Fuzzy modeling of skin permeability coefficients

Angela K. Pannier, Rhonda M. Brand, David D. Jones

Research output: Contribution to journalArticle

28 Scopus citations

Abstract

Purpose. The purpose of this work was to determine whether a new modeling methodology using fuzzy logic can predict skin permeability coefficients that are given compound descriptors that have been proven to affect percutaneous penetration. Methods. Three fuzzy inference models were developed using subtractive clustering to define natural structures within the data and assign subsequent rules. The numeric parameters describing the rules were refined through the use of an Adaptive Neural Fuzzy Inference System implemented in MatLab. Each model was evaluated using the entire data set. Then predicted outputs were compared to the published experimental data. Results. All databases produced fuzzy inference models that successfully predicted skin permeability coefficients, with correlation coefficients ranging from 0.83 to 0.97. The lowest correlation coefficient resulted from a model using log octanol/water partition coefficient and molecular weight as inputs with two input membership functions evaluated by two fuzzy rules. The correlation coefficient of 0.97 occurred when log octanol/water partition coefficient and hydrogen bond donor activity were used as inputs with three input membership functions evaluated by three fuzzy rules. Conclusions. Fuzzy rule-based models are a realistic and promising tool that can be used to successfully model and predict skin permeability coefficients as well as or better than previous algorithms with fewer inputs.

Original languageEnglish (US)
Pages (from-to)143-148
Number of pages6
JournalPharmaceutical Research
Volume20
Issue number2
DOIs
StatePublished - Feb 1 2003

Keywords

  • Adaptive neural fuzzy inference system
  • Clustering
  • Fuzzy logic
  • Percutaneous absorption
  • Skin permeability

ASJC Scopus subject areas

  • Biotechnology
  • Molecular Medicine
  • Pharmacology
  • Pharmaceutical Science
  • Organic Chemistry
  • Pharmacology (medical)

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