Emerging Technologies Required for Pharma 4.0
Complete the form below to unlock access to ALL audio articles.
Over the past 20 years, many factors, including globalization, supplier specialization and reimbursement pressures to reduce cost, have increased the complexity of the drug-making process. Pharma 4.0TM is a framework for adapting digital strategies to the unique contexts of pharmaceutical manufacturing, especially using more analytical information to improve productivity and product quality. The term Pharma 4.0 was coined by the International Society for Pharmaceutical Engineers to represent an industrial transition in which “digitalization and automation meet very complex product portfolios and life-cycles.”
As regulatory agencies mandate improved traceability of data and enhanced quality oversight of the complex processes and supplier networks within the pharmaceutical industry, this creates pressures that manufacturers must address in addition to continuing to accelerate their therapeutic pipelines. Doing so will require new or more advanced technologies that can fulfill unmet analytical needs, ranging from new instrumentation, better automation and sophisticated informatics, such as artificial intelligence (AI) that can be used to displace current data analysis and interpretation approaches. Consequently, Pharma 4.0 is expected to completely change pharmaceutical manufacturing – making it faster, more efficient, and ultimately, providing safe, more accessible therapeutics.
To achieve all of Pharma 4.0’s benefits, the industry must incorporate three key elements: digitally-enabled laboratories, automation and distributed quality control (QC). Evolving from today’s analytical platforms, these advances will provide sophisticated methods of measuring, including the future use of sensors for in-line testing and valuable capabilities, such as real-time quality tracking and streamlined QC and in-line release protocols.
Most of these steps also require higher quality data in greater volumes – from research and development and manufacturing lines – plus improved analysis and informatics. This information will allow pharmaceutical companies to better understand the science that underlies every step in developing and manufacturing a drug. Only then can the processes be better controlled and improved.
In this article, we discuss the evolution from today’s pharmaceutical manufacturing to the future methods of Pharma 4.0. In particular, we explore some of the technologies and techniques that can smooth that transition and briefly review several ongoing examples of industrial innovation.
The trends in technological advances
On the way to Pharma 4.0, suppliers will create new technologies and continue to make improvements to today’s platforms. In current reaction monitoring during manufacturing, for example, scientists often use various analytical techniques. These include gas or liquid chromatography (GC and LC, respectively) as well as ion chromatography. Increasingly mass spectrometry (MS)-based workflows provide some of the reaction monitoring, because multi-step analysis can be streamlined to a single test reducing potential human error while improving the confidence in results. In the near future, even more pharmaceutical manufacturers are expected to use high-resolution accurate MS-based methods like multi-attribute monitoring (MAM). Tomorrow’s advanced sensors will replace some of the MS-based monitoring, which will simplify testing and improve data confidence and operational efficiency. Many of the required sensors, however, still need to be developed.
As we see shifts toward performing analytical measurements in-line with bioprocessing, the approaches to monitoring must also change. Moving to these methods, though, depends on overcoming some key technological challenges. Perhaps most notably, in-line methods could contaminate therapeutics where the monitoring technology connects with the production line. Any contaminants could endanger patients. To obviate that risk, the analytical devices need to work safely within the manufacturing line. In addition, the analytical approach must meet regulatory approval. For overall improvements in efficiency, manufacturers will increase their use of automation.
Almost all technology in Pharma 4.0 is likely to be smaller, faster and smarter; focused on improved tracking during manufacturing. This applies to a wide range of devices. For example, portable analyzers could be used in the laboratory and the manufacturing line, which would be especially useful in developing new methods – where scientists could make the same measurements on research and production scales. In other cases, a dedicated analytical device could be integrated into specific manufacturing processes for ongoing in-line measurements.
Working with small and large molecules, plus biosimilars
Current pharmaceuticals come in three general types: small molecules, which have a low molecular weight; large biological molecules, which are typically composed of more than 1,000 amino acids; and biosimilars, which mimic off-patent large biological molecules. Each of these categories includes unique challenges, as indicated below.
