Comparison of statistical analysis and Bayesian Networks in the evaluation of dissolution performance of BCS Class II model drugs.
This project compared the effect of formulation variables on the dissolution performance of model Biopharmaceutics Classification System (BCS) Class II drugs from hard gelatin capsules using statistical analysis and Bayesian networks. The drugs chosen for this study were carbamazepine (CAR), chlorpropamide (CHL), diazepam (DIA), ketoprofen (KET), and naproxen (NAP). Formulations contained anhydrous lactose, microcrystalline cellulose, sodium stearyl fumerate, sodium lauryl sulfate, and croscarmellose sodium. A Box-Behnken experimental design was used in the statistical analysis. The weakly acidic drugs were tested using USP apparatus II with capsule sinkers in 0.1M pH 6.8 Potassium Phosphate buffer. The weakly basic drugs were tested using USP apparatus I in 0.1N HCl buffer. Mean dissolution profiles were compared via calculation of the similarity factor. The Box-Behnken experimental design was found to be useful in assessing primary and secondary excipient effects on dissolution. The Bayesian Network developed for the dataset mirrored the key excipient effects on dissolution performance.
Wilson
Wendy I
WI
Department of Pharmaceutical Sciences, University of Maryland, Baltimore, Baltimore, Maryland 21201, USA. WWilson@huber.com
Peng
Yun
Y
Augsburger
Larry L
LL
D001499:Bayes Theorem; D002214:Capsules; D002626:Chemistry, Pharmaceutical / Q000706:statistics & numerical data*; D005780:Gelatin; D008956:Models, Chemical; D015999:Multivariate Analysis; D004364:Pharmaceutical Preparations / Q000737:chemistry* / Q000145:classification; D012995:Solubility
D002214:Capsules; D004364:Pharmaceutical Preparations; D005780:Gelatin