Next Article in Journal
Undercover Toxic Ménage à Trois of Amylin, Copper (II) and Metformin in Human Embryonic Kidney Cells
Next Article in Special Issue
Stability-Indicating Analytical Approach for Stability Evaluation of Lactoferrin
Previous Article in Journal
Functionality and Acceptance of the EsoCap System—A Novel Film-Based Drug Delivery Technology: Results of an In Vivo Study
Previous Article in Special Issue
Comprehensive Stability Study of Vitamin D3 in Aqueous Solutions and Liquid Commercial Products
Article

Prediction of Drug Stability Using Deep Learning Approach: Case Study of Esomeprazole 40 mg Freeze-Dried Powder for Solution

1
Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia
2
Faculty of Physical Chemistry, University of Belgrade, Studentski trg 12-16, 11158 Belgrade, Serbia
*
Author to whom correspondence should be addressed.
Academic Editor: Beverley D. Glass
Pharmaceutics 2021, 13(6), 829; https://doi.org/10.3390/pharmaceutics13060829
Received: 6 May 2021 / Revised: 28 May 2021 / Accepted: 31 May 2021 / Published: 3 June 2021
(This article belongs to the Special Issue Drug Stability and Stabilization Techniques Volume II)
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. View Full-Text
Keywords: esomeprazole sodium; stability; deep learning; multilayer perceptron; artificial neural network; freeze-drying; pH; temperature; related substances; assay esomeprazole sodium; stability; deep learning; multilayer perceptron; artificial neural network; freeze-drying; pH; temperature; related substances; assay
Show Figures

Figure 1

MDPI and ACS Style

Ajdarić, J.; Ibrić, S.; Pavlović, A.; Ignjatović, L.; Ivković, B. Prediction of Drug Stability Using Deep Learning Approach: Case Study of Esomeprazole 40 mg Freeze-Dried Powder for Solution. Pharmaceutics 2021, 13, 829. https://doi.org/10.3390/pharmaceutics13060829

AMA Style

Ajdarić J, Ibrić S, Pavlović A, Ignjatović L, Ivković B. Prediction of Drug Stability Using Deep Learning Approach: Case Study of Esomeprazole 40 mg Freeze-Dried Powder for Solution. Pharmaceutics. 2021; 13(6):829. https://doi.org/10.3390/pharmaceutics13060829

Chicago/Turabian Style

Ajdarić, Jovana, Svetlana Ibrić, Aleksandar Pavlović, Ljubiša Ignjatović, and Branka Ivković. 2021. "Prediction of Drug Stability Using Deep Learning Approach: Case Study of Esomeprazole 40 mg Freeze-Dried Powder for Solution" Pharmaceutics 13, no. 6: 829. https://doi.org/10.3390/pharmaceutics13060829

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop