As the life-sciences industry amasses ever-increasing volumes of manufacturing data, the need grows for better data management, especially among smaller biopharmaceutical manufacturers. Like their larger colleagues, small companies can strategically leverage data with the transformational power of process intelligence—defined as the technology and systems needed to design, commercialize, and sustain robust manufacturing processes that give predictable, high-quality outcomes cost-effectively, based on scientific process understanding.
Current approaches to data management
Traditional approaches to data management attempt to provide process intelligence, but are not efficient or effective. Proceeding "the way we've always done it" is no longer enough due to three significant industry trends working against life-sciences manufacturers today.First, across the range of small and large companies, most new processes are operated in existing captive or contract manufacturing facilities. Gone are the days when companies could raise capital to build a plant to produce only one product. Today's available manufacturing capacity means products typically have to perform within the constraints of existing facilities, so new processes need to be developed from the design stage on to fit the available facilities.
In the second trend, the best resources available to address problems that arise in commercial process scale-up and manufacturing, or to identify improvements in those processes, are often the experts who developed new manufacturing processes in the first place. The ideal team to get process variability under control quickly would include those who designed the process and best understand its components. These teams need easy access to all the data from the early process design stages through commercial operations and a collaborative environment in which to work with it.
A related and final trend points to changes in the knowledge base. The people with the deepest process knowledge are usually concentrated at a few sites, rather than at every site. Today's process experts have responsibility for multiple sites and face challenges gathering reliable data from remote sites to execute on their area of expertise (e.g., investigative analysis, predictive modeling, or batch/campaign comparisons).
Whether we refer to boundaries in the geographical sense or to the "organizational boundaries" that exist around departments and manufacturing sites, we need better methods of crossing "borders" to improve collaboration and technology transfer and to reduce associated risks that threaten product batches, consumer confidence, and ultimately company reputations. These very real trends are shaping the business landscape today and will continue, so manufacturers need to adjust accordingly by incorporating better process-intelligence approaches and tools across their organizations.
Along with trends driving process-intelligence challenges and increased outsourcing, regulatory guidance is encouraging new approaches to achieve the desired state of process understanding and validation. FDA defines process validation as the collection and evaluation of data, from the process design stage throughout production, which establishes scientific evidence that a process is capable of consistently delivering quality products (1). FDA's focus in Stage 3 of its validation guideline states that, "Ongoing assurance is gained during routine production that the process remains in a state of control" (1).
An organization's size typically corresponds to the maturity of its data infrastructure that supports process control and validation. Smaller companies have fewer of the legacy data systems (e.g., LIMS, enterprise resource planning) typically found at larger manufacturers, and they tend to rely more on paper-based records and spreadsheets for data capture and reporting. Challenges and risks result when accessing, aggregating, and analyzing data for FDA approvals, annual product reviews (APRs), campaign and batch reports, investigations, and proactive analysis.
As financial models evolve in the life sciences, there is an increased number of new "small companies" funded by investors (or sometimes by larger pharmaceutical companies) to identify and evaluate new products quickly. Smaller companies can work efficiently by outsourcing drug development, clinical trials, and early-stage manufacturing, but typically they use traditional data-management methods that involve mountains of paper and spreadsheets. With manufacturing data dispersed across contractors and geographies, these smaller firms often struggle with technology transfer and controlling process variability.
New companies have the opportunity to overcome such challenges from the start by using newer process-intelligence-based approaches. These systems can add efficiencies, reduce risks, and deliver better results (i.e., commercialized products and profits) to investors and healthcare consumers.