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

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.

Application of the mol2vec Technology to Large-size Data Visualization and Analysis.

Generative Topographic Mapping (GTM) is a dimensionality reduction method, which is widely used for both data visualization and structure-activity modeling. Large dimensionality of the initial data space may require significant computational resources and slow down the GTM construction. Therefore, it may be meaningful to reduce the number of descriptors used for encoding molecular structures. The Principal Component Analysis (PCA), a standard preprocessing tool, suffers from the information loss upon the dimensionality reduction. As an alternative, we propose to use substructure vector embedding provided by the mol2vec technique. In addition to the data dimensionality reduction, this technology also accounts for proximity of substructures in molecular graphs. In this study, dimensionality of large descriptor spaces of ISIDA fragment descriptors or Morgan fingerprints were reduced using either the PCA or the mol2vec method. The latter significantly speeds up GTM training without compromising its predictive power in bioactivity classification tasks.

A Novel Calibration-Minimum Method for Prediction of Mole Fraction in Non-Ideal Mixture.

This article proposes a novel concentration prediction model that requires little training data and is useful for rapid process understanding. Process analytical technology is currently popular, especially in the pharmaceutical industry, for enhancement of process understanding and process control. A calibration-free method, iterative optimization technology (IOT), was proposed to predict pure component concentrations, because calibration methods such as partial least squares, require a large number of training samples, leading to high costs. However, IOT cannot be applied to concentration prediction in non-ideal mixtures because its basic equation is derived from the Beer-Lambert law, which cannot be applied to non-ideal mixtures. We proposed a novel method that realizes prediction of pure component concentrations in mixtures from a small number of training samples, assuming that spectral changes arising from molecular interactions can be expressed as a function of concentration. The proposed method is named IOT with virtual molecular interaction spectra (IOT-VIS) because the method takes spectral change as a virtual spectrum x nonlin,i into account. It was confirmed through the two case studies that the predictive accuracy of IOT-VIS was the highest among existing IOT methods.