Systematic development of pH-independent controlled release tablets of carvedilol using central composite design and artificial neural networks

Aktas E., EROĞLU H., Kockan U., ÖNER L.

DRUG DEVELOPMENT AND INDUSTRIAL PHARMACY, cilt.39, sa.8, ss.1207-1216, 2013 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 39 Sayı: 8
  • Basım Tarihi: 2013
  • Doi Numarası: 10.3109/03639045.2012.705291
  • Dergi Adı: DRUG DEVELOPMENT AND INDUSTRIAL PHARMACY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1207-1216
  • Anahtar Kelimeler: Carvedilol, pH-dependent solubility, pH-independent release, artificial neural network, HPMC K4M, EUDRAGIT L100, WEAKLY BASIC DRUG, MATRIX TABLETS, HYDROPHILIC MATRICES, DELIVERY-SYSTEMS, BETA-BLOCKERS, HYDROCHLORIDE, OPTIMIZATION, FORMULATION, DISSOLUTION, SOLUBILITY
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

The purpose of this study was to apply the optimization method incorporating artificial neural network (ANN) using pH-independent release of weakly basic drug, carvedilol from HPMC-based matrix formulation. Because of weakly basic nature of carvedilol, drug shows pH-dependent solubility. The enteric polymer EUDRAGIT L100 was added formulations to overcome pH-dependent solubility of carvedilol. Effects of the Hydroxypropylmethyl cellulose (HPMC) K4M and EUDRAGIT L100 amount on drug release were investigated. For this purpose 13 kinds of formulations were prepared at three different levels of each variables. The optimization of the formulation was evaluated by using ANN method. Two formulation parameters, the amounts of HPMC K4M and Eudragit L100 at three levels (-1, 0, 1) were selected as independent/input variables. In-vitro dissolution sampling times at twelve different time points were selected as dependent/output variables. By using experimental dissolution results and amount of HPMC K4M and EUDRAGIT L100, percentage of dissolved carvedilol was predicted by ANN. Similarity factor (f(2)) between predicted and experimentally observed profile was calculated and f(2) value was found 76.33. This value showed that there was no difference between predicted and experimentally observed drug release profile. As a result of these experiments, it was found that ANNs can be successfully used to optimize controlled release drug delivery systems.