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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 

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.

REFERENCES

  1. 1.
    Lakshmi P, Kumar GA. Nanosuspension technology: a review. Int J Pharm Sci. 2010;2(4):35–40.Google Scholar
  2. 2.
    Kocbek P, Baumgartner S, Kristl J. Preparation and evaluation of nanosuspensions for enhancing the dissolution of poorly soluble drugs. Int J Pharm. 2006;312(1):179–86.CrossRefPubMedGoogle Scholar
  3. 3.
    Müller RH, Jacobs C, Kayser O. Nanosuspensions as particulate drug formulations in therapy: rationale for development and what we can expect for the future. Advanced Drug Delivery Reviews. 2001;47(1):3–19.CrossRefPubMedGoogle Scholar
  4. 4.
    Chingunpituk J. Nanosuspension technology for drug delivery. Walailak J Sci Tech. 2007;4(2):139–53.Google Scholar
  5. 5.
    Lindfors L, Skantze P, Skantze U, Westergren J, Olsson U. Amorphous drug nanosuspensions. 3. Particle dissolution and crystal growth. Langmuir. 2007;23(19):9866–74.CrossRefPubMedGoogle Scholar
  6. 6.
    Wu L, Zhang J, Watanabe W. Physical and chemical stability of drug nanoparticles. Adv Drug Deliv Rev. 2011;63(6):456–69.CrossRefPubMedGoogle Scholar
  7. 7.
    Pu X, Sun J, Li M, He Z. Formulation of nanosuspensions as a new approach for the delivery of poorly soluble drugs. Curr Nanosci. 2009;5(4):417–27.CrossRefGoogle Scholar
  8. 8.
    Patravale VB, Kulkarni RM. Nanosuspensions: a promising drug delivery strategy. J Pharm Pharmacol. 2004;56(7):827–40.CrossRefPubMedGoogle Scholar
  9. 9.
    Aghajani M, Shahverdi AR, Rezayat SM, Amini MA, Amani A. Preparation and optimization of acetaminophen nanosuspension through nanoprecipitation using microfluidic devices: an artificial neural networks study. Pharm Dev Technol. 2013;18(3):609–18.CrossRefPubMedGoogle Scholar
  10. 10.
    Weibel DB, Whitesides GM. Applications of microfluidics in chemical biology. Curr Opin Chem Biol. 2006;10(6):584–91.CrossRefPubMedGoogle Scholar
  11. 11.
    Voldman J, Gray ML, Schmidt MA. Microfabrication in biology and medicine. Annu Rev Biomed Eng. 1999;1(1):401–25.CrossRefPubMedGoogle Scholar
  12. 12.
    Crowe CT, Elger DF, Roberson JA. Engineering fluid mechanics. Hoboken: Wiley; 2005.Google Scholar
  13. 13.
    Miyazaki M, Honda T, Yamaguchi H, Briones MPP, Maeda H. Enzymatic processing in microfluidic reactors. Biotechnol Genet Eng Rev. 2008;25(1):405–28.CrossRefPubMedGoogle Scholar
  14. 14.
    Weigl BH, Bardell RL, Cabrera CR. Lab-on-a-chip for drug development. Adv Drug Deliv Rev. 2003;55(3):349–77.CrossRefPubMedGoogle Scholar
  15. 15.
    Panagiotou T, Mesite SV, Fisher RJ. Production of norfloxacin nanosuspensions using microfluidics reaction technology through solvent/antisolvent crystallization. Ind Eng Chem Res. 2009;48(4):1761–71.CrossRefGoogle Scholar
  16. 16.
    Wang J-X, Zhang Q-X, Zhou Y, Shao L, Chen J-F. Microfluidic synthesis of amorphous cefuroxime axetil nanoparticles with size-dependent and enhanced dissolution rate. Chem Eng J. 2010;162(2):844–51.CrossRefGoogle Scholar
  17. 17.
    Schianti JN, Cerize NN, de Oliveira AM, Derenzo S, Seabra AC, Góngora-Rubio MR. Rifampicin nanoprecipitation using flow focusing microfluidic device. J Nanomedicine Nanotechnol. 2013;4(4):2–172.CrossRefGoogle Scholar
  18. 18.
    Ali HS, York P, Blagden N. Preparation of hydrocortisone nanosuspension through a bottom-up nanoprecipitation technique using microfluidic reactors. Int J Pharm. 2009;375(1):107–13.CrossRefPubMedGoogle Scholar
  19. 19.
