Abstract
A critical step in the production of Esomeprazole powder for solution is a period between the filling process and lyophilization, where all vials, partially closed, are completely exposed to environmental influences. Excessive instability reflects in pH value variations caused by oxygen’s impact. In order to provide pH control, which consequently affects drug stability, Esomeprazole batches, produced in the same way, were kept in partially closed vials for 3 h at temperatures of 20 °C and −30 °C, after which they were lyophilized and stored for long-term stability for 36 months. The aim of the presented study was to apply a deep-learning algorithm for the prediction of the Esomeprazole stability profile and to determine the pH limit for the reconstituted solution of the final freeze-dried product that would assure a quality product profile over a storage period of 36 months. Multilayer perceptron (MLP) as a deep learning tool, with four layers, was used. The pH value of Esomeprazole solution and time of storage (months) were inputs for the network, while Esomeprazole assay and four main impurities were outputs of the network. In order to keep all related substances and Esomeprazole assay in accordance with specifications for the whole shelf life, the pH value for the reconstituted finish product should be set in the range of 10.4–10.6.
Highlights
Data mining is recognized as a useful tool in the prediction and control of drug product quality profiles in a Pharma 4.0 concept in the pharmaceutical industry
Excessive instability of Esomeprazole in solution reflects in the variations of the pH value of the solution due to sensitivity to oxygen. This lack of stability further leads to many issues such as changes in related substances’ content, affecting the assay results through the shelf life
Starting from the pH value of the bulk Esomeprazole solution, which decreases at different speeds depending upon the conditions to which it is exposed, makes it hard to control and predict the pH value for the lyophilized product at the end of the production process, and to control its behavior during its shelf life
Summary
Data mining is recognized as a useful tool in the prediction and control of drug product quality profiles in a Pharma 4.0 concept in the pharmaceutical industry. Deep learning is a subfield of data mining methods that uses algorithms called artificial neural networks (ANNs). It has been successfully used over the past two decades in formulation and process development both in academia and in pharmaceutical applications [1,2,3,4]. There are only a few attempts in the literature of data mining applications in drug stability prediction. We compared multiple regression analysis (MRA) and dynamic neural network (DNN) for the prediction of the stability of Hydrocortisone
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
