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KMID : 1146920230530020269
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Journal of Pharmaceutical Investigation
2023 Volume.53 No. 2 p.269 ~ p.306
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A quality by design approach in oral extended release drug delivery systems: where we are and where we are going?
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A. S. Sousa
J. Serra
C. Estevens
R. Costa
A. J. Ribeiro
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Abstract
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Background : Oral extended release (ER) delivery systems have quickly gained increasing importance because of their ability to maintain drug levels in the blood more consistently, reducing side effects and improving patient compliance. The complexity of ER formulation leads to additional development challenges in the fulfilment of quality-related regulatory requirements. Despite their challenging properties, the potential of ER system formulation and process development can be better exploited by applying quality by design (QbD) approaches and advanced modeling techniques such as machine learning (ML).
Area covered : This review provides a comprehensive overview of QbD concepts applied to oral ER delivery systems, clarifying the impact of raw materials and process variables on critical quality attributes (CQAs). Moreover, data science coupled with ML algorithms is also elucidated in this article as a potential tool for predicting and optimizing ER formulation design and manufacturing processes.
Expert opinion : QbD, as a scientific and risk-based approach, provides a comprehensive understanding of oral ER drug delivery systems improving product quality and reducing postapproval changes. Enabling QbD with ML-driven pharmaceutical development can provide an opportunity to move toward risk mitigation for efficient ER tablet formulation and process development. However, there are some barriers to overcome in the way of adopting QbD concepts. The key issues are the lack of understanding and the gap between industries and regulatory authorities concerning the scientific principles and terms beyond QbD, which can create an obstacle during the approval process. Furthermore, it is generally believed that the resources and time invested in applying QbD tools are not cost-effective during constant and continuous improvement. Today, it is time to realize that a multidisciplinary understanding of the formulation and manufacturing process is as important as achieving the final result.
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KEYWORD
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Design of experiments, Extended release, Machine learning, Multivariate data analysis, Process analytical technology, Quality by design
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