    Ali HS, Blagden N, York P, Amani A, Brook T. Artificial neural networks modelling the prednisolone nanoprecipitation in microfluidic reactors. Eur J Pharm Sci. 2009;37(3):514–22.CrossRefPubMedGoogle Scholar
  20. 20.
    Zhao H, Wang J-X, Wang Q-A, Chen J-F, Yun J. Controlled liquid antisolvent precipitation of hydrophobic pharmaceutical nanoparticles in a microchannel reactor. Ind Eng Chem Res. 2007;46(24):8229–35.CrossRefGoogle Scholar
  21. 21.
    Agatonovic-Kustrin S, Beresford R. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal. 2000;22(5):717–27.CrossRefPubMedGoogle Scholar
  22. 22.
    Amani A, Mohammadyani D. Artificial neural networks: applications in nanotechnology. Artificial neural networks—application Rijeka. INTECH; 2011.Google Scholar
  23. 23.
    Shao Q, Rowe RC, York P. Comparison of neurofuzzy logic and neural networks in modelling experimental data of an immediate release tablet formulation. Eur J Pharm Sci. 2006;28(5):394–404.CrossRefPubMedGoogle Scholar
  24. 24.
    Amani A, York P, Chrystyn H, Clark BJ. Factors affecting the stability of nanoemulsions—use of artificial neural networks. Pharm Res. 2010;27(1):37–45.CrossRefPubMedGoogle Scholar
  25. 25.
    Muthu M, Singh S. Poly (D, L-Lactide) nanosuspensions of risperidone for parenteral delivery: formulation and in-vitro evaluation. Current Drug Deliv. 2009;6(1):62–8.CrossRefGoogle Scholar
  26. 26.
    Amani A, York P, Chrystyn H, Clark BJ, Do DQ. Determination of factors controlling the particle size in nanoemulsions using artificial neural networks. Eur J Pharm Sci. 2008;35(1):42–51.CrossRefPubMedGoogle Scholar
  27. 27.
    Aghajani M, Shahverdi AR, Amani A. The use of artificial neural networks for optimizing Polydispersity Index (PDI) in nanoprecipitation process of acetaminophen in microfluidic devices. AAPS PharmSciTech. 2012;13(4):1293–301.PubMedCentralCrossRefPubMedGoogle Scholar
  28. 28.
    Rosenfeld C, Serra C, Brochon C, Hadziioannou G. Influence of micromixer characteristics on polydispersity index of block copolymers synthesized in continuous flow microreactors. Lab Chip. 2008;8(10):1682–7.CrossRefPubMedGoogle Scholar
  29. 29.
    Ali HS, York P, Ali A, Blagden N. Hydrocortisone nanosuspensions for ophthalmic delivery: a comparative study between microfluidic nanoprecipitation and wet milling. J Control Release. 2011;149(2):175–81.CrossRefPubMedGoogle Scholar
  30. 30.
    Su Y-F, Kim H, Kovenklioglu S, Lee W. Continuous nanoparticle production by microfluidic-based emulsion, mixing and crystallization. J Solid State Chem. 2007;180(9):2625–9.CrossRefGoogle Scholar
  31. 31.
    Zhang J-Y, Shen Z-G, Zhong J, Hu T-T, Chen J-F, Ma Z-Q, et al. Preparation of amorphous cefuroxime axetil nanoparticles by controlled nanoprecipitation method without surfactants. Int J Pharm. 2006;323(1):153–60.PubMedGoogle Scholar
  32. 32.
    Fokin VM, Yuritsyn NS, Zanotto ED. Nucleation and crystallization kinetics in silicate glasses: theory and experiment. Nucleation Theory Appl. 2005:74–125.Google Scholar
  33. 33.
    Matteucci ME, Hotze MA, Johnston KP, Williams RO. Drug nanoparticles by antisolvent precipitation: mixing energy versus surfactant stabilization. Langmuir. 2006;22(21):8951–9.CrossRefPubMedGoogle Scholar
  34. 34.
    Dong Y, Ng WK, Shen S, Kim S, Tan RB. Preparation and characterization of spironolactone nanoparticles by antisolvent precipitation. Int J Pharm. 2009;375(1):84–8.CrossRefPubMedGoogle Scholar

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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