AAPS PharmSciTech

, Volume 4, Issue 1, pp 62–70 | Cite as

Artificial neural networks in the modeling and optimization of aspirin extended release tablets with eudragit L 100 as matrix substance

  • Svetlana IbrićEmail author
  • Milica Jovanović
  • Zorica Djurić
  • Jelena Parojčić
  • Slobodan D. Petrović
  • Ljiljana Solomun
  • Biljana Stupar


The purpose of the present study was to model the effects of the concentration of Eudragit L 100 and compression pressure as the most important process and formulation variables on the in vitro release profile of aspirin from matrix tables formulated with Eudragit L 100 as matrix substance and to optimize the formulation by artificial neural network. As model formulations, 10 kinds of aspirin matrix tablets were prepared. The amount of Eudragit L 100 and the compression pressure were selected as causal factors. In vitro dissolution time profiles at 4 different sampling times were chosen as responses. A set of release parameters and causal factors were used as tutorial data for the generalized regression neural, network (GRNN) and analyzed using a computer. Observed results of drug release studies indicate that drug release rates vary widely between investigated formulations, with a range of 5 hours to more than 10 hours to complete dissolution. The GRNN model was optimized. The root mean square value for the trained network was 1.12%, which indicated that the optimal GRNN model was reached. Applying the generalized distance function method, the optimal tablet formulation predicted by GRNN was with 5% of Eudragit L 100 and tablet hardness 60N. Calculated difference (f 1 2.465) and similarity (f 2 85.61) factors indicate that there is no difference between predicted and experimentally observed drug release profiles for the optimal formulation. This work illustrates the potential for an artificial neural network, GRNN, to assist in development of extended release dosage forms.


artificial neural network matrix tablets controlled release Eudragit L 100 aspirin 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Takayama K, Fujikawa M, Nagai T. Artificial neural networks as a novel method to optimize pharmaceutical formulations. Pharm Res. 1999;16(1):1–6.PubMedGoogle Scholar
  2. 2.
    Takahara J, Takayama K, Nagai T. Multi-objective simultaneous optimization technique based on an artificial neural, network in sustained release formulations. J Control Release. 1997;49:11–20.CrossRefGoogle Scholar
  3. 3.
    Kesavan JG, Peck GE. Pharmaceutical granulation and tablet formulation using neural networks. Pharm Dev Technol. 1996;1(4):391–404.PubMedCrossRefGoogle Scholar
  4. 4.
    Wu T, Pan W, Chen J, Zhang R. Formulation optimization technique based on artificial neural network in salbutamol sulfate osmotic pump tablets. Drug Dev Ind Pharm. 2000;26(2):211–215.PubMedCrossRefGoogle Scholar
  5. 5.
    Chen Y, McCall TW, Baichwal AR, Meyer MC. The application of an artificial neural network and pharmacokinetic simulations in the design of controlled-release dosage fomrs. J Control Release. 1999;59:33–41.PubMedCrossRefGoogle Scholar
  6. 6.
    Bourquin J, Schmidli H, Hoogevest P, Leuenberger H. Application of artificial neural networks (ANN) in the development of solid dosage forms. Pharm Dev Technol. 1997;2(2):111–121.PubMedCrossRefGoogle Scholar
  7. 7.
    Bourquin J, Schmidli H, Hoogevest P, Leuenberger H. Advantages of artificial neural networks (ANNs) as alternative modelling technique for data sets showing non-linear relationships using data from a galenical study on a solid dosage form. Eur J Pharm Sci. 1998;7:5–16.PubMedCrossRefGoogle Scholar
  8. 8.
    Bourquin J, Schmidli H, Hoogevest P, Leuenberger H. Pitfalls of artificial neural networks (ANN) modelling technique for data sets containing outlier measurements using a study on mixture properties of a direct compressed dosage form. Eur J Pharm Sci. 1998;7:17–28.PubMedCrossRefGoogle Scholar
  9. 9.
    Achanta AS, Kowalski JG, Rhodes CT. Artificial neural networks: implications for pharmaceutical sciences. Drug Dev Ind Pharm. 1995;21(1):119–155.CrossRefGoogle Scholar
  10. 10.
    Takka S, Rajbhandari S, Sakr A. Effect of anionic polymers on the release of propranolol hydrochloride from matrix tablets. Eur J Pharm Sci. 2001;52:75–82.Google Scholar
  11. 11.
    Rekhi GS, Nellore RV, Hussain AS, Tillmean LG, Malinowski HJ, Ausburger LL. Identification of critical formulation and processing variables for metoprolol tartarate extended release tablets. J Control Release. 1999;59:327–342.PubMedCrossRefGoogle Scholar
  12. 12.
    Jovanović M, Jovičić G, Durić Z, Agbaba D, Karlijković-Rajic K, Radovanović J, Nikolic L. Effect of fillers and lubricants on acetylsalicylic acid release kinetics from Eudragit matrix tablets. Drug Dev Ind Pharm. 1997;23:595–602.CrossRefGoogle Scholar
  13. 13.
    Jovanović M, Jovičić G, Durić Z, Agbaba D, Karljiković-Rajic K, Nikolić L, Radovanović J. The influence of Eudragit type on the dissolution rate of acetylsalicylic acid from matrix tablets. Acta Pharm Hung. 1997;67:229–234.PubMedGoogle Scholar
  14. 14.
    Speckt DF. A generalized regression neural network. IEEE Trans Neural Networks. 1991;2(6):568–576.CrossRefGoogle Scholar
  15. 15.
    Patterson D. Artificial Neural Networks. Singapore: Prentice Hall; 1996.Google Scholar
  16. 16.
    Bishop C. Neural Networks for Pattern Recognition. Oxford, England. Oxford University Press, 1995.Google Scholar
  17. 17.
    StatSoft, Inc. Manual of STATISTICA Neural Networks Software. Tulsa, OK. StatSoft, Inc; 1998.Google Scholar
  18. 18.
    Khuri I, Conlon M. Simultaneous optimization of multiple responses represented by polynomial regression functions. Technometrics. 1981;23:363–375.CrossRefGoogle Scholar
  19. 19.
    FDA Guidance for Industry: Dissolution Testing of Immediate Release Solid Oral Dosage Forms. Rockville, MD: Center for Drug Evaluation and Research; 1997.Google Scholar

Copyright information

© American Association of Pharmaceutical Scientists 2003

Authors and Affiliations

  • Svetlana Ibrić
    • 1
    Email author
  • Milica Jovanović
    • 1
  • Zorica Djurić
    • 1
  • Jelena Parojčić
    • 1
  • Slobodan D. Petrović
    • 2
  • Ljiljana Solomun
    • 2
  • Biljana Stupar
    • 2
  1. 1.Institute of Pharmaceutical Technology and Cosmetology, Faculty of PharmacyUniversity of BelgradeBelgradeSerbia and Montenegro
  2. 2.Hemofarm a.d., VrsacSerbia and Montenegro

Personalised recommendations