In this work, we used the artificial intelligence tool known as neurofuzzy logic (NFL) for fabricating uniform nanoparticles of polycaprolactone by the nanoprecipitation method with a focus on stabilizer selection. The adaptability of NFL assisted the decision-making on different manufacturing and formulation conditions. The nanoprecipitation method can be summarized as mixing a poorly water-soluble polymer solution with water and its consequent precipitation. Although nanoprecipitation seems simple, the process is highly variable to even slight modifications, leading to polydispersity and nanoparticle aggregation. Here, the NFL model established relationships between mixing conditions, different stabilizers and solvents, among other parameters. Seven parameters measured by dynamic light scattering and laser doppler electrophoresis were modelized with high predictability using NFL tool, as a function of the raw materials and operation conditions. The model allowed the principal component analysis to be carried out, showing that the selection of a stabilizer is the most critical parameter for avoiding nanoparticle aggregation. Then, inputs related to fluid dynamics were relevant to tune the characteristics of the stabilized nanoparticles even further. NFL model showed great potential to support pharmaceutical research by finding subtle relationships between several variables, even from incomplete or fragmented data, which is common in pharmaceutical development.
Keywords: Aggregation; Artificial neural networks; Fuzzy logic; Nanoprecipitation; Neurofuzzy logic; Polymeric nanoparticles; Stabilizers.
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