Preparation and optimization of acetaminophen nanosuspension through nanoprecipitation using microfluidic devices: an artificial neural networks study

Aghajani, Masoud; Shahverdi, Ahmad Reza; Rezayat, Seyed Mahdi; Amani, Amir

doi:10.3109/10837450.2011.649854

Cited by 17 publications

(21 citation statements)

References 22 publications

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“…The effects of solvent/antisolvent flow rate as well as temperature were found to influence the PDI of the nanosuspension which could be an explanation for changes in physical stability. Nevertheless, a more detailed assessment appeared to be necessary to confirm the effect of the input parameters on the PDI (22). In the current work, we applied ANNs to study relations between parameters affecting the PDI on the nanoprecipitation process of the acetaminophen nanosuspension prepared with microfluidic devices.…”

Section: Introductionmentioning

confidence: 99%

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

2012

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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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Section: Introductionmentioning

confidence: 99%

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

2012

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“…In this method, the effects of changing two input variables on the output are studied through visualizing their effects by response surfaces produced by the software, while the remaining two input variables are fixed at three specific values (i.e., low, mid, and high ranges) (30). Following this method, 54 graphs were produced and briefed in Figs.…”

Section: Resultsmentioning

confidence: 99%

Investigating the Parameters Affecting the Stability of Superparamagnetic Iron Oxide-Loaded Nanoemulsion Using Artificial Neural Networks

et al. 2012

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Abstract. Nanoemulsions are increasingly being investigated for their fascinating capability of loading both hydrophobic and hydrophilic molecules while their stability is still an issue, being affected by various factors. In this study, to evaluate the dominant factors affecting the stability of nanoemulsions, artificial neural networks (ANNs) were implemented. Nanoemulsions of almond oil in water containing oleic acidcoated superparamagnetic iron oxide nanoparticles were prepared using a mixture of Tween 80 and Span 80 as surfactant system and ethanol as a co-surfactant. The ratio of transparency of the samples at 30 min and 7 days after preparation was taken as an indication of the stability of samples. Four independent variables, namely, concentration of nanoparticle, surfactant, oil, and alcohol were investigated to find their relations with the dependent variable (i.e., transparency ratio). Using ANNs modeling, it was concluded that the stability is affected by all variables, with all variables showing reverse effect on the stability beyond an optimum amount.

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“…Visual observation of sedimentation is one of the methods to estimate stability of prepared nanosuspension (6,9). As nanosuspension of stable iodine is a deflocculated nanosuspension and produced a densely packed sediment, sedimentation can be seen by the visual observation.…”

Section: Physical Stability Of Nanosuspension Of Stable Iodine ( 127 I)mentioning

confidence: 99%

“…The major advantages of microfluidic channel are high surface area to volume ratio and low Reynolds number which refers to laminar flow patterns (i.e., a liquid flows in parallel layers) (11,12). Therefore, a significant reduction in reagents usage, time of experiments, and costs is observed while reproducible experiments and performance are provided (9,13,14). Furthermore, monodispersed particles are commonly obtained in this method.…”

Section: Introductionmentioning

confidence: 99%

“…In the precipitation process, a drug is solved in a solvent to produce the supersaturated solution, then mixed with an antisolvent in order for precipitation of drug molecules and form drug nuclei (nucleation) (9). In microfluidic devices technology, the nucleation procedure is performed in microreactors with internal diameters usually smaller than 1 mm (10).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2015

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Abstract. In this study, nanosuspension of stable iodine ( 127 I) was prepared by nanoprecipitation process in microfluidic devices. Then, size of particles was optimized using artificial neural networks (ANNs) modeling. The size of prepared particles was evaluated by dynamic light scattering. The response surfaces obtained from ANNs model illustrated the determining effect of input variables (solvent and antisolvent flow rate, surfactant concentration, and solvent temperature) on the output variable (nanoparticle size). Comparing the 3D graphs revealed that solvent and antisolvent flow rate had reverse relation with size of nanoparticles. Also, those graphs indicated that the solvent temperature at low values had an indirect relation with size of stable iodine ( 127 I) nanoparticles, while at the high values, a direct relation was observed. In addition, it was found that the effect of surfactant concentration on particle size in the nanosuspension of stable iodine ( 127 I) was depended on the solvent temperature.

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Preparation and optimization of acetaminophen nanosuspension through nanoprecipitation using microfluidic devices: an artificial neural networks study

Cited by 17 publications

References 22 publications

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

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

Investigating the Parameters Affecting the Stability of Superparamagnetic Iron Oxide-Loaded Nanoemulsion Using Artificial Neural Networks

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

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