Scientists use a collection of analytical technologies in manufacturing small-molecule medicines. For example, manufacturers can analyze raw materials with hand-held Raman spectrometers and use near-infrared (NIR) spectroscopy – and sometimes Raman – for in-line monitoring. If a production line includes granulation, Raman could be used to monitor particle size and water content. In the future, the incorporation of data from sensors in-process models promises to control the manufacturing of a small-molecule drug.
With large molecules, scientists desire more in-line monitoring, which provides one example of distributed QC. If something is going wrong in a batch, a manufacturer wants to know as soon as possible to stop the process, and limit the loss of time and resources. In many cases, LC/MS can provide this QC tracking. Although modern MS platforms are much smaller than the room-size space they took up 30 years ago, there is demand from drug manufacturers to further reduce the size of these systems so they can place the analytical device close to a process, even where space is limited.
In making biosimilars, high-throughput analysis is needed to measure many attributes, which are required for comparison with the original biologic. Such analysis can be most helpful early in a drug’s lifecycle, where in-line monitoring can be applied to many attributes during development to improve the intended manufacturing process as much as possible before scaling up to commercial levels.
Combining analytics and informatics
For Pharma 4.0, all areas of the pharmaceutical industry must make better use of data to improve the efficiency and effectiveness of drug development and manufacturing. In some cases, especially in development labs, scientists need more advanced analysis, which could include AI. However, most AI- or machine learning (ML)-based tools expect large datasets. Although pharmaceutical manufacturing produces sizeable datasets, they are not usually large enough or in the necessary format to meet the expectations of most current AI or ML algorithms. So, to overcome this, some forms are now being developed to work with the datasets available in pharmaceutical manufacturing.
While pharmaceutical companies keep collecting more data, they want to use it more thoughtfully. In the past, companies took a brute-force approach of trying to analyze every piece of data. Today, some companies want to reduce the amount of data that is analyzed and simplify the number of dimensions in some applications, such as during manufacturing. Simplifying the approach to the analysis allows everyday users – not just experts and data scientists – to make use of the analytical tools.
Reaching such levels of improvement in data analysis will depend on manufacturers working closely with biocomputing experts to find the best digital solutions for collecting and managing information. For instance, a laboratory information management system (LIMS) stores data so that it is traceable for better data integrity, and that provides higher quality data for analytics. Scientists can also add data efficiency by replacing paper with an electronic lab notebook (ELN), which can be integrated with a LIMS.
By collaborating with analytical and instrumentation experts, drug manufacturers might develop more efficient testing methods during manufacturing, while still meeting the standards set by regulators. In one example, some companies are pursuing such advances in dissolution (release) testing, which determines the time required for a pill to go into a solution. Rather than performing off-line testing, some companies hope to combine NIR technology and modeling to develop in-line dissolution testing.
Many manufacturers foresee an increasing integration of analytics in processes. The collected information could be used from R&D through manufacturing, which provides the data continuity envisioned for Pharma 4.0.
Pharma 4.0 – The future
As discussed, Pharma 4.0 depends on a range of existing and new technologies that will improve the manufacturing of small and large molecules, as well as biosimilars. Today’s existing technology already creates a foundation for these advances. Going even further will require collaborations between suppliers, academics, industry and regulatory agencies.
Similar collaborations will also be required to ensure the successful and optimal integration of digital systems with physical instruments. With that integration, a biotechnology or pharmaceutical company will gain advantages in producing high-quality products and doing so with improved reproducibility, increased speed and, perhaps, reduced cost.
In Pharma 4.0, manufacturers will have access to better information about drug-making processes and use that knowledge to accelerate the development of new methods, but that depends on teamwork. The power of integrated partnerships can focus a pharmaceutical manufacturer on the most pressing goals – improving processes, limiting interruptions, balancing quality and operational costs, meeting business needs and maintaining a reputation for quality and dependability.
About the author
Eric Grumbach is Director of Pharma/Biopharma, Chromatography and Mass Spectrometry at Thermo Fisher Scientific.