title: Adaptive neuro-fuzzy modeling of poorly soluble drug formulations.
abstract: OBJECTIVE
  The purpose of this study was to evaluate the efficiency of a neuro-fuzzy logic-based methodology to model poorly soluble drug formulations and predict the development of the particle size that has been proven to be an important factor for long-term stability.
  
  
  METHODS
  An adaptive neuro-fuzzy inference system was used to model the natural structures within the data and construct a set of fuzzy rules that can subsequently used as a predictive tool. The model was implemented in Matlab 6.5 and trained using 75% of an experimental data set. Subsequently, the model was evaluated and tested using the remaining 25%, and the predicted values of the particle size were compared to the ones from the experimental data. The produced adaptive neuro-fuzzy inference system-based model consisted of four inputs, i.e., acetone, propylene glycol, POE-5 phytosterol (BPS-5), and hydroxypropylmethylcellulose 90SH-50, with four membership functions each. Moreover, 256 fuzzy rules were employed in the model structure.
  
  
  RESULTS
  Model training resulted in a root mean square error of 4.5 x 10(-3), whereas model testing proved its highly predictive efficiency, achieving a correlation coefficient of 0.99 between the actual and the predicted values of the particle size (mean diameter).
  
  
  CONCLUSIONS
  Neuro-fuzzy modeling has been proven to be a realistic and promising tool for predicting the particle size of drug formulations with an easy and fast way, after proper training and testing.
authors.affiliation: Phoqus Pharmaceuticals Limited, 10 Kings Hill Avenue, Kings Hill, West Malling, Kent, ME19 4PQ, UK. dionysios.douroumis@phoqus.com
authors.forename: Dionysios
authors.initials: D
authors.lastname: Douroumis
authors.forename: Leontios J
authors.initials: LJ
authors.lastname: Hadjileontiadis
authors.forename: Alfred
authors.initials: A
authors.lastname: Fahr
mesh_terms: D000096:Acetone / Q000737:chemistry; D002626:Chemistry, Pharmaceutical; D004355:Drug Stability; D005079:Excipients / Q000737:chemistry*; D017143:Fuzzy Logic; D065347:Hypromellose Derivatives; D008747:Methylcellulose / Q000031:analogs & derivatives / Q000737:chemistry; D016571:Neural Networks, Computer; D010316:Particle Size; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D010840:Phytosterols / Q000737:chemistry; D019946:Propylene Glycol / Q000737:chemistry; D015203:Reproducibility of Results; D012995:Solubility; D013678:Technology, Pharmaceutical / Q000379:methods*
chemical_list: D005079:Excipients; D004364:Pharmaceutical Preparations; D010840:Phytosterols; D000096:Acetone; D065347:Hypromellose Derivatives; D019946:Propylene Glycol; D008747:Methylcellulose; C033580:soysterol