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Neuro-fuzzy modeling of ibuprofen-sustained release from tablets based on different cellulose derivatives

Drug Delivery and Translational Research volume 9, pages 162–177 (2019)Cite this article

Abstract

In the present study, we investigated the drug release behavior from cellulose derivative (CD) matrices in the oral form of tablets. We used the adaptive neural-fuzzy inference system (ANFIS) to predict the best formulation parameters to get the perfect sustained drug delivery using ibuprofen (IB) as a model drug. The different formulations were prepared with different CDs, namely CMC, HEC, HPC, HPMC, and MC. The amount of the active ingredient varied between 20 and 50%. The flow properties of the powder mixtures were evaluated for their angle of repose, compressibility index, and Hausner ratio, while the tablets were evaluated for weight uniformity, hardness, friability, drug content, disintegration time, and release ratio. All tablet formulations presented acceptable pharmacotechnical properties. In general, the results showed that the drug release rate increases with an increase in the loaded drug. Kinetic studies using the Korsmeyer-Peppas equation showed that different drug release mechanisms were involved in controlling the drug dissolution from tablets. The drug release mechanism was influenced by the gel layer strength of the CDs formed in the dissolution medium. The mean dissolution time (MDT) was determined and the highest MDT value was obtained for the HPMC formulations. Moreover, HPMC exhibited release profiles adequate for sustained release formulations for over 14 h. The intelligent model based on the experimental data was used to predict the effect of the polymer’s nature, the amount of the active ingredient, and the kinetic release profile and rate (R2 = 0.9999 and RMSE = 5.7 × 10−3). The ANFIS model developed in this work could accurately model the relationship between IB release behavior and tablet formulation parameters. The proposed model was able to successfully describe this phenomenon and can be considered an efficient tool with predictive capabilities that is useful for the designing and testing of new dosage systems based on polymers.

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Affiliations

  1. Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Medea, Nouveau pôle urbain, Medea University, 26000, Medea, Algeria

    Samia Rebouh, Sonia Lefnaoui & Salah Hanini

  2. Faculty of Sciences, University of Medea, Nouveau pôle urbain, Medea University, 26000, Medea, Algeria

    Samia Rebouh, Sonia Lefnaoui & Madiha M. Yahoum

  3. Laboratory of Advanced Electronic Systems (LSEA), University of Medea, Nouveau pôle urbain, Medea University, 26000, Medea, Algeria

    Mounir Bouhedda

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  1. Samia Rebouh
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  2. Sonia Lefnaoui
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Rebouh, S., Lefnaoui, S., Bouhedda, M. et al. Neuro-fuzzy modeling of ibuprofen-sustained release from tablets based on different cellulose derivatives. Drug Deliv. and Transl. Res. 9, 162–177 (2019). https://doi.org/10.1007/s13346-018-00592-0

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Keywords

  • ANFIS
  • Cellulose derivatives
  • Matrix tablets
  • Modeling
  • Sustained release