Today, people worldwide have greater access to a broader range of pharmaceutical and biological treatments than ever before. This expanded access has created an expectation —patients anticipate safe and timely treatments when and where they need them. As a result, life sciences organizations face growing pressures to deliver new, innovative treatments to meet market demand faster. While this shift has created many new opportunities, it also introduces a new array of challenges across the development pipeline.
Today, there are more emerging therapies in development that are patient centric than traditional blockbuster therapies. Manufacturers need to find ways to introduce both types rapidly, and to switch to different therapies quickly. Nimble life sciences organizations are striving for fast changeover of products and seamless orchestration of equipment, people, and materials to develop commercialization strategies to meet market demands. Moreover, increasing flexibility also drives a need for a more integrated control strategy, parameterized product recipe definitions, and faster implementation — all delivered with the same high quality at a lower cost.
In addition to meeting these increasing demands, plants must operate efficiently and effectively. Organizations are simultaneously focused on operational integrity to deliver products on time by both adjusting schedules to accommodate unplanned issues and predicting failures. They must also plan for events or prevent them before they turn into process deviations or manufacturing losses.
If teams can overcome all these challenges, they can continue working toward the two of the most complex — but also most rewarding — challenges: real-time release and sustainable operation. As teams implement in-line quality monitoring and control measurements of the process and quality attributes, workflows that prevent deviations, and automatic triggering of exceptions with an immediate path to capture event details for resolution, they can limit the time spent waiting on batch record reviews, deviation resolutions, and testing results. And those same gains provide operational insights that drive the changes necessary to reduce waste and optimize energy usage, helping companies to achieve global environmental, social, and governance goals.
While there are many key strategies to optimizing operations, increasing speed-to-market, and capturing competitive advantage, data management is a critical foundational element to implementing any of these strategies. Today, life sciences organizations are including a boundless automation vision as part of their data management foundation. The goal is not just access to data, but unlocking “data as a product” through seamless mobility of contextualized information — including both the individual phases of treatment development and manufacturing — while covering the entire development pipeline and manufacturing value chain.
Ultimately, with effective data management grounded in data accessibility, contextualization, and traceability sourced from a boundless automation vision, life sciences teams can unlock faster, easier technology transfer and operational sustainability. Meeting those goals will help get treatments into the hands of patients around the globe as quickly as possible.
From process development to clinical manufacturing
In the process development and clinical manufacturing phases of the treatment development pipeline, teams explore the most effective ways to manufacture a new product consistently. The extensive design adjustments occurring in these phases inherently generate many process changes, and, by extension, much data.
Throughout the many experiments they perform, team members need to understand what version of the process is current at any given time. They need clear visibility into the order of unit operations, as well as the parameters for a given experiment. That is a lot of data to track by itself, but the issue is compounded by the fact that data is not used in isolation.
As they see results and gain more experience with the process, the team will continue to refine the process and quality parameters, update the risk assessment around how specific process controls might impact quality attributes, and develop appropriate control strategies to mitigate risk. Ultimately, the team needs to control a wide array of data-intensive elements to ensure they will consistently produce high quality, effective, and consistent product.
Accomplishing these goals not only requires a lot of data from a wide variety of sources, it also requires the team to bring that data together quickly and effectively for analysis. If data is stored in many different formats across many different systems and recording tools, team members will have to spend time gathering the data, translating it among different formats, and managing error checking and reporting. Performing such tasks manually means accepting significant delays and increased chance of errors.
Fortunately, automation tools intentionally designed around seamless data mobility can dramatically reduce manual work and help manage data integrity and context across the entirety of process development and clinical manufacturing, and the transfer to commercial manufacturing. Process knowledge management (PKM) and workflow management tools help standardize processes and boost flexibility. The most advanced PKM solutions provide the functionality to seamlessly manage both product and process specifications throughout the drug development lifecycle, while workflow management tools provide a lightweight and flexible solution to enforce workflows and ensure appropriate operations.
Using PKM software, development teams gain access to centralized recipe translation and management to create a single source of truth for fast, accurate information sharing. PKM software facilitates the standardization and communication of product definition, specification structures, and process terminology throughout the product lifecycle. Digital workflow management solutions provide drag-and-drop interfaces for building new workflows quickly, making it easy for authors of any skill level to assign critical tasks and enforce their execution.
Applying digital transformation solutions also helps teams improve collaboration. Data in PKM software can be securely viewed from anywhere via a web-based interface. Users can simultaneously access recipes, tag annotations, reference documents or other data sources, and use an embedded check in or check out process for digital approvals.
