Modeling of a roller-compaction process using neural networks and genetic algorithms

Abstract

In this study, roller-compaction of acetaminophene was studied to model the effect of binder type (hydroxypropyl methyl cellulose (HPMC), polyethylene glycol (PEG), Carbopol), binder concentration (5, 10 and 20%), number of roller-compaction passes (one or two), and extragranular microcrystalline cellulose addition on the properties of compressed tablets. Forty-two batches resulted from the experimental design. The artificial neural network methodology (ANN) along with genetic algorithms were used for data analysis and optimization. ANN and genetic models provided R2 values between 0.3593 and 0.9991 for measured responses. When a set of validation experiments was analyzed, genetic algorithm predictions of tablet characteristics were much better than the ANN. Optimization based on genetic algorithm showed that using HPMC at 20%, with two roller-compaction passes would produce mechanically acceptable acetaminophene tablets. PEG and carbopol would also produce acceptable tablets perhaps more suitable for sustained release applications. Using PEG as a binder had the additional advantage of not requiring an external lubricant during tablet manufacturing.