AAPS PharmSciTech

, Volume 16, Issue 5, pp 1059–1068 | Cite as

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

  • Mohamad Hosein Aghajani
  • Ali Mahmoud Pashazadeh
  • Seyed Hossein Mostafavi
  • Shayan Abbasi
  • Mohammad-Javad Hajibagheri-Fard
  • Majid Assadi
  • Mahdi Aghajani
Research Article

Abstract

In this study, nanosuspension of stable iodine (127I) 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 (127I) 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 (127I) was depended on the solvent temperature.

Graphical Abstract

Nanoprecipitation process of stable iodine (127I) and optimization of particle size using ANNs modeling.

KEY WORDS

ANNs microfluidic nanoprecipitation particle size stable iodine 

INTRODUCTION

Nanosuspension formulation of stable iodine (127I) can have a remarkable advantageous over the usual form of iodine. Stable iodine (127I) is one of the main preventive measures used for those people near a nuclear event to lessen absorption of released radioiodine in their thyroid glands. Since, in such cases, there is a competition between stable iodine (127I) and radioiodine to be absorbed in the thyroid; therefore, nanosuspension form of the stable iodine (127I) may increase uptake rate of stable iodine (127I) in comparison to radio iodine, reducing uptake of radio iodine. Various nanosizing techniques have been developed to increase dissolution velocity and solubility of drug through enhancing surface area to volume ratio of the drug particles, e.g., nanosuspension engineering. During the formulation of nanosuspensions, drug particles under 1 μm are distributed in an outer liquid phase and are fixed by stabilizers (1,2). The nanosuspension engineering is a simple procedure which is generically applicable for most of drug molecules (3). Also, in comparison of the conventional formulation systems, it is shown that formulation of nanosuspension is associated with advantages such as increased absorption rate, enhanced dissolution, improved bioavailability (4,5), increased adhesion, high drug loading (6,7), and scale-up capability (8).

Typically, during nanosuspensions process, drug nanocrystals are made in a liquid medium using top-down (i.e., milling by media and high pressure homogenizer) or bottom-up (i.e., precipitation) methods. 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). 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. In the microfluidic nanoprecipitation, at first, a supersaturated solution of drug is prepared by dissolving the drug in a solvent. The solution obtained is then injected into an antisolvent, as a poor solvent, to form and grow of nucleation sites on the fluids interface (see Fig. 1). Using surfactant to limit drug crystal growth, it is possible to prevent creation of microparticles.
Fig. 1

Schematic of nanoprecipitation in microfluidic channels (with the 1 mm internal diameter)

Effects of changing the various parameters of the microfluidic precipitation process, including flow rate of drug solution and antisolvent, drug saturation level, channels internal diameter, microreactor inlet angle, drug concentration, precipitation temperature, and surfactant concentration, on the size of nanosized drug particles have been evaluated widely in previous studies (15, 16, 17, 18, 19, 20). It is observed that increasing in the flow rate of antisolvent and decreasing in the flow rate of drug solution, sharpen inlet angles and lessen saturation levels resulted in smaller particle sizes (19). In another study, it was shown that design of microreactor and processing conditions may have an influence on the hydrocortisone particle size during nanoprecipitation using microfluidic reactors (18). Also, microfluidic nanoprecipitation of the other pharmaceutical ingredients such as danazol (20), norfloxacin (15), cefuroxime axetil (16), and rifampicin (17) have shown the creation of drug particles with controlled sizes, improved dissolution rate, continuous synthesis, and amorphous profile.

In this study, we used artificial neural networks (ANNs) to find a predictive and quality model describing the effects of experiment parameters (independent variables) on the particle size (dependent variable) of the stable iodine (127I) nanosuspension formulation obtained by nanoprecipitation in microfluidic reactors. ANNs are artificial intelligence tools which arbitrary find the nonlinear and complex relation between the experimental data (21). It can be used to predict the relations between independent variables (i.e., inputs) and corresponding dependent variable(s) (i.e., output(s)) (19), especially in case of complex experimental data in nanotechnology studies (22).

MATERIALS AND METHODS

Materials

For the present study, potassium iodide (KI) (Merck chemicals, Germany), Tween 80 (polysorbate 80) (Sigma-Aldrich, USA), and absolute ethyl alcohol (ethanol) 99/6% (V/V) (Jahan Alkol Teb Arak co. (JATA), Iran) were used as preliminary materials to set experiment up.

