Industrial application of QbD and NIR chemometric models in quality improvement of immediate release tablets

Ahmed Ramadan; Emad B Basalious; Mohamed Abdallah

doi:10.1016/j.jsps.2021.04.012

Industrial application of QbD and NIR chemometric models in quality improvement of immediate release tablets

Saudi Pharm J. 2021 Jun;29(6):516-526. doi: 10.1016/j.jsps.2021.04.012. Epub 2021 Apr 22.

Authors

Ahmed Ramadan^{1

2}, Emad B Basalious¹, Mohamed Abdallah^{1

3}

Affiliations

¹ Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Cairo University, Egypt.
² Department of Applied Statistics, Faculty of Postgraduate Studies for Statistical Research, Cairo University, Egypt.
³ Department of Pharmaceutics and Industrial Pharmacy, School of Pharmacy, New Giza University, Egypt.

Abstract

Quality by Design (QbD) and chemometric models are different sides of the same coin. While QbD models utilize experimentally designed settings for optimization of some quality attributes, these settings can also be utilized for chemometric prediction of the same attributes. We aimed to synchronize optimization of comparative dissolution results of carvedilol immediate release tablets with chemometric prediction of dissolution profile and content uniformity of the product. As an industrial application, selection of variables for optimization was done by performing risk assessment utilizing the archived product records at the pharmaceutical site. Experimental tablets were produced with 20 different settings with the variables being contents of sucrose, sodium starch glycolate, lactose monohydrate, and avicel Ph 101. Contents of the excipients were modelled with F1 dissimilarity factor and F2 similarity factor in HCL, acetate, and USP dissolution media to determine the design space. We initiatively utilized Partial Least Square based Structural Equation Modelling (PLS-SEM) to explore how the excipients and their NIR records explained dissolution of the product. Finally, the optimized formula was utilized with varied content of carvedilol for chemometric prediction of the content uniformity.

Keywords: Artificial Intelligence; Chemometrics; Content Uniformity; Dissolution; Formulation; NIR, Near Infra-red; Near Infrared Spectroscopy (NIRS); PAT, Process Analytical Technology; PC, Principal Component; PCA, Principal Component Analysis; PLS, Partial Least Square; QbD, Quality by Design; Quality by Design (QbD); RMSEP, Root Mean Square Error of Prediction; SEM, Structural Equation Modelling.