Abstract

Physical or chemical properties of excipients and active pharmaceutical ingredients play a significant role in pharmaceutical process robustness and product quality, which are foundation elements of the Process Analytical Technology initiative in the pharmaceutical industry. This paper describes the investigation of the use of continuous data output from three different near infrared qualification algorithms (correlation, absolute normalised spectral distance and principal component analysis-based Mahalanobis Distance) coupled with statistical processing to create a material quality conformance approach. This approach extends the application of near infrared spectroscopy material qualification beyond pass/fail classification, to a tool for in-depth process understanding. The three material quality conformance methods were applied in parallel to commercial deliveries, and the benefits and limitations of the different methods compared. Global material quality conformance methods were demonstrated to provide an opportunity to gain greater insight in to the design space of product formulations and processes, and enabled rapid identification of material variation with the potential to greatly impact the pharmaceutical manufacturing process and/or product quality.

© 2014 IM Publications LLP

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