Stable Iodine (127I) Nanoprecipitation in Microchannel Reactor

In order to prepare nanosuspension of stable iodine (127I) using microfluidic devices, supersaturated solutions of stable iodine (127I) in distilled water (as solvent) at given temperatures (24–80°C) were pumped to the microreactor. Certain flow rates of solvent (0.5–2.4 ml/min) and antisolvent (0.5–2.5 ml/min) were controlled using pumps (Fanavaran Nano-Meghyas, Iran). Also, ethanol as antisolvent system was mixed with predetermined concentrations of surfactant (Tween 80) at the controlled lab temperature of 24 ± 2°C. Temperature of solvent solution was predetermined and controlled by heater throughout the study. Microfluidic instrument was fabricated from the Aluminum material, with internal diameter of 1 mm and inlet angle of 50°. In order to prevent particles growth in prepared nanosuspension, the particle size was measured freshly using dynamic light scattering. Experimentally, 45 samples at random values from four independent variables (i.e., input factors) including solvent flow rate (ml/min), solvent temperature (°C), antisolvent flow rate (ml/min), and surfactant concentration (mg/5 ml) were prepared, and size of samples was considered as dependent variable (i.e., output). Obtained data from experimental study were used to achieve a model by ANNs modeling to understand effects of four input factors on the particle size of prepared nanosuspension through nanoprecipitation in microchannel reactor.

ANNs Modeling

Modeling of the relations between four input factors and one output parameter was studied using commercially available ANNs software (INForm v4.02, Intelligensys, UK). Then, the response surfaces, illustrated as 3D-graphs, were applied to evaluate the effect of two input parameters on the output parameter while two other input parameters were fixed at given values (i.e., low, medium, and high values).

The results of experimental samples were used to train a model by ANNs. Number of 32 data sets was defined as training data for teaching the network of relations between input variables and output variable. Also, three data set (10% of the training data) were taken as the test data in order to prevent overtraining during the training process. Details of the training parameters (listed in Table I) during the modeling by ANNs have been described previously (23). Following the training of network, ten more data set (as validation data set or unseen data) was also used to evaluate the predictive capability of the model provided by ANNs software. The predictive capability of the obtained model was finally validated using correlation coefficient (R2) close to one (24) calculated for unseen data set (see Table II).
Table I

The Training Parameters Applied in INForm v4.02

Network structure

No. of hidden layers

1

No. of nodes in hidden layer

4

Backpropagation type

 

Incremental

Backpropagation parameters

Momentum factor

0.8

Learning rate

0.7

Targets

Maximum iterations

1000

MS error

0.0001

Random seed

10,000

Smart stop

Minimum iterations

20

Test error weighting

0.1

Iteration overshoot

200

Auto weight

On

Smart stop enabled

On

Transfer function

Output

Symmetric sigmoid

Hidden layer

Symmetric sigmoid

Table II

The Unseen Data Sets Used by ANNs Software

Solvent flow rate (ml/min)

Solvent temperature (°C)

Antisolvent flow rate (ml/min)

Surfactant concentration (mg/5 ml)

Observed particle size (nm)

Predicated particle size (nm)

1.0

80

1.0

1296

185.4

199.3

1.8

60

0.8

1296

104.0

117.1

2.0

60

1.0

864

109.0

116.7

0.8

60

2.5

1296

105.0

104.9

2.0

40

0.5

1080

169.0

175.1

1.0

70

2.5

1296

102.2

124.7

2.0

60

2.1

1252.8

52.1

60.4

1.8

70

1.8

972

116.0

129.4

1.0

50

1.0

1252.8

125.8

128.4

Particle Size Measurement

Relying on the impact of input parameters on the output parameter predicted by obtained model, an optimum formulation (i.e., nanosuspension of stable iodine (127I) with minimum size) was prepared and evaluated to determine particle size and morphology.

DLS Analysis

Particle size (statistical mean value of particle diameter) in optimum formulation was measured by dynamic light scattering (Nano ZS, Malvern instruments, UK) with PCS software (version 1.27). All samples were analyzed at lab temperature (controlled at 22°C), freshly diluted by adding 2 ml deionized water to 1 ml of sample.

TEM Analysis

Evaluating of the nanoparticle size and its morphology of stable iodine (127I) in the optimized nanosuspension formulation was implemented using transmission electronic microscope (Zeiss EM 10C, Germany).

The optimized sample was dispersed in deionized water by ultrasonic for 3 min. Two drops of the nanosuspension sample were put on the copper grid coated by carbon and placed in lab temperature for 15 min (drying sample). Subsequently, the grid was put on a holder to be monitored using TEM at the voltage of 80 kV.

