Biosimilars Increase the Need for IT to Reduce Variability

Healthcare legislation is spurring important changes in today’s economy, including an increased demand for follow-on biologics. These products have properties and benefits similar to those of their patented large-molecule counterparts. Manufacturers of follow-on biologics have a new opportunity to earn profits by reproducing biologics for known markets with high demand. Original patent holders, meanwhile, hope to be the best producer of a given product in the business and to hold on to valuable market share in the face of shrinking profit margins.

For both types of manufacturers, process understanding will be more crucial than ever because it will help them compete and seize new opportunities by ensuring predictable product outcomes. Using information technology (IT) tools to enhance process understanding helps reduce variability that can affect speed to market. IT tools can maximize profits by reducing waste and increasing yields. They also can reduce manufacturing risks related to process variability and provide a higher assurance of safety for consumers.

An IT tool, such as a process-intelligence platform, helps to identify and manage risks and puts the focus on quality, thus helping to achieve the abovementioned business benefits for original drugmakers and follow-on manufacturers. For example, easy access to data early during process development can help personnel build a process model to control process variability during scale-up and manufacturing, thus reducing the risks of extended startup times and lost batches.

Process and quality data management is essential and only possible if the right tools are in place to enable collaborative data analysis across manufacturing and quality teams, which might span locations around the globe. When all manufacturing sites can view the data from multiple sites and systems on a common platform, teams can compare and contrast results and share information that helps ensure consistent quality. Reducing technology-transfer risks and enabling comparisons of critical process parameters (CPPs) and critical quality attributes (CQAs) across process-development teams and contract manufacturing organizations takes quality and process understanding to the appropriate level.

Data gathered from the process-design stage and throughout the manufacturing processes can improve understanding of a product and its manufacturing process. A process-intelligence software platform can aggregate, contextualize, and analyze data on demand to make process characterization easier and less error-prone. The platform also can improve process predictability and safety for both original drugs and biosimilars.

For example, a producer of follow-on biologics might use a process-intelligence platform in the following ways to improve manufacturing processes:

• To ensure that CQAs are identified and controlled within the design space as part of a quality-by-design program. This task requires effective collaboration between process-development, manufacturing, and quality-assurance teams that are often spread across several time zones, organizations, or corporations. The process understanding needed to define and obtain regulatory approval for a design space that enables the desired quality outcomes should be developed, implemented, and supported jointly. By better identifying the relationships between CPPs and CQAs that provide effective process control and continuous quality verification, process variability can be significantly reduced, and thus the excess of raw-material and finished-product inventory can be reduced.

• To monitor manufacturing processes at contract manufacturing sites or at a company’s own sites to compare outcomes and determine sources of process variability. An IT tool, such as a process-intelligence platform, provides self-service access to data from multiple sources (e.g., paper records and various manufacturing and quality systems), reduces inefficiencies, and minimizes variability and risks.

Biopharmaceutical manufacturers have always realized business benefits from improved process understanding, but now more than ever, manufacturers need IT tools to reduce and control variability and improve yields. The business pressure for makers of follow-on biologics and original biopharmaceuticals to reduce margins requires both kinds of companies to reduce process variability to improve bottom-line results.

Justin O. Neway, PhD, is executive vice-president, chief science officer, and cofounder of Aegis Analytical, 1380 Forest Park Circle, Suite 200, Lafayette, CO 80026, tel. 303.625.2100, [email protected].