Effect of roll compaction on granule size distribution of microcrystalline cellulose-mannitol mixtures: computational intelligence modeling and parametric analysis

Abstract

Dry granulation using roll compaction is a typical unit operation for producing solid dosage forms in the pharmaceutical industry. Dry granulation is commonly used if the powder mixture is sensitive to heat and moisture and has poor flow properties. The output of roll compaction is compacted ribbons that exhibit different properties based on the adjusted process parameters. These ribbons are then milled into granules and finally compressed into tablets. The properties of the ribbons directly affect the granule size distribution (GSD) and the quality of final products; thus, it is imperative to study the effect of roll compaction process parameters on GSD. The understanding of how the roll compactor process parameters and material properties interact with each other will allow accurate control of the process, leading to the implementation of quality by design practices. Computational intelligence (CI) methods have a great potential for being used within the scope of quality by design approach. The main objective of this study was to show how the computational intelligence techniques can be useful to predict the GSD by using different process conditions of roll compaction and material properties. Different techniques such as multiple linear regression, artificial neural networks, random forest, Cubist and k-nearest neighbors algorithm assisted by sevenfold cross-validation were used to present generalized models for the prediction of GSD based on roll compaction process setting and material properties. The normalized root-mean-squared error and the coefficient of determination (R²) were used for model assessment. The best fit was obtained by Cubist model (normalized root-mean-squared error =3.22%, R²=0.95). Based on the results, it was confirmed that the material properties (true density) followed by compaction force have the most significant effect on GSD.

Keywords: Cubist; computational intelligence; dry granulation; milling; neural network; roll compaction.

Figure 1

Schematic diagram of sevenfold cross-validation.

Figure 2

Predicted versus actual granule size (q3[x]) obtained by MLR models. Abbreviation: MLR, multiple linear regression.

Figure 3

Predicted versus actual granule size (q3[x]) obtained by Cubist (A) and ANN (B) models. Abbreviation: ANN, artificial neural network.

Figure 4

Deficiency of ANN model in the prediction of entire GSD. Abbreviations: ANN, artificial neural network; GSD, granule size distribution; k-NN, k-nearest neighbors algorithm.

Figure 5

Variable importance based on Cubist model. Abbreviations: FA, feeding auger; TA, tamping auger.

Figure 6

Effect of different parameters on granule size (d₅₀) (A) as a function of true density, (B) as a function of compaction force, (C) as a function of gap width. Abbreviation: d₅₀, mean particle size.

Figure 7

Surface plots of predicted volume distribution, q3(x), as a function of compaction force.

Figure 8

Correlation between input parameters based on Pearson correlation. Note: d₅₀, mean particle size. Abbreviations: FA, feeding auger; TA, tamping auger.