Determination of Stability

Optimization of the stability of the nanosuspension formulation of stable iodine (127I) demands another study that was out of the scope of our study. Here, physical stability of obtained nanosuspension formulation of stable iodine (127I) with minimum size was investigated, in which time of sedimentation was considered as indicator of physical stability of nanosuspension. Obtained formulation (i.e., sample with minimum particle size) was observed daily, in controlled lab temperature of 24 ± 2°C, in order to determine the time of phase separation of nanoparticles in nanosuspension.

Determination of Entrapment Efficiency of Stable Iodine (127I)

The percentage of entrapment efficiency was measured using spectrophotometric detection at 226 nm. At first, nanosuspension sample of stable iodine obtained (i.e., sample with minimum particle size) was centrifuged at speed of 10,000 rpm for 20 min, at temperature of 20°C. Then, supernatant was separated and content of the free dissolved stable iodine (127I) was detected by UV–vis spectrophotometer at 226 nm. The amount of nanosized stable iodine (127I) was determined by subtraction of amount of initial stable iodine (127I) minus the free stable iodine (127I) (25). This amount can be entrapment efficiency (EE %) according to the following equation:
$$ \mathrm{Entrapment}\ \mathrm{efficiency}\ \left(\mathrm{E}\mathrm{E}\%\right) = \frac{\ \mathrm{initial}\ \mathrm{drug}\ \mathrm{amount} - \mathrm{free}\ \mathrm{drug}\ \mathrm{amount}}{\ \mathrm{initial}\ \mathrm{drug}\ \mathrm{amount}} \times 100 $$

RESULTS

After the model was generated by ANNs, R2 values of 0.92, 0.81, and 0.80 were obtained for training, test, and unseen data, respectively. Resulted values show a satisfactory trained model. Subsequently, the generated model was used to evaluate the effect of above-mentioned input parameters on the particle size in nanosuspension formulation of stable iodine (127I). The first choice to determine the relations of input variables and output parameter could be the method of sensitivity analyses. In the current study, an alternative systematic method reported previously (26) was used to determine relations between the input variables and output. As described previously (19), in this method, the effect of changes in two input variables on output parameter (i.e., the particle size) is shown by response surfaces, whereas the remaining input variables are fixed on the predetermined low, mid-range, and high amounts.

Considering the procedure explained above, at first, in order to study change of particle size of stable iodine (127I) nanosuspension against solvent and antisolvent flow rate, the other two input variables were fixed at the low, mid-range, and high values, namely, 936.0, 1024.0, and 1290.0 mg/5 ml for Tween 80 concentration and 42, 60, and 78°C for solvent temperature. Resulted three-dimensional graphs are displayed in Fig. 2. According to these graphs, the effect of antisolvent flow rate on nanoparticles size, for every fixed data set, clearly shows that an increase in antisolvent flow rate has led to a considerable decrease in the particle size of stable iodine (127I) nanosuspension. Also, it is obviously demonstrated that particles size in the formulation of generated nanosuspension has decreased when the solvent flow rate in nanoprecipitation process has increased.
Fig. 2

3D plots of the nanosuspensions size predicted by the ANNs model fixed at low, mid-range, and high values of the Tween 80 concentration and solvent temperature

Figure 3 shows the effect of Tween 80 concentration and solvent flow rate on the particle size of stable iodine (127I) nanosuspension in creation of 3D graphs, when other two variables, antisolvent flow rate and solvent temperature, are fixed at the values of low, mid-range, and high (i.e., 0.9, 1.7, and 2.3 ml/min for antisolvent flow rate, and 42, 60, and 78°C for solvent temperature).
Fig. 3

3D plots of nanosuspensions size predicted by the ANNs model fixed at low, mid-range, and high values of the antisolvent flow rate and solvent temperature

It is clear that increasing Tween 80 concentration in the low and mid-range data set of fixed solvent temperature may lead to reduction in the size of stable iodine (127I) nanoparticles in nanosuspension. Nevertheless, for the higher values of fixed solvent temperature, an increase in the nanoparticles size after rising Tween 80 concentration is obvious. Also, it is shown that for each data set of fixed antisolvent flow rate and solvent temperature, any increase in solvent flow rate may lead to a considerable reduction in the particle size of obtained nanosuspension, as mentioned above.

