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. 2005 Oct 22;6(3):E449–E457. doi: 10.1208/pt060356

Generalization of a prototype intelligent hybrid system for hard gelatin capsule formulation development

Wendy I Wilson 1,✉, Yun Peng 3, Larry L Augsburger 1
PMCID: PMC2750390  PMID: 16354004

Abstract

The aim of this project was to expand a previously developed prototype expert network for use in the analysis of multiple biopharmaceutics classification system (BCS) class II drugs. The model drugs used were carbamazepine, chlorpropamide, diazepam, ibuprofen, ketoprofen, naproxen, and piroxicam. Recommended formulations were manufactured and tested for dissolution performance. A comprehensive training data set for the model drugs was developed and used to retrain the artificial neural network. The training and the system were validated based on the comparison of predicted and observed performance of the recommended formulations. The initial test of the system resulted in high error values, indicating poor prediction capabilities for drugs other than piroxicam. A new data set, containing 182 batches, was used for retraining. The percent of the test batches were used for cross-validation, resulting in models with R2≥70%. The comparison of observed performance to the predicted performance found that the system predicted succcessfully. The hybrid network was generally able to predict the amount of drug dissolved within 5% for the model drugs. Through validation, the system was proven to be capable of designing formulations that met specific drug performance criteria. By including parameters to address wettability and the intrinsic dissolution characteristics of the drugs, the hybrid system was shown to be suitable for analysis of multiple BCS class II drugs.

Keywords: in silico modeling, capsule formulation, artificial neural networks, expert systems, low solubility drugs

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