Cambridge Healthtech Institute’s Kent Simmons recently spoke with Martin Mayer, a Director with Austria’s evon GmbH, Austria, about his upcoming presentation “Data Management in Process Development as a Key to Process Understanding and Development Efficiency”, to be delivered in the Process Characterization and Control meeting at the 2017 Bioprocessing Summit. The Summit is scheduled for August 21-25 at the Westin Copley Place hotel in Boston, with the process charactertization program set for August 24-25.
How is data management supporting improved efficiency in biopharmaceutical development?
Recent studies from PhRMA state that US biopharmaceutical companies spend almost 14 times the amount in R&D per employee than any other manufacturing industries. Since drug efficiency and patient safety are the primary goals, a complex development and testing phase is needed. But although all the money is spent, just 12% of all drug candidates of Phase 1 make it to an FDA approval. Data management, modeling and analytics, and Quality by Design approaches significantly improve the efficiency of development teams and lead to knowledge-based decisions which can reduce the drop out rate in clinical testing significantly.
How are regulatory agencies aligned with this approach?
In 2004, the FDA published the PAT Guidance for PAT — A Framework for Innovative Pharmaceutical Manufacturing and Quality Assurance. This framework defines both methods and tools to support a knowledge driven, or Quality by Design approach. Modern analytical tools such as NIR or RAMAN are named, the need to introduce data management and data analytics (multivariate data analyses MVDA) are highlighted and the use of models (= analyses results) for process development and closed loop control (advanced process control) are strongly recommended. In addition to that, the harmonization documents ICH 8-10 cover all the regulatory needs to introduce more flexible and knowledge based development and manufacturing processes.
One of today’s hottest topics is data integrity. What does that mean with regards to data management in the biopharmaceutical development environment?
In the R&D environment, a significant amount of data is generated and this needs to be managed. This data is the basis for decision making and successful drug development. From that point of view, the integrity of the data is essential. In addition, reliable data for clinical trials is required by the authorities. Therefore the data management has to contribute or guaranty data integrity. This does not only include automation and IT Systems but also the training and awareness of the people.
We hear other buzzwords like Industrial Internet of Things, Big Data and Predictive Analytics. How do you see these topics impacting biopharmaceutical development?
Modern PAT Analyzers like Fluorescence or RAMAN deliver a huge volume of data within very short measurement periods. These can generate thousands of variables, including their value, per second! This vast amount of data has to be analyzed and “filtered” to obtain the relevant information needed to answer concretely asked questions or tackle development issues. For this task, data analytics tools are used, including Multi Variate Data Analyses (MVDA) as well as machine learning algorithms. Some names are changing, but in general the toolset is the same.
It is now said that digitalization is is the next new thing and that data is the new gold! Is that really true?
The comparison with the goldrush is somewhat in accurate, as it cofuses data and information. Rather the information is the gold, the data is the gold claim or the goldmine. Without having the mine you are not able to find gold. But just owning the mine does not mean that you have got gold in your hands! But for sure, digitalization will have an hugh impact also on the pharma industry.
And finally, what are you most looking forward to at this year’s Bioprocessing Summit?
The Bioprocessing Summit is one of the most attractive conferences for catching up with latest trends, getting new ideas and inspirations and finally meeting people from academia and industry. The broad program from presentations to seminars and short courses offers a great variety of knowledge that is presented in different ways and formats.
Martin Mayer, Director, evon GmbH, Austria
Head of business development at evon GmbH, Austria. Over 15 years background in data driven process analyses and model based closed loop control in industry. Member of steering committee of ISPE Workgroup for “IT Automation System Structure & Integration Standardization”. evon realized a number of integrated data management systems for research and development facilities – academic as well as industrial environment - to enable efficient data driven process analyses, softsensor implementations and model predictive control applications.