Actively Working to Enhance Biomanufacturing’s Digital Maturity
While meaningful progress has been made by the biopharmaceutical industry into digitalization, there is still a lot of work to do to achieve a true digital transformation.
The global biopharmaceutical industry has been experiencing a period of significant growth over recent years, with biologic-based drugs comprising an expanding share of the market. However, while gaining popularity amongst biopharmaceutical companies and experiencing increased demand from patients, these complex biologic-based therapies pose numerous development and manufacturing challenges (1).
As the production of biologics is inherently more complicated than that for traditional small-molecule drug products, it is unsurprising that the time and costs associated with their production also be inflated. Given the amplification of pricing pressures over recent years, it is becoming ever more critical that the industry invests in new technologies that can improve production efficiencies (2).
“[A] shift towards digital transformation, known as Pharma 4.0, has become a significant priority, with many companies seeking to improve efficiency, productivity, and quality while reducing costs,” comments John Atkinson, Global Head of Information Security Office, FUJIFILM Biotechnologies. “Digital tools are crucial in streamlining processes, minimizing human error, and enabling real-time data analytics across development and production stages. This evolution results in faster decision-making and more agile production models, ensuring quicker time-to-market without compromising safety or compliance.”
Meaningful Progress but More Work Needed
“[There has been] meaningful progress in the biopharmaceutical industry’s digital maturity, but it remains uneven,” remarks Minni Aswath, VP of Process Development & PD Downstream, Bionova Scientific. “While larger pharma and CDMOs have begun integrating data lakes, management execution systems (MES), and digital twins, widespread adoption is still in early-to-mid stages.”
Although there have been a few leading companies that have embraced digitalization, the majority of the industry is in an intermediary stage of digital maturity, concurs Alexander Seyf, CEO & Co-founder, Autolomous. “Many organizations continue to rely on traditional paper-based systems supplemented by partial digitisation, hybrid approaches that support existing workflows but fall short of replacing them in ways that deliver measurable efficiency or quality gains. This reliance on fragmented data systems and isolated digital tools highlights a persistent gap between digital ambition and practical execution,” he says.
For Dan Strange, CTO, Cellular Origins, while inroads have been made into the digitalization of biopharma, there is still a long way to go to achieve truly digital manufacturing. “While electronic batch records, MES [manufacturing execution system] platforms, and analytics dashboards are becoming increasingly common, many facilities still operate with fragmented digital infrastructure and limited real-time integration,” he explains. “In particular, the physical process architecture (how materials move, how units talk to each other) is often still rigid and siloed, and it’s still not uncommon to see completely paper-based batch records.”
Significant investments into the industry 4.0 transformation have clearly been made by many manufacturers within the biopharma sector, allowing for operational efficiency improvements, reports Max Baumann, Co-Founder and Partner, Treehill Partners. However, “the picture overall is quite heterogeneous with pen and paper still being very familiar working equipment to most employees across the industry,” he continues. “In many instances we see process analytical technologies being adopted by manufacturing firms, but such adoptions then being confined to individual projects like a particular product.”
While the industry has made some strides towards digital maturity, it needs to do more than just isolated technology upgrades to be able to achieve true digital transformation, emphasizes Elvin Vargas, Senior Director, Automation Engineering and CAPEX, FUJIFILM Biotechnologies. “[True digital transformation] requires end to end integration, intelligent automation and a cultural shift that puts data and connectivity at the core of the innovation and patient care,” he says. “Industry leaders and technology partners need to work together to co-create ecosystems that are interoperable by design, adaptive by nature, and driven by a shared commitment to accelerate innovation and ensure quality.”
A Long List of Challenges to Comprehensive Digitalization
There is a long list of challenges to overcome before comprehensive digital transformation across biomanufacturing workflows can be achieved, points out Baumann. “It is not to be expected that legacy infrastructure gets integrated into one comprehensive automated system, so we really only talk about new builds when we mean comprehensive automation,” he says.
