A quality by design approach using artificial intelligence techniques to control the critical quality attributes of ramipril tablets manufactured by wet granulation

Abstract

Quality by design (QbD) is an essential part of the modern approach to pharmaceutical quality. This study was conducted in the framework of a QbD project involving ramipril tablets. Preliminary work included identification of the critical quality attributes (CQAs) and critical process parameters (CPPs) based on the quality target product profiles (QTPPs) using the historical data and risk assessment method failure mode and effect analysis (FMEA). Compendial and in-house specifications were selected as QTPPs for ramipril tablets. CPPs that affected the product and process were used to establish an experimental design. The results thus obtained can be used to facilitate definition of the design space using tools such as design of experiments (DoE), the response surface method (RSM) and artificial neural networks (ANNs). The project was aimed at discovering hidden knowledge associated with the manufacture of ramipril tablets using a range of artificial intelligence-based software, with the intention of establishing a multi-dimensional design space that ensures consistent product quality. At the end of the study, a design space was developed based on the study data and specifications, and a new formulation was optimized. On the basis of this formulation, a new laboratory batch formulation was prepared and tested. It was confirmed that the explored formulation was within the design space.

Keywords::

• Quality by design (QbD)
• Artificial neural networks (ANNs)
• Critical quality attributes (CQAs)
• optimization
• ramipril
• wet granulation