Predictive shape optimization and pH-responsive drug release in Au-doped Fe₃O₄ nanocarriers: a hybrid computational–experimental approach for targeted theranostics

The modeling and simulation of the next-generation of therapeutic and diagnostic systems (i.e. theranostics) will involve hybrid nanosystems: a combination of two or more nanoparticles. The release rate of this may be studied in silico and atomistically to guide experimentalist in the design of such drug-delivery systems. Here, classical atomistic molecular dynamics was used to investigate the expected shape, long term stability and release rate of 5-fluororacil molecules loaded onto Au doped Fe₃O₄nanoparticles. The binding energy (BE) of three potential shapes were used to identify the shape that is the most stable and therefore the most likely to be synthesized. Hexagonal nanoparticles (7 ± 2 nm) were identified in the simulations as the most stable with BE per molecule ∼ -1.1 eV (-25 kcal mol^-1). Furthermore, to support our theoretical observations, Au-Fe₃O₄nanoparticles were synthesized and characterized with the aid of TEM, XRD, FTIR and VSM. TEM confirmed the size distribution 1-12 nm and the release at pH 5.7 reached 84% vs 41% at pH 7.4 within 48 h.