“Within that, there of course are cutting edge new models, model series, and custom-built factory machinery which get leveraged, but these systems rarely ‘talk’ with systems outside the key workflow they are integrated in,” Baumann continues. “So, this is a point of system integration. As the drug manufacturing process is somewhat compartmentalized, integration across the steps is naturally difficult to achieve.”
Incompatibility between equipment and software is one of the biggest challenges, confirms Seyf. “Each step in the process often relies on machines from different vendors, and these systems typically use proprietary software that doesn’t easily connect with others. As a result, creating a fully automated, end-to-end process requires significant effort to integrate these disparate systems,” he says.
Mike Tomasco, Senior Vice President, Chief Information Officer, FUJIFILM Biotechnologies, also emphasizes the disparate vendor landscape of all the equipment and instruments as a barrier to achieving end-to-end automation in a biomanufacturing facility. “[Industry] operates in an environment where the equipment, hardware, and software from multiple vendors must be integrated into a single data driven ecosystem to facilitate complete end-to-end automation,” he adds.
“Historically, many suppliers of this equipment operated within their own closed ecosystems and the data and information generated from company A’s equipment was not easily integrated with the same type of information from company B’s equipment,” Tomasco adds. However, he remarks that this situation has improved over the years, and it is now much easier to share data between equipment.
System interoperability, whereby the integration of disparate legacy systems with new platforms, is definitely complex, asserts Aswath. “Then, there is the challenge of data standardization, and ensuring that data formats are consistent across unit operations is key,” she says.
“An important foundational element of any digital automation strategy should be rooted in good data governance and management practices,” Tomasco agrees. “Defining data standards and rigorously applying your own rules to data management will help overcome the inherent challenges related to a multi-vendor ecosystem of capabilities and solutions.”
Within the cell therapy space, progress has been made into the creation of individual ‘islands of automation’ that are highly effective; however, integration of these units into a fully streamlined, end-to-end automated workflow is still a primary challenge, Strange specifies. “Currently, manual intervention is still needed for critical tasks like loading and unloading consumables, sterile welding connections, and physically transporting materials between different process stages,” he says. “These manual steps introduce inefficiencies and variability, limiting the overall effectiveness and scalability of automation.”
While there have been attempts to resolve this issue through all-in-one consumables, the flexibility that is inherent with modular systems is sacrificed, Strange explains. “Modular systems allow developers to select the best-performing unit operations tailored specifically to their processes, promoting efficiency and adaptability,” he reports.
Additionally, even when systems are fully integrated, companies are required to demonstrate to the regulatory authorities that these systems are reliable, safe, and compliant, demanding time-consuming validation and documentation, Seyf points out. “These technical and regulatory challenges mean that many manufacturers still rely on people to manage handoffs between automated steps, limiting the overall efficiency gains,” he notes.
Furthermore, the level of investment, both financially and in terms of effort, to implement full automation is significant and is difficult or deters smaller manufacturers who do not have the resources or risk tolerance to cope with such a toll, Seyf adds. “As a result, automation is typically adopted in stages, with some parts of the process becoming highly automated while others remain manual,” he says.
Raised Regulatory Expectations
“As biomanufacturing becomes more digital, regulators are raising expectations around how companies handle data and ensure the reliability of new technologies,” Seyf continues. “One major focus is data integrity, making sure that digital records are accurate, secure, and traceable. This means companies need systems that can track who accessed or changed data and ensure that nothing can be lost or altered without detection.”
While the outlined requirements form part of long-standing regulations, such as FDA’s 21 CFR Part 11, they are gaining in importance as digital systems and automated equipment are more commonly used, Seyf explains. “At the same time, regulators expect companies to properly validate digital tools, making sure they work as intended and that any updates or changes are carefully controlled throughout the system’s life,” he says.
“Regulatory bodies, such as the FDA, are actively considering the potential of artificial intelligence (AI) and machine learning (ML) to transform the industry,” adds Tomasco. After AI started to be applied in real-world settings within industry, approximately 5–7 years ago, regulatory authorities have issued guidance documents and offered workshops to help companies with its application in drug development and manufacturing.
