Modeling of a roller-compaction process using neural networks and genetic algorithms

Turkoglu M., Aydin I., Murray M., Sakr A.

EUROPEAN JOURNAL OF PHARMACEUTICS AND BIOPHARMACEUTICS, cilt.48, sa.3, ss.239-245, 1999 (SCI İndekslerine Giren Dergi) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 48 Konu: 3
  • Basım Tarihi: 1999
  • Doi Numarası: 10.1016/s0939-6411(99)00054-5
  • Sayfa Sayıları: ss.239-245


In this study, roller-compaction of acetaminophene was studied to model the effect of binder type (hydroxypropyl methyl cellulose (HPMC), polyethylene glycol (PEG), Carbopol), binder concentration (5, 10 and 20%), number of roller-compaction passes (one or two), and extragranular microcrystalline cellulose addition on the properties of compressed tablets. Forty-two batches resulted from the experimental design. The artificial neural network methodology (ANN) along with genetic algorithms were used for data analysis and optimization. ANN and genetic models provided R-2 values between 0.3593 and 0.9991 for measured responses. When a set of validation experiments was analyzed, genetic algorithm predictions of tablet characteristics were much better than the ANN. Optimization based on genetic algorithm showed that using HPMC at 20%, with two roller-compaction passes would produce mechanically acceptable acetaminophene tablets. PEG and carbopol would also produce acceptable tablets perhaps more suitable for sustained release applications. Using PEG as a binder had the additional advantage of not requiring an external lubricant during tablet manufacturing. (C) 1999 Elsevier Science B.V. All rights reserved.