Obtained 3D graphs in Fig. 4 show the effect of changes in solvent temperature and Tween 80 concentration on the particles size, when solvent flow rate and antisolvent flow rate are fixed in the amount of low, mid-range, and high (0.9, 1.7, and 2.3 ml/min for both data set). Based on Fig. 4, at low solvent temperature of ~45°C, the particle size in nanosuspension is minimum, showing an indirect relation with lower temperatures and direct relation with higher solvent temperatures. Information on the rows and columns in Fig. 4 show that for both data sets of fixed antisolvent and solvent flow rate, increasing the concentration of Tween 80 results in small decrease in the nanoparticles size of stable iodine (127I).
Fig. 4

3D plots of nanosuspensions size predicted by the ANNs model fixed at low, mid-range, and high values of the solvent flow rate and antisolvent flow rate

With attention to the details in Figs. 5, 6, and 7 that show the changes of nanoparticles size against Tween 80 concentration and flow rate of antisolvent, solvent temperature and flow rate of antisolvent, and flow rate of solvent and solvent temperature, respectively, the following findings which is indicated above can be obtained:
Fig. 5

3D plots of nanosuspensions size predicted by the ANNs model fixed at low, mid-range, and high values of the solvent flow rate and solvent temperature

Fig. 6

3D plots of nanosuspensions size predicted by the ANNs model fixed at low, mid-range, and high values of the solvent flow rate and Tween 80 concentration

Fig. 7

3D plots of nanosuspensions size predicted by the ANNs model fixed at low, mid-range, and high values of the antisolvent flow rate and Tween 80 concentration

  1. I.

    Increasing flow rate of solvent and antisolvent during the nanoprecipitation process can cause to a considerable reduction in the particle size in nanosuspension.

     
  2. II.

    The relation between size of iodine nanoparticles and solvent temperature is direct and indirect for high and low temperatures, respectively.

     
  3. III.

    In the low (or mid-range) and high ranges of the solvent temperature, respectively, increasing the Tween 80 concentration results in a decrease and increase poorly at the size of nanoparticles obtained during nanoprecipitation.

     

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 (127I) was created to achieve minimum particle size. The nanosuspension sample was prepared in solvent flow rate of 2.4 ml/min, antisolvent flow rate of 2.5 ml/min, solvent temperature of 45°C, and Tween 80 concentration of 1296 mg/5 ml. Particle size of the generated sample was determined ~20 nm which was evaluated by DLS and confirmed using TEM (see Fig. 8).
Fig. 8

Picture of stable iodine nanoparticle (Transmission Electron Microscope (TEM))

Physical Stability of Nanosuspension of Stable Iodine (127I)

Sedimentation time of obtained nanosuspension of stable iodine (127I) with minimum particle size was determined by visual observation. 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. Finally, physical stability of obtained sample was determined as 68 days.

Entrapment Efficiency of Stable Iodine (127I)

The entrapment efficiency of nanosuspension formulation of stable iodine (127I) with minimum particle size was determined to be ~82%. This finding show that almost 82% of supersaturated solution of potassium iodide (KI) powder was converted into stable iodine (127I) nanoparticles produced via nanoprecipitation using microfluidic devices.

DISCUSSION

Up to now, various approaches have been introduced to produce drug nanoparticles. One of the simple and cost-effective methods to prepare nanoparticles is the use of microfluidics technology (channels with a micrometer scale) (29). It is indicated recently that fluids manipulation and fine control of fluid interfaces in these channels is one of the main benefits of this approach. Controlling the flow and mixing conditions in the microchannels may yield a reproducible monodispersed nanosuspension and provide opportunity to better control of particle size (27,29).

According to the modeling results of our work, particle size of obtained nanosuspension decreased by increasing flow rate of antisolvent, which is in agreement with work of H.S.M. Ali et al. (19). This finding is because of the decrease of drug solubility, by adding antisolvent to the solvent (increased mixing), which enhances the level of supersaturation and consequently results in a higher nucleation rate than growth rate. Subsequently, this process leads to generation of smaller nanosized particles (20). Also, any increase in the flow rate of antisolvent can result in diminishing the diffusion process of drug molecules to antisolvent stream per unit volume of antisolvent. This phenomena can cause to fewer solute concentration around growing drug nanocrystal, leading to formation of smaller nanoparticles (19,29).

In addition, by increasing flow rate of solvent in stable iodine (127I) nanoprecipitation, the particle size of prepared nanosuspension decreased, which this finding is similar to that of Y.F. Su et al. regarding preparation of continuous nanoparticle using microfluidic-based emulsion (30). In fact, this finding may be due to enhancement in the supersaturation level against increased mixing, obtained with increase at flow rate of solvent (18,30). Consequently, increasing nucleation than growth and reduction of particle size can be seen, as expressed above.

