Validating Pharma 4.0 for Smart Manufacturing

medical vials on production line in a pharmaceutical company manufacturing facility | Image Credit: © Ali - stock.adobe.com

In September 2004, FDA's Current Good Manufacturing Practice (cGMP) working group published a landmark report titled "Pharmaceutical CGMPs for the 21st Century — A Risk-Based Approach; Final Report" (1). This version echoed the initial draft, released in 2002 and no longer publicly accessible, in stating a groundbreaking objective for the life sciences industry “to devise a maximally efficient, agile, flexible pharmaceutical sector that reliably produces high-quality drugs without extensive regulatory oversight.” Additionally, the intent was to modernize pharmaceutical manufacturing regulations by transitioning from a rigid, rules-based framework to a more agile, risk-based approach, thereby enhancing flexibility and innovation in the industry.

This goal posed new challenges to industry by adding to the delicate balance between patient needs and investor demands. Fortunately, advancements in cutting-edge digital technologies, such as artificial intelligence (AI), machine learning (ML), the Industrial Internet of Things (IIoT), and Big Data, are helping the life sciences industry to address these challenges (2).

The convergence of these digital technologies forms a drug manufacturing ecosystem called Pharma 4.0, a term coined in 2017 by the International Society for Pharmaceutical Engineers (ISPE). This ecosystem promises a revolution across drug discovery, manufacturing, and supply chain logistics, ensuring drugs are delivered precisely when needed, and improving overall patient care.

The current state of manufacturing control and management

Since the 1980s, the pharmaceutical industry has witnessed a significant shift in its manufacturing processes. Traditional, paper-based manual workflows have given way to automated systems, and the industry is on the cusp of embracing Pharma 4.0 and ushering in the era of smart manufacturing. Although precise data on the percentage of life sciences companies shifting from paper-based to digitalized manufacturing are not readily available, there is a growing awareness of the advantages gained through the adoption of smart manufacturing technologies, which include:

• manufacturing and business process automation
• robotics
• big data analytics
• edge computing
• cyber-physical systems
• IIoT
• AI/ML.

The FDA is promoting the use of cutting-edge technologies through its Advanced Manufacturing initiative, a collaborative effort with industry stakeholders to develop and promote innovative manufacturing practices (3). This includes leveraging automation, digitalization, and artificial intelligence (AI) to optimize production processes and improve process control. By adopting these Advanced Manufacturing Technologies, pharmaceutical companies can create flexible and tailored production systems, enabling the creation of customized drugs for personalized therapy treatments based on a patient's unique disease characteristics and genetic profile.

Transformational change in current manufacturing

To meet individualized therapy needs, the life sciences industry is embracing a new, flexible manufacturing approach that allows for producing multiple types of drug batches. To make it cost-effective for manufacturers and patients, switching between these different batch types must be expeditious. Speed and flexibility are manufacturing game-changers, efficiently adjusting production lines and enabling drug companies to better cater to individual patients without compromising quality.

Recognizing technology's critical role in creating innovative treatments, the industry is embracing digital transformation to unlock the full potential of flexible manufacturing. This transformation compels organizations to rewire and restructure their operations to seize these substantial benefits.

Pharma 4.0, which enables flexible manufacturing, has been a game-changer for life sciences manufacturers, empowering them to leverage digital tools to create a more efficient and effective healthcare system.

A deeper dive into Pharma 4.0

It is important to clear up some misconceptions about Pharma 4.0. Some might view Pharma 4.0 as a more holistic approach to manufacturing where the control strategy uses data and automation to streamline production. Others might view it as a digital revolution, bringing new tools and automated processes. Both perspectives are valid.

Pharma 4.0 is not about one specific technology. It represents a comprehensive shift toward data-driven, interconnected manufacturing within the pharmaceutical sector. The operational framework integrates the core principles of Industry 4.0 and tailors them to suit the unique needs of pharmaceutical manufacturing, encompassing the challenges of operational realities and strict regulatory environments within the industry.

Pharma 4.0 envisions an interconnected environment where manufacturing equipment seamlessly communicates, orchestrating a symphony of "smart manufacturing." This leads to a digitized setting where data reigns supreme, acting as the conductor that triggers events and fosters uninterrupted collaboration between business operations and digital processes. This harmonious collaboration ultimately drives production optimization across the entire factory and its supply chain.

Figure 1 depicts a typical Pharma 4.0 ecosystem of a smart manufacturing environment.

Figure 1: Pharma 4.0 components. Figure courtesy of the author.

