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Investigation of Preprocessing and Validation Methodologies for PAT: Case Study of the Granulation and Coating Steps for the Manufacturing of Ethenzamide Tablets

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Abstract

After the Food and Drug Association in the USA published guidelines on the enhanced use of process analytical technology (PAT) and continuous manufacturing, many studies regarding PAT and continuous manufacturing have been published. This paper describes a case study involving granulation and coating steps with ethenzamide to investigate interference for PAT model construction and model management. We investigated what factors should be considered and addressed when PAT is implemented for continuous manufacturing and how predictive models should be constructed. The product qualities that were monitored were moisture content and particle size in the granulation step and tablet weight and moisture content in the coating step. We have constructed models for the granulation step and validated the predictive capability of the models against an external dataset. A partial least squares (PLS) model with manual wavelength selection had the best predictive accuracy for loss on drying against the external validation set. We found that the prediction of loss on drying was accurate, but the prediction of particle size was not sufficiently accurate. In the coating step, because of the small amount of data, we performed three-fold cross-validation and y-scrambling 10 times, to select the optimal hyper-parameters and to check if the models were fitted to chance correlations. We confirmed that the coating agent weights, tablet weights, and water content could be accurately predicted based on the mean of the R2 score for cross-validation. Addition of other variables, as well as the absorbance, slightly improved the predictive accuracy.

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Acknowledgments

We acknowledge the support of the Core Research for Evolutionary Science and Technology (CREST) project “Development of a knowledge-generating platform driven by big data in drug discovery through production processes,” grant number JPMJCR1311 of the Japan Science and Technology Agency (JST). The author S. S. acknowledges financial support of the Japan Society for the Promotion of Science (JSPS) in Grant-in-Aid for JSPS Fellows (DC2 and PD) program. The authors wish to thank members of the Facility of the Future in ISPE Japan for their kind support. We thank Takashi Terada at Freund Corp. and Takuya Nagato and Yosuke Tomita at Powrex Corp. for their kind help, data acquisition, and fruitful discussions. Victoria Muir, PhD, from Edanz Group (https://en-author-services.edanzgroup.com/ac) edited a draft of this manuscript.

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  1. The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, Japan

    Shojiro Shibayama & Kimito Funatsu

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  1. Shojiro Shibayama
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Correspondence to Kimito Funatsu.

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Appendix 1

Appendix 1

We describe the overview of data-driven prediction models below to provide readers information of methods.

Genetic Algorithm

GA is an evolutionary optimization algorithm that mimics a way of evolution that organisms on the earth have experienced. A numerical solution of an optimization problem is coded as an organisms or “chromosomes” in GA. Every chromosome in the form of vector returns a fitness score that is a value of the objective function to be maximized (or minimized) in the optimization. Chromosomes improve themselves over “generations,” iterations in optimization, by mutation of the solutions or cross-over of the solutions among chromosomes that have higher (or smaller) fitness scores; namely, elements in vector of a chromosome are either randomly altered or shuffled across chromosomes.

Partial Least Squares

PLS is a rudimentary linear regression method. The concept of PLS is to extract latent variables that is best fit to prediction of objective variables. The input vector is weighted by weight vector that is the first eigenvector of the XTYYTX for input matrix X and output matrix Y. The inner product of the input and weight vectors is a scalar latent variable that stands best for objective variables in terms of prediction of the objective variables. Afterward, a linear multivariate regression model is fit to the latent variables and objective variables by least squares.

Support Vector Regression

SVR is a sparse modeling method for regression, which is fully based on support vector machine (SVM). The concept of SVM is to fit the classification model to data as measurement noise in the objective variable does not affect the model by sparse modeling. In a similar manner, SVR fits the regression model to data in ignorance of measurement noise.

Both SVM and SVR have interested researchers for decades, because they are easily incorporated into kernel trick. Kernel trick replaces inner product with a kernel function that is defined in reproducing kernel Hilbert space. SVR with RBF kernel is a model that expresses non-linear relationship between input and output variables.

Random Forest

RF, which can be utilized for both regression and classification, is an ensemble of decision trees that form a non-continuous regression or classification plane. RF attempts to minimizing variances of prediction by aggregation of sub-models through bagging.

A decision tree model splits a space that is formed by input variables into sub-spaces, each of which has a corresponding predicted value or predicted label. When input variables are determined, one regression or classification plane corresponding to the input returns a value or label. By counting how many times a variable splits the space for sub-spaces, one decision tree can return a feature importance. The frequency is averaged over samples, and then, the ratio based on the averaged frequency is calculated as feature importance. Feature importance reflects which variable affects the prediction results; therefore, large feature importance of RF for classification of training and test samples indicates variables that are most different between training samples and test samples.

Principal Component Analysis

PCA is a basic latent variable modeling method, which extracts latent variables by maximization of variance and covariance of input variables. The input variables after mean centering are projected onto new bases that capture the largest covariances of the data and the new measurements after the projection are the latent variables that account the input matrix in smaller dimensions with the standardized orthogonal bases.

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Shibayama, S., Funatsu, K. Investigation of Preprocessing and Validation Methodologies for PAT: Case Study of the Granulation and Coating Steps for the Manufacturing of Ethenzamide Tablets. AAPS PharmSciTech 22, 41 (2021). https://doi.org/10.1208/s12249-020-01911-w

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