Automated Data Processing and Analysis for Quality Monitoring of Biotherapeutics by Multi-Attribute Method (MAM)

摘要

Biopharmaceutical firms adopt complex and costly process monitoring strategies and quality systems to ensure final product quality. Critical quality attributes (CQAs) are currently monitored using an array of analytical techniques. Although routinely used as release tests, these techniques generally do not measure attributes at the molecular level. In this context, many industrial players are exploring the adoption of innovative analytical approaches employing mass spectrometry (MS) to enable direct measurement of CQAs at the molecular level. In addition, MS-based methodologies offer the benefit of measuring many different quality attributes on a given biotherapeutic with a single test. The multi-attribute method (MAM) can potentially reduce development and manufacturing costs and at the same time increase product quality. We present an implementation of MAM using a single software platform for the data processing, analysis, and management of MS data. In this approach, dedicated workflows were tailored to measure the CQAs for a given biomolecule, while testing for impurities (new peak detection), as well as checking the instrument qualification (system suitability). Optimized data processing was applied to large data sets and execution times scaled linearly with the number of samples. Browsing and downstream data analyses, including statistical tests, visual verification of the results, and generation of customized reports, were performed. This approach can be fully automated and employed as part of a bioprocess control strategy. In this case, we show as an example the real-time monitoring of quality attributes of the materials produced in a bioreactor. A compliance module including GxP functionalities such as audit trails, electronic signatures and data security allows the deployment of this MAM implementation in regulated environments.