of size and zeta potential, we show that there is clustering at certain regions of hydrodynamic diameter and zeta potentials that can be classified using k-clustering technique. A cluster boundary was observed around 50 nm, a size known for its optimal response to cells. However, neither size nor zeta potential alone determined the optimal cellular response (e.g., percentage cell survival) induced by such nanoparticles. A complex interplay prevails between size, zeta potential, nature of surface functionalization, and extent of adhesion of the cell to a solid matrix. However, it follows that the ratio of zeta potential to surface area, which scales as the electrical field (by Gaussian law), serves as an appropriate indicator for optimal cellular response. The phase plot spanned by fractional survival and effective electric field (charge density) indicates a positive correlation between mean cell survival and the magnitude of the electric field. The phase plot spanned by fractional survival and effective electric field (charge density) associated with the nanosurface shows a bifurcation behavior. Wide variation of cell survival response is observed at certain critical values of the surface charge density, whereas in other ranges the cellular response is well behaved and more predictable. Existence of phase points near the critical region corresponds to wide fluctuation of nanoparticle-induced response, for small changes in the nanosurface property. Smaller nanoparticles with low zeta potential (e.g., those conjugated with arginine) can have such an attribute (i.e., higher electrical field strength), and eventually they cause more cell death. The study may help in optimal design of nanodrugs. From the Clinical EditorIn this paper, important aspects of the background theory of optimizing gold nanoparticles for cancer therapy is discussed. Such nanoparticles have an enormous potential in future human applications once we fully understand the design parameters.

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