The Use of Artificial Neural Networks for Optimizing Polydispersity Index (PDI) in Nanoprecipitation Process of Acetaminophen in Microfluidic Devices

Abstract

Abstract. Artificial neural networks (ANNs) were used in this study to determine factors that control the polydispersity index (PDI) in an acetaminophen nanosuspension which was prepared using nanoprecipitation in microfluidic devices. The PDI of prepared formulations was measured by dynamic light scattering. Afterwards, the ANNs were applied to model the data. Four independent variables, namely, surfactant concentration, solvent temperature, and flow rate of solvent and antisolvent were considered as input variables, and the PDI of acetaminophen nanosuspension was taken as the output variable. The response surfaces, generated as 3D graphs after modeling, were used to survey the interactions happening between the input variables and the output variable. Comparison of the response surfaces indicated that the antisolvent flow rate and the solvent temperature have reverse effect on the PDI, whereas solvent flow rate has direct relation with PDI. Also, the effect of the concentration of the surfactant on the PDI was found to be indirect and less influential. Overall, it was found that minimum PDI may be obtained at high values of antisolvent flow rate and solvent temperature, while the solvent flow rate should be kept to a minimum.

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“…While smaller PDI values indicate the homogeneity of the particle size in suspension. It was reported that PDI lower than 0.2 is considered to be ideal hence, the particle size distribution falls within a narrow range of size (40,(42)(43)(44)(45). The surface charge also plays an important role in the stability of the nanoparticles and the magnitude of zeta potential is indicative of the colloidal stability of the system (46).…”

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“…This caused the precipitation of less drug molecules per nucleation site and consequently decreased PS. 46 Moreover, the decrease in PS with an increased weight of stabilizer could be attributed to the decrease in surface tension, which facilitated the size reduction and stabilized the formed nanoparticles with inhibition of aggregation. 27 It is well known that the conformation of physically adsorbed triblock copolymers (Pluronics) depends on the hydrophobicity of the sorbent surface.…”

“…Hence, less drug molecules precipitated per nucleation site and a more uniform distribution for the PS was obtained resulting in lower PDI. 46 On the other hand, the increase in molecular weight of stabilizer resulted in an increase in PDI. This could be attributed to the less kinetically restricted adsorption process of the stabilizers onto the drug surfaces that was enabled when the molecular weight of the stabilizer decreased.…”

Size Control in the Nanoprecipitation Process of Stable Iodine (127I) Using Microchannel Reactor—Optimization by Artificial Neural Networks

“…Also, in this study, polydispersity index (PDI) of nanosuspension samples was determined in the range of 0.09-0.38, which is normal range obtained in microchannel devices and so their good potential to generate the monodispersed colloidal disperses, as reported in the former works (27,28). After considering all findings, a new nanosuspension formulation of stable iodine ( 127 I) was created to achieve minimum particle size.…”