Latent Structure Analysis in Pharmaceutical Formulations Using Kohonen's Self-Organizing Map and a Bayesian Network

• Published in: Journal of pharmaceutical sciences Vol. 100; no. 3; pp. 964 - 975
• Main Authors: Kikuchi, Shingo, Onuki, Yoshinori, Yasuda, Akihito, Hayashi, Yoshihiro, Takayama, Kozo
• Format: Journal Article
• Language: English
• Published: Hoboken Elsevier Inc 01.03.2011; Wiley Subscription Services, Inc., A Wiley Company; Wiley; American Pharmaceutical Association; Elsevier Limited
• Subjects: Algorithms; Bayes Theorem; Biological and medical sciences; Calcium Channel Blockers - chemistry; Calcium Channel Blockers - pharmacokinetics; Cluster Analysis; controlled release; Dextran Sulfate - analogs & derivatives; Dextran Sulfate - chemistry; Diltiazem - chemistry; Diltiazem - pharmacokinetics; dissolution rate; Dosage Forms; Drug Compounding - methods; excipients; formulation; General pharmacology; Hydrophobic and Hydrophilic Interactions; Hypromellose Derivatives; Medical sciences; Methylcellulose - analogs & derivatives; Methylcellulose - chemistry; Models, Statistical; multivariate analysis; nonlinear regression; Pharmaceutical Preparations - chemistry; Pharmaceutical technology. Pharmaceutical industry; Pharmacology. Drug treatments; Polymers - chemistry; preformulation; simulations; solid dosage form; Solubility; Tablets - chemistry; Viscosity; nonlinear regression; dissolution rate; controlled release; formulation; excipients; multivariate analysis; solid dosage form; preformulation; simulations; Pharmaceutical technology; Controlled release form; Control release polymer; Vehicle(excipient); Multivariate analysis; Dissolution; Simulation; Formulation; Dosage form; Solid form
• ISSN: 0022-3549; 1520-6017; 1520-6017
• DOI: 10.1002/jps.22340

A latent structure analysis of pharmaceutical formulations was performed using Kohonen's self-organizing map (SOM) and a Bayesian network. A hydrophilic matrix tablet containing diltiazem hydrochloride (DTZ), a highly water-soluble model drug, was used as a model formulation. Nonlinear relationship correlations among formulation factors (oppositely charged dextran derivatives and hydroxypropyl methylcellulose), latent variables (turbidity and viscosity of the polymer mixtures and binding affinity of DTZ to polymers), and release properties [50% dissolution times (t50s) and similarity factor] were clearly visualized by self-organizing feature maps. The quantities of dextran derivatives forming polyion complexes were strongly related to the binding affinity of DTZ to polymers and t50s. The latent variables were classified into five characteristic clusters with similar properties by SOM clustering. The probabilistic graphical model of the latent structure was successfully constructed using a Bayesian network. The causal relationships among the factors were quantitatively estimated by inferring conditional probability distributions. Moreover, these causal relationships estimated by the Bayesian network coincided well with estimations by SOM clustering, and the probabilistic graphical model was reflected in the characteristics of SOM clusters. These techniques provide a better understanding of the latent structure between formulation factors and responses in DTZ hydrophilic matrix tablet formulations.