Originally published in BioProcess International March 20, 2017
What is digital manufacturing
The digital revolution in manufacturing began with an explosion in monitoring, analytics, and new computing capabilities. Combined with advances such as artificial intelligence (AI), automation, and robotics, digital is changing manufacturing concepts such as product development, factory operations, and materials supply. This evolution also connects product and process designers and leaders in manufacturing engineering. Digital manufacturing is a reality that will change industry (1, 2).
For many years, manufacturing plants have been generating more and better data. Some companies have started harnessing this information to gain valuable insights that could improve efficiency, productivity, and growth (3). Recent advances in asset performance management owe much to the digital manufacturing revolution (4).
The general public sees this type of development embodied in the self-driving car. Most people understand that these cars do more than generate large amounts of data regarding their surroundings. That information is managed by advanced control systems that can interpret it to distinguish things on the road and set a path forward. Such cars are moving beyond highly automated to becoming autonomous.
Digital manufacturing can improve the productivity and robustness of existing processes and facilities. It also allows efficient development of some previously unmanageable products or processes. Digital manufacturing is a local and online means for continuous optimization of process performance. This functionality is based on information derived from both current operations and previous batches or periods of operation. It relies on the comprehensive, real-time interfacing of both human- and machine-sourced information through one centralized system.
Digital manufacturing provides more than legacy distributed control system (DCS) and supervisory control and data acquisition (SCADA) provide. Digital biomanufacturing provides an integral connection and real-time access to divergent information sources.
Thus, it can enable deep analysis and predictions leading to advanced process control. Such comprehensive analyses extend beyond operation-performance data from a production floor. Analyses extend to data that drive activities such as raw materials security of supply and business continuity management systems (5).
GE’s Predix system is an example of how manufacturers can use continuous data acquisition, cloud technology, and advanced analytics to provide a platform for the industrial internet. The software feeds data from new sensors and other high-value sources such as process history records into advanced process-control algorithms. From those, manufacturers can gain actionable intelligence. They can also gain transformative insights and improved process control (6).
The availability of modern tools is providing deep insights into manufacturing processes. These tools include ultrafast digital processors, inexpensive and flexible data storage, advanced analytics and control algorithms, and even AI. Now companies can know specifically what is happening based on many measured parameters in every part of an operation. They can also get this knowledge in real time. Powerful computers then can predict and control the critical operational parameters of those processes. Small-molecule drug manufacturers already use such understandings to invent end-to-end processes for continuous manufacturing.
Fig 1. Digital manufacturing schematic, showing data inputs, industrial data lake, and outputs to stakeholders.
What is digital biotechnology
The ‘digital’ concept is applied in many areas of biotechnology. Each area involves millions of discrete values (digital). And most use living cells or their immediate parts (biologics). However, biotech companies are approaching digital biotechnology from several angles. It is becoming important to understand and distinguish a few related terms and digital approaches. For example, DNA digital storage is distinct from digital gene circuits.
Digital biomanufacturing for upstream and downstream processes
Digital biomanufacturing can be viewed as a potentially larger embodiment of digital biotechnology. Similar to digital manufacturing, digital biomanufacturing promotes improvements in the biological manufacturing by using computer-aided design, manufacture, and verification (7). However, the living components (cells) involved in bioprocesses impart a distinctly different character to the systems involved here. Because it addresses unique aspects of biological activities, a specific term was coined: digital biomanufacturing.
Digital biomanufacturing is undergoing most of the same advances as in digital manufacturing. These advances include increased monitoring, data collection and handling, connectivity, computer power, process control algorithms, and automation. However, upstream and downstream processing must address the unique requirements of living cells and their products. Upstream, that includes dynamic control of a changing ambient environment, a cell culture’s metabolic state, and total mass. Downstream, it can include assessment of lot-variant harvest compositions and consequent adjustment of process inputs for critical parameter control. Also, downstream processes must allow for variability in the amount, state, and quality of intermediate products.
Digital biomanufacturing is part of an evolution. It is a further step in the development of the industrial internet of things (IIoT), which connects disparate sources of data and uses advanced analytics to turn them into actionable insights. IIoT refers to instruments becoming interconnected. But more than that, it denotes high levels of data analysis, information management, and process control implemented into a ‘process network.'
Digital biomanufacturing promises real-time optimization of manufacturing processes, based on highly valuable criteria such as projected product quality and batch profitability. Digital biomanufacturing software increases asset reliability and availability while reducing maintenance requirements. New data-gathering and interfacing techniques can be achieved with advanced computer hardware. Adding in object linking and embedding for process control and cloud capabilities, digital biomanufacturing is producing a qualitative change in the ability to understand an entire biomanufacturing process. Digital biomanufacturing promotes collaboration and knowledge management across an entire organization. It also allows optimization and continuity of operations by providing real-time access to critical information through high-demand manipulation and analysis of rich, timely data.
