Why Bioprocessing 4.0 Is A Force To Be Reckoned With
Contributed Commentary by Michele Wilson
October 15, 2019 | Whether you're ready or not, the fourth industrial revolution is on its way. Not just a funky new buzzword, "Industry 4.0" refers to a new approach to manufacturing that we can't afford to ignore – bioprocessing engineers included.
To give bioprocessing 4.0 some context, we need to consider a trend that is shaking up manufacturing across different sectors: Industry 4.0.
Industry 4.0 is an umbrella term which refers to a new approach to manufacturing. Preceded by the first (think steam engines, mechanization and the first factories), second (electricity, gas, and oil) and third (electronics, computers, the internet) industrial revolutions which enabled productivity leaps, this one is different. It's digital, connected, integrated.
While there's no hard and fast definition, these are the general features of industry 4.0:
- Physical processes affect computations, and vice versa. Through feedback loops known as cyber-physical systems, control components in a manufacturing process are controlled by computer-based algorithms.
- Connection via the Internet of Things (IoT). Computing devices and machines are connected to each other via the internet. Through inbuilt sensors, data from interrelated devices are fed into a cloud platform, allowing algorithm-driven analysis and optimization.
- Smart factories. Factories run in an autonomous manner, self-optimize and learn in real-time.
Powered by artificial intelligence, advanced robotics and sensor technology, the manufacture and delivery of products are adapted to real-time shifts in demand and transport availability.
The original term was coined by the German government in 2011, which announced "Industrie 4.0" as one of the key initiatives in its high-tech strategy to drive manufacturing forward. When these concepts are applied to different industries, loosely defined variations arise, such as "pharma 4.0", "bioprocessing 4.0", or "digital bioprocessing".
Bioprocess Engineers Set To Benefit From The Evolving Industry
So, what does this mean for bioprocessing engineers? How could it shake up the way they work? The potential benefits are enormous. For example, connected devices and automated collation, organization, and analysis of data structures would enable greater data integrity and faster processing of time-critical information. Furthermore, adaptive control of unit operations using real-time data processing opens the door to much more efficient bioprocess optimization, potentially removing whole experimental iterations in the development of a robust, commercially viable bioprocess.
Automated data processing combined with physical process execution has the potential to boost many bioprocesses. In protein production, the number of possible parameter combinations grows exponentially with each parameter, of which there are many: best expression construct, secretion signal peptide, inductor concentration, induction time, temperature and substrate feed rate in fed-batch operation. Using standard microtiter plate cultivation, the parameter selection process will often be based on empirical knowledge, and only a few process modifications can be tested. In contrast, the time to finding optimal bioprocess parameters can be dramatically accelerated by feeding robotics data that has been rapidly and automatically analyzed.
In a more recent example, the value of automation has been demonstrated in cell culture. The generation of retinal pigment epithelium (RPE) is an attractive therapy for treating age-related macular degeneration. Recognizing that the differentiation of human pluripotent stem cells (hPSCs) remains a laborious, expensive, and long process, Regent et al. describe in Scientific Reports a fully automated RPE cell differentiation process, from hPSCs thawing to the banking of differentiated cells (https://doi.org/10.1038/s41598-019-47123-6). Following their protocol, it is theoretically possible to produce a cell bank far larger than has previously been described.
Overall, a shift toward a more automated, data-driven system would allow engineers to spend more time on innovative aspects of their role, and less time navigating tedious re-runs and breakdowns. The same applies for bench scientists; few would complain about spending less time preparing plates, re-doing assays, and drowning in masses of data.
How Automation Can Help Biopharma
Today, biopharmaceutical companies are under pressure to get products to market quicker, with every day of delay projected to cost approximately $1-13M in lost revenue for a best-in-class product in a major indication. Likewise, in some rare diseases, it is highly possible to treat an entire latent patient population in the trial, leading to a natural acceleration among companies chasing these indications. As such, many companies with innovative products are choosing to compress their clinical trials through an accelerated approvals route, essentially weighing up patient benefit versus the risk of less Chemistry, Manufacturing and Control (CMC) data. This means that innovative products are being launched into the market, often without extensive CMC studies, as would be the case in a standard clinical development pathway. Within the cell and gene therapy industry, challenges around process and assay robustness, and the increased stringency of the commercial label are also hampering the ability of companies to deliver reimbursable product to patients (or expand their label into other indications). Likewise, companies adopting an expedited 505(b)2 route for less innovative products, are running into expensive failures often due to an incomplete understanding of the formulation design space.
In all these cases a potential solution is to fully adopt a Quality by Design (QbD) approach earlier, with preliminary design space exploration through a DoE (design of experiments) even maybe being done within R&D. Enabling this burdensome task, earlier in the value chain, through QbD coupled to physical and data automation is where we see the potentially transformative impact of new digitally-enabled tools in biopharma.
CAR T therapy and other innovative personalized therapies hold great promise, yet these place significant pressure on bioprocessing companies to deliver complicated, high quality products, for often small patient subsets, at affordable costs. Fair to say, biopharma could do with a helping hand.
Here, "scaling out" (bioreactors remain small, but more are used) is expected to enable the delivery of these innovative products to patients, where the traditional model of "scaling up" (increasing the size of bioreactors) may not. Having small, closed and single-use bioreactors means that multiple patients can be treated in parallel. This then introduces further complications around sample traceability, release testing, and facility planning—all challenges that can be partially resolved through technology. The end goal being localized manufacturing of patient specific therapeutics, controlled for quality by a suite of industry 4.0 multi-site software and hardware.
If the bioprocessing 4.0 future is to be realized, what technical changes are needed?
- More equipment needs to be digitally- or cloud-enabled
- Investment in internal IT systems to enable high data volumes
- Investment in pre-processing systems to cater for experiments that produce large data sets (e.g. metabolomics, a technique that is starting to be adopted in bioprocessing)
What would a 4.0 advocate say to those concerned about the time it could take to transition into a more connected laboratory environment? Well, we must consider why the transition may be time-consuming. For example, it will be time-consuming due to the high CaPEx requirement of replacing equipment, or due to internal IT. In our vision, software should work with existing lab hardware, breaking down a slow procurement blocker and delivering benefits to bioprocessing teams today.
Other industries, such as finance, have shifted away from a "this is how we've always done it" mindset… now it seems it's only a matter of time for biopharma to make the leap too. In this context, it is worth contemplating a quote by Ralf Speth, automotive executive and current CEO of Jaguar Land Rover: "If you think good design is expensive, you should look at the cost of bad design."
Michele Wilson PhD. is a content writer at Synthace Ltd. After completing her PhD in Endocrinology (Lincoln University, New Zealand) Michele worked as a UK-based science writer and editor for Technology Networks, where she managed the Biopharma, Cell Science, and Diagnostics Communities. She can be reached at wilsonmichele444@gmail.com.