From the results mentioned above, the particles size increased in nanosuspension against higher and lower values of solvent temperature, while minimum particle size was observed at low range of solvent temperature near to a certain value (~45°C). It can be argued that the high temperature of solvent leads to increase drug solubility in solvent and consequently, it will be difficult to achieve high supersaturation level during nanoprecipitation process at microfluidic reactor. As a result, this may cause to a low nucleation rate and increased particle size at high temperature values in comparison to low solvent temperature in which solubility of drug in solvent decreases and supersaturation occurs easily (31). Also, nucleation process is a favorable process energetically (i.e., by releasing heat). Therefore, number of created drug nuclei increases at low temperature of solvent (i.e., high nucleation rate/low growth rate and decrease at particle size of output) (32). In addition, with increasing solvent temperature, diffusion of solute molecules into antisolvent flow will increase. So, more drug nuclei will form (i.e., enhanced nucleation than growth) (27) and consequently, particle size in nanosuspension decreases, as observed in lower ranges of solvent temperature (an indirect relation).

The impact of surfactant concentration on particle size is different in low and high values of solvent temperature. The findings in Figs. 3 and 5 show that particle size in nanosuspension decreases against increasing Tween 80 concentration in low (or mid-range) values of solvent temperature, as reported in previous studies by M.E. Matteucci et al. and Y. Dong et al. (33,34). It is clear that surfactant acts as a steric barrier against particles growth generated by precipitation; as a result, nucleation rate than growth rate increases with enhancing surfactant concentration (33,34). On the other hand, an increase at surfactant concentration in the high values of solvent temperature results in the increasing particle size of output. This may be due to an increase in the diffusion coefficient of surfactant at higher temperature that can result in increasing surfactant molecules uncontrollably on the surface of particles generated during nanoprecipitation at microfluidic channel. Consequently, this can lead to reduce in the mobility of particles dispersed and increase in the observed particle size using dynamic light scattering.

CONCLUSION

In the current study, modeling by ANNs software has been applied to create a quality model that illustrated effects of solvent flow rate and temperature, antisolvent flow rate, and surfactant concentration on the particle size in nanosuspension formulation of stable iodine (127I) obtained by nanoprecipitation technique at microfluidic devices. The obtained response surfaces after modeling showed that input parameters have remarkable impacts on the size of stable iodine (127I) nanocrystals. Enhancement of flow rate of solvent and antisolvent caused a decrease in the size of stable iodine (127I) nanoparticles. The low and high solvent temperature had an indirect and direct relation, respectively, with particle size of nanosuspension. Also, the size of nanoparticles decreased against increasing the Tween 80 concentration in the low values of solvent temperature, while an increase in Tween 80 concentration at high values of solvent temperature resulted in increasing particle size.

Notes

ACKNOWLEDGMENTS

This project was supported by the vice-chancellor of research at Bushehr University of Medical Sciences and Health Services grant no 20-18-3-46333. The author wishes also to thank Dr. Afshin Ostovar for his support in this research.

Conflict of Interest

The authors express that they have no conflicts of interest declaration to display.

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Copyright information

© American Association of Pharmaceutical Scientists 2015

Authors and Affiliations

  • Mohamad Hosein Aghajani
    • 1
  • Ali Mahmoud Pashazadeh
    • 2
  • Seyed Hossein Mostafavi
    • 3
    • 4
  • Shayan Abbasi
    • 5
  • Mohammad-Javad Hajibagheri-Fard
    • 6
  • Majid Assadi
    • 2
  • Mahdi Aghajani
    • 2
    • 3
  1. 1.Faculty of Advanced Medical TechnologyGolestan University of Medical SciencesGorganIran
  2. 2.Department of Nanotechnology, The Persian Gulf Nuclear Medicine Research Center, The Persian Gulf Biomedical Sciences InstituteBushehr University of Medical SciencesBushehrIran
  3. 3.Department of Medical Nanotechnology, School of Advanced Technologies in MedicineTehran University of Medical SciencesTehranIran
  4. 4.Nanotechnology Research Centre, Faculty of PharmacyTehran University of Medical SciencesTehranIran
  5. 5.Institute of Biochemistry and Biophysics (IBB)University of TehranTehranIran
  6. 6.Shohadaye Khalije Fars HospitalBushehr University of Medical SciencesBushehrIran

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