Drug companies that do not use digital tech and automation in manufacturing are missing out, say researchers, who argue that, from an efficiency perspective, the “4.0” approach, which already has been available for a number of years, just makes financial sense.
The operational benefits of digital production technologies are now well-established, according to Peyman Moghadam, PhD, associate professor of data-driven materials engineering at University College London in the U.K.
“Industry 4.0 could revolutionize biomanufacturing, delivering order-of-magnitude cost reductions for life-changing treatments, including vaccines. The manual nature of current Good Manufacturing Practices (cGMP) leads to human errors and sub-optimal use of raw materials that can cost hundreds of thousands of dollars per batch. Additionally, the lack of real-time monitoring of batch quality can lead to costly out-of-spec (OOS) drugs.
“Therefore, by integrating IoT connectivity to unit operations, companies can harness [machine learning] ML and mathematical models to monitor and control processes in real-time, thereby enabling continuous automated operations. Additionally, models can rapidly detect and diagnose faults and predict quality, thereby reducing downtime and OOS batches,” he tells GEN.
Digital technologies also give manufacturers the ability to respond to fluctuations in demand, Moghadam says.
“Beyond ROI, Industry 4.0 brings agility to manufacturing operations. Predictive models and simulations allow proactive adjustments to upstream or downstream changes, such as supply chain disruptions, shifts in product demand, and patient feedback, supporting the ISPE Pharma 4.0 holistic control strategy framework.”
QbD profitability
Industry 4.0-enabled techniques like automation and artificial intelligence can also make process development more cost-effective, according to Moghadam, who cited quality-by-design (QbD) as an area of potential application.
“QbD’s cost-effectiveness hinges on leveraging expert knowledge to iteratively reduce experimental requirements for process understanding. However, traditional QbD methods rely heavily on brute-force statistical tools that make it resource-intensive.
“ML techniques such as Gaussian Processes and Bayesian Optimization, have demonstrated a clear benefit in their ability to transfer knowledge from one process to the next, thereby cutting experimental costs by up to 50%. When applied adequately, these techniques can provide a quick ROI within QbD,” Moghadam said.
Adoption
Moghadam and colleagues argued in favor of “biopharma 4.0” in a recent study, concluding that, for companies with the right data infrastructure and expertise in place, investing in the approach is a no-brainer.
He tells GEN: “A company’s initial digital maturity significantly impacts its ability to adopt 4.0 principles, but digital transformation is a continuous journey.”
“Embracing IT best practices like agile methodologies, continuous integration, and continuous delivery allows companies to transition incrementally, demonstrating ROI and de-risking technology at each step while integrating stakeholder feedback.”
The key, Moghadam says, is being able to integrate manufacturing technologies so they can gather and transmit process data from each unit operation in real time.
“Once connectivity is established, simulation software such as gPROMS, Simulink, and programming languages like Python or C++ can be used.
“Additionally, several vendors now offer more bespoke Industry 4.0 solutions. These tools further streamline the transition, helping companies progress toward a fully integrated, smart manufacturing ecosystem.”