“For AI-driven systems, authorities like the FDA and EMA are introducing guidance for risk-based validation approaches in GxP [good practice] environments, including verification of data quality, algorithm traceability, and model transparency,” Baumann specifies. “Continuous and adaptive validation models are gaining favor to ensure ongoing compliance in dynamic digital contexts. These evolving frameworks focus on protecting product quality, patient safety, and public health by keeping pace with rapid advances in automation, AI, and analytics in modern biomanufacturing.”
In light of the increasing role of AI and automation in biomanufacturing, an evolution of regulatory frameworks is inevitable, confirms Aswath who also identifies data integrity as a key area for companies to consider. For data, companies need to ensure that they are attributable, legible, contemporaneous, original, and accurate, while also being complete, consistent, enduring, and available (ALCOA+ principles), she emphasizes.
“As facilities become more digitally integrated, regulators are placing increased emphasis on data integrity, cybersecurity, and AI validation,” agrees Strange. “ALCOA++ [which adds traceability to the set of principles] is being applied not just to lab notebooks, but to robotic actions and ML-driven decisions.”
Importance is definitely being placed on cybersecurity, “and with increased connectivity, data protection becomes mission-critical,” Aswath asserts. “AI and ML validation is required too as regulators are beginning to require transparency into the behavior of algorithms used and decision logic.”
The regulators need to see that the AI algorithms being “used in decision-making are reliable, explainable, and properly monitored over time,” concurs Seyf. “Regulatory guidance is still evolving, but it’s clear that companies will need to be transparent about how AI tools are used and have plans in place to manage any risks. Overall, moving to digital and automated systems offers many benefits, but it also means companies need to be proactive about compliance in this new environment,” he states.
“The industry also has an opportunity to influence regulatory changes; the FDA recently announced a CEO listening tour to meet directly with pharmaceutical and biotech executives, gathering honest feedback and ideas, with the intent to support innovation,” Tomasco specifies. “We expect to see much of this innovation driven by technological advancements and AI.”
At a Pivotal Moment
Even though investment into digital infrastructure is rising, challenges around scaling digital strategies across the entire lifecycle persist, Aswath asserts. “Industry-wide, cultural change and legacy system integration are slowing full digital transformation,” she says.
According to the most recent Pharma 4.0 survey from the International Society of Pharmaceutical Engineering (ISPE) (3), while the majority of industry is piloting digital technologies, end-to-end integration of digital solutions across GxP processes is rare, notes Seyf. “These figures reinforce the idea that while digital tools are being adopted, full digital maturity has yet to be reached,” he remarks.
“Although the foundation for broader digital adoption has been established, achieving genuine digital maturity across the sector will require a more unified strategy, stronger leadership commitment, and clearer industry-wide standards,” Seyf adds. “Without these, the sector risks missing the opportunity to fully realise the transformative potential of digital technologies by the end of this decade.”
Industry is at a pivotal moment, stresses Strange. The science behind therapies, such as CGT, is “no longer the bottleneck, but manufacturing therapies to scale is,” he says. “To serve the next 100,000 patients, we need systems that are flexible, reliable, and scale-ready from day one.”
However, for highly regulated industries, such as the biopharmaceutical industry, complex regulations and the management of sensitive data pose hurdles to the adoption of digitalization, emphasizes Atkinson. “Despite these hurdles, the industry, supported by organizations such as ISPE, is actively working to implement digitalization and automation to enhance its digital maturity,” he summarizes. “Companies that fully embrace digital solutions will be better positioned to meet the rapidly evolving market demands and regulatory expectations.”
References
CAS. The Rise of Biologics: Emerging Trends and Opportunities. White Paper, June 7, 2023.
Narayanan, H.; Sponchioni, M.; Morbidelli, M. Integration and Digitalization in the Manufacturing of Therapeutic Proteins. Chem. Eng. Sci. 2022, 248 (Part A), 117159.
Minero, T.; Kuger, L. The 7th ISPE Pharma 4.0 Survey: Digital Transformation. ISPE.org, Online Exclusive, September/October 2024.
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