An in-house computer code based on artificial intelligence has been developed and applied in modeling and closed-loop optimization of release behavior of Poly(lactic-co-glycolic acid) (PLGA) biodegradable particles. A series of micro- and nanoparticles were prepared via water-in-oil-in-water double emulsion to be loaded with albumin–fluorescein isothiocyanate conjugate as a typical drug. The interrelationship between input variables (molecular weight of polymer and stabilizer, polymer concentration, and sonication rate) and outputs (PLGA particle size and percentage of initial burst) was uncovered with the aid of artificial neural network modeling. The regression analysis confirmed acceptable correlation coefficients for the aforementioned responses, where the PLGA molecular weight played the most important role among the studied variables. Input variables needed to minimize PLGA size and PLGA initial burst were then obtained via multiobjective optimization performed by a genetic algorithm. PLGA nanoparticles were checked for particle size and particle size distribution using scanning electron micrographs.

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