In addition, organizations will need a way to store all the related process and manufacturing data in a federated data fabric accessible by any authorized user and application. To accomplish this, many teams are implementing plans to extend enterprise service buses and centralized data lakes to an integrated industrial and business data fabric. The fabric seamlessly connects diverse systems to collect and store large volumes of data to make it accessible for analysis and reporting locally and across the enterprise—leveraging findability, accessibility, interoperability, and reuse of digital assets. Together, PKM software and an industrial data fabric reduce time spent on low-value tasks and dramatically shorten the technology transfer process.
Facility design and testing
As organizations transition to commercial manufacturing, they will need to consider where products will be produced in the supply chain. Whether the organization plans to build an entirely new facility for manufacturing, determine which existing site in their network will have the capacity to meet demand, or leverage an outsourced contract manufacturer, data management remains critical.
For greenfield manufacturing facilities, many organizations will choose to perform design simulation to ensure the site is built to the proper specifications. The team can use simulation tools to ensure their design meets capacity requirements and to identify where bottlenecks might occur, all before construction begins. Additionally, simulation tools are frequently used to help train operators in advance of facility startup to ensure they are ready to function at their best from the very first moments of operation.
However, the benefits of discrete simulation extend to existing facilities. Using simulation software with “automated model creation,” the organization can see the realistic capacity of any facility for a particular product. The most advanced solutions can identify whether a site meets capacity requirements, and they can provide the ability to run various scenarios to identify and eliminate bottlenecks.
To supply the vast amounts of data necessary for efficient and accurate simulation, teams are connecting their simulation software to data fabric solutions. Leveraging the pre-contextualized data in a data fabric, teams can quickly and easily build the wide variety of models necessary to take full advantage of simulation software. The more data and context the team has, the more accurate the results will be.
In addition, for existing licensor or outsourced contract facilities, organizations typically need to perform facility fit procedures to determine where to best manufacture a given product at a given scale. In such a scenario, PKM software can be particularly helpful. Advanced PKM solutions include facility fit tools that use the data gathered in previous development phases to identify whether a given site has the right quantity and type of equipment with the appropriate capacity to meet production goals. The best tools can get very specific — identifying specific needs or shortcomings, such as having the right number of chromatography columns or a requirement for a vessel with a jacket — alerting users when manufacturing configurations do not meet their needs.
Commercial manufacturing
The need for fast, flexible access to data increases further when an organization pivots to large-scale manufacturing. On the shop floor, a manufacturing team must now consider data from the process control system, manufacturing execution system, quality system, and even reliability and process data at the edge. Moreover, they may need to serve up that data, with context, to many different applications and personnel so they can make critical decisions in real time.
Ultimately, the production and quality teams need the right data to confirm and prove that batches are manufactured per the licensed process to confirm the product meets the quality attributes. All these manufacturing and release activities must be right-the-first-time, every time. That means monitoring the entire process, including calibration and condition of all the devices monitoring the process. Teams need to include device management software to bridge the data context from the instrumentation to the control system. A device management solution runs alongside the distributed control system (DCS) to help operators calibrate assets and document the status and health of their devices.
Device management provides an automatic audit trail of who changed a device, how it was changed, and when and how devices were calibrated. Not only does this provide records of changes for validation and auditing purposes, it also provides alerts when a change happens so the team can react quickly to make sure everything is still running correctly so they do not lose a batch or interrupt a continuous operation.
In addition to monitoring devices, teams need access to high quality contextualized data around the balance of plant assets supporting the entire process. Equipment such as cooling towers, HVAC systems, compressors, pumps, and more are often integral to the process — so when they fail the result will likely impact manufacturing.
To manage the critical health data of rotating machinery, modern facilities rely upon wireless vibration monitors and edge analytics solutions that feed data seamlessly into the DCS and data fabric. Seamless access to asset health data empowers teams to keep a finger on the pulse of their overall plant health so they can identify issues and intervene well before they turn into failures that could impact quality. Moreover, by leveraging the data fabric for all this connected data, teams can more easily leverage more complex, AI-based analytics systems to provide the instantaneous feedback and decision support necessary to support more autonomous operation.
Better data drives competitive edge
As life sciences companies strive to improve pipeline acceleration, flexible manufacturing, operational integrity, real-time release, and sustainability, they are finding themselves more reliant on data than ever. Fortunately, many of the most effective modern automation solutions are built with seamless data mobility in mind, helping teams drive data to the right place at the right time for appropriate action, without losing the critical context that helps drive decision making from the manufacturing edge to the business enterprise.
Building a foundation of seamless data mobility today will pay dividends in coming years as it increases the organization’s flexibility and speed to market, not only enabling competitive advantage that is essential in an increasingly complex global marketplace, but also providing life changing therapies sooner to more patients around the world.