Technologies that support digital manufacturing of biologics
Increased power in information handling and processing began with dramatic increases in microprocessor speed, pipelining, and parallelism over the past couple of decades (8). The trend continues with advances in data-handling software and device interfaces (9). The phenomenon of ‘big data’ refers to information sets that are so large and complex that traditional data-processing methods are inadequate. Advanced methods of extracting value from huge data sets is a key development that enables digital manufacturing of biologics. The resulting power promises to vastly improve operational efficiencies, reduce costs, and lower risk in bioprocessing.
A key to the future in advanced bioprocessing control is cloud computing. Internet-based systems provide shared computer processing power and data on demand. The cloud can allow on-demand access to a shared pool of many configurable computing resources. Also, it can provide access to resources that are geographically located far from users.
Lean product and process development (PPD)
Lean product and process development is gaining influence in developing bioprocess understanding. Practical advances such as solid-state memory and cloud computing enable this progress. Heightened bioprocess knowledge and establishment of critical control parameters synergize with statistics-based analysis and design approaches in product and process development. The emerging field of analytical quality by design supports lean product and process development. This support includes advanced process engineering techniques, new analytical equipment, testing capabilities, and science-based approaches (10, 11).
Increased availability of real-time data is feeding improved closed-loop control systems that can alter critical control parameters robustly. The digital revolution in biomanufacturing began with improvements in process monitoring capabilities. That generated large amounts of data relating to more aspects of bioprocessing. Innovations driven by process analytical technology (PAT) include several light-based probes and automated, cell-free sampling devices.
Progress in biochemistry, photonics, and information technology has promoted powerful chemical optical sensors for bioprocess monitoring. Even single-use sensors are emerging in designs that enable in situ, real-time monitoring of important culture parameters without sampling. These advances are based on several physical properties and constants of recently engineered materials and the biological components that they measure. New advantages include miniaturization, flexibility, embedded sensing, rapid at-line sensing, multisensing, and continuous quantitative or qualitative measurements. From very small sensor spots to robust dipping probes, chemical optical sensors can monitor a number of culture parameters online throughout culture duration
The availability of single-use, cell-free multiplex sampling technologies is an enabling factor in generating near–real-time process information. Previously, many culture-monitoring and potentially controllable process attributes often were ignored because near–real-time digital data were unavailable. But that is changing. For example, Cytiva’s Biacore surface plasmon resonance (SPR) system can characterize monoclonal antibodies in terms of their concentration, specificity, kinetics, and affinity. Other new systems can provide near–real-time concentration measurements of bioprocess components using, for example, a calibration-free concentration analysis method. Concentrations now can be measured in the nanomolar range (12).
Advanced algorithms, increased processing speeds, and the large amounts of process data available are changing both the power and scope of process control systems. Adaptive ‘fuzzy expert’ systems use AI to provide powerful and robust models that predict operational control. This is especially important in bioreactor control, because cells change their ambient culture media and respond dynamically to the resulting changes. That foundation supports brilliant software that implements new mathematical, statistical, and logical algorithms.
For example, Cytiva’s UNICORN system control software provides built-in knowledge for planning and controlling process runs. This is accomplished by analyzing results from several bioproduction operations. Such software is suitable for use in regulated environments, because it provides traceability and back-up functionality as called for in 21 CFR Part 11, for example. Beyond that, distributed control elements and supervisory systems are allowing companies to consider exciting innovations such as end-to-end continuous biomanufacturing.
Enterprise control systems now provide coordination of every aspect of a business. As computer systems and AI become more powerful, enterprise control is becoming more automated. An international control has been published for this: the ISA-95 standard from the International Society of Automation (13. Table 1 outlines the levels of activity in an entire biomanufacturing process. Enterprise resource planning (ERP) often describes a suite of integrated applications that biomanufacturers can use to collect, store, manage, and interpret data from many sources. These sources include production planning, purchasing, manufacturing, marketing, sales, inventory management, shipping, and finance. ERP provides an integrated view and control of those business processes — often in real-time — using a common database.
Table 1. Levels of biomanufacturing activities in digital manufacturing of biopharmaceuticals
|0: The process||What is actually happening on the bioproduction floor|
|1: Responsive devices||Sensing and altering operational processes, sensors, analyzers, and actuators|
|2: Process control systems||Supervising, monitoring, and controlling biomanufacturing operations; from simple device PLC elements to supervisory and SCADA software|
|3: Manufacturing operations systems||Managing bioproduction work flows, operations and laboratory management, plant performance management|
|4: Managing the entire business||Basically, digital biomanufacturing-enabled ERP systems looking at all the activities of the business, including scheduling, material use, manufacturing, shipping, and sales|
Benefits of improved bioprocess understanding, development, and control
New and optimized manufacturing technologies such as continuous biomanufacturing demand the interfacing of many sources of information, deep data analysis, and model-based predictions of digital biomanufacturing. Older platforms are also greatly improved by these interfaces. Four essential benefits result from increased bioprocess understanding, development, and control:
1. Personnel are relieved of many manual and repetitive tasks.
2. Strategic planning and operational efficiency are improved.
3. Real-time optimization of end-to-end manufacturing is based on high-value criteria such as projected product quality and batch profitability.
4. Digital biomanufacturing can enable previously unmanageable operations and processes.
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