Biopharmaceutical manufacturing’s competitive advantage is shifting from biological advances to cost effectiveness and resilience. Succeeding isn’t as straightforward as merely improving process efficiency, though.
Key challenges include process uncertainty and batch-to-batch variability, manufacturing dynamics, and addressing market pressure to meet growing demands at lower costs.
Transitioning “from science labs to smart operations…requires a proactive use of data analytics and operations management to inform daily decisions,” according to a recent paper by scientists from Eindhoven University of Technology (TU/e), MSD, Merck USA, GEA Group, and Ceva Santé Animale. The roadmap they developed begins with strategic vision and the right technology infrastructure, such as continuous manufacturing and real-time release testing.
The process intensification inherent in continuous manufacturing makes it attractive to biomanufacturers. Now, more sophisticated sensors are providing processing data that otherwise is difficult to obtain. Consequently, “Continuous manufacturing systems will generate large amounts of process data,” Tugce Martagan, PhD, associate professor, TU/e, told GEN.
Greater gains
To achieve greater gains, however, “We also need to equip these new technologies with operations management/AI-driven control algorithms to achieve optimal performance,” Martagan says. By combining in-depth sensor data with increasingly sophisticated analytics and operations management strategies, deeper, often cross-functional, insights into production processes may emerge that can further improve operations and results.
“It is important to have the right vision and technology infrastructure to collect, store, and make the most of the process data to achieve the best performance,” Martagan emphasized.
The most important optimization step, however, may be to standardize processes. “Our industry depends on so many known and unknown parameters, and, if known, their contribution to the whole is often not known well,” Bram van Ravenstein, associate director and operations lead for bacteriological production, MSD, told GEN. “By highly standardizing the process, gathered data can more easily be used to start building models.”
Furthermore, he noted, multidisciplinary expertise is vital not only to develop and implement the initial models, but to transfer knowledge among manufacturing sites.
“The easiest place to start are packaging and filling processes, because the number of contributing parameters is reduced,” van Ravenstein added.
The team’s original study involved tablet production, but, “I believe many of the insights, models, and conclusions can be transposed to other pharmaceutical processes, [including biotechnology], therefore boosting gains in other areas,” van Ravenstein said.
Ultimately, Martagan concluded, “We hope the roadmap and data we have presented will inspire and facilitate new research at the intersection of operations management, data science, and biomanufacturing to advance science and industry practice.”