Pharma 4.0 is not about bringing entirely new equipment or products to the factory floor, as Figure 1 illustrates. Instead, the focus is on using existing and developing technologies, like IIoT, communication networks, cloud computing, data analytics, and more, in innovative ways. This creates a more digital and interconnected environment that offers smart manufacturing functionalities. Some core principles of Pharma 4.0 are:

• Seamless data sharing: All manufacturing and business systems are interconnected so they can share data seamlessly.
• Automation: Process control, process management, and business processes are automated so human operators can focus on more complex tasks.
• Data analytics: Vast amounts of data that manufacturing systems generate can be collected and analyzed in real time using data analytics packages so that failure trends can be identified and preempted.
• Use of breakthrough technologies: Cutting-edge technologies such as AI can be used to discover new drugs and optimize manufacturing processes for personalized medicines.

The key terms of Pharma 4.0

Companies can unlock the full potential of Pharma 4.0 by embracing digitalization. However, before starting the digitalization journey, it is important to understand the various terms used in digital technologies.

Digitization is the process of converting analog information that exists as paper records into electronic formats like Adobe Acrobat’s PDF. This conversion typically involves scanning the paper records.

Digitalization involves using digital technologies to capture and manage data. Examples include field sensors recording process data, operators entering values into databases, implementing a digital document management system, and using software to automate tasks such as manufacturing process control and management.

Digital transformation focuses on how an organization operates, driven by digital technologies. It is not about making incremental process improvements but about using technology to create new business models and customer experiences.

Artificial intelligence (AI) is a branch of computer science that focuses on creating intelligent machines capable of executing certain tasks without explicit programming (4). These tasks include problem-solving, learning from experience, understanding natural language, and recognizing patterns.

Artificial general intelligence (AGI) takes AI a step further. While AI is task-specific, AGI aims to develop machines that possess general intelligence similar to that of humans. This means creating systems capable of understanding, learning, and applying knowledge across different domains (4). AGI would be, for instance, a machine capable of understanding, learning, and applying knowledge in diverse domains, much like a human being.

The difference between AI and AGI is in scope. While AI represents a powerful tool for performing specific tasks, AGI is the next frontier, capable of self-learning using neural networks.

The building blocks of Pharma 4.0

While Pharma 4.0 leverages existing and evolving technologies, it does introduce unique elements that distinguish it from traditional manufacturing. Some of the groundbreaking components that set Pharma 4.0 apart include the following.

Data engineering. Pharma 4.0 hinges on interconnected digital systems, making data the lifeblood of the entire operation. This data-centric approach necessitates a strong emphasis on data engineering, which involves building and maintaining the infrastructure that processes, stores, and analyzes data. Unlocking the full potential of Pharma 4.0 requires a strong foundation in data engineering.

Data lakes. Pharma 4.0's data-driven nature demands capturing vast amounts of information over extended periods, regardless of immediate use. These data are stored in centralized "data lakes" instead of scattered across individual workspaces, as is common practice.

Data lakes provide global data access and empower users with a holistic view of the entire process. They can rapidly analyze the data and take corrective actions in real time to address unexpected issues on the production floor, such as declining batch quality. Companies like Google and Meta refer to this as Big Data, which involves large volumes of data from diverse sources and formats. These diverse data are stored in data lakes for expeditious, authorized global access.

Data integration. Data integration is a foundational element of Pharma 4.0. It combines data from multiple sources into a single, unified view, integrating disparate data sets, formats, and structures to create a cohesive and accurate dataset.

Currently, data exist in data silos. Integration between these silos has been achieved through point-to-point, custom-developed software. As data sources multiply in Pharma 4.0, it becomes imperative for companies to fully integrate these sources and derive cross-department intelligence in near-real time. Consequently, data from different sources must be integrated on an integration platform. This integration platform serves as the central hub, connecting all data sources to the back-end business systems and manufacturing control systems.

Advanced analytics. Pharma 4.0 requires high-quality data from various sources to fuel advanced analytics. These analytics provide the foundation for actionable insights and data-driven decisions that optimize the supply chain. Organizations that excel at leveraging these insights see a significant impact on their bottom line, including increased yields, reduced waste, and maximized production resources. Advanced analytics delve deeper than basic reporting, uncovering hidden trends and connections in massive datasets. This approach uses powerful tools and techniques to forecast future events and recommend the best course of action, going beyond merely describing what the data show (5).

Hyperautomation. Gartner defines hyperautomation as a rapid, business-driven, disciplined approach that organizations use to quickly identify, vet, and automate as many business and IT processes as possible (6).

Hyperautomation is a business-driven strategy that automates various systems and processes using technologies like AI and robotic process automation (RPA). It aims to eliminate manual intervention wherever possible, resulting in increased efficiency and productivity.

While both terms are often used interchangeably, automation and hyperautomation have distinct purposes. Automation focuses on eliminating manual effort for repetitive tasks. Think of it as building a single machine to handle a specific job. Hyperautomation, on the other hand, is like setting up an entire factory. It combines various automation tools, including machine learning and robotic process automation, to tackle complex processes across a larger scale.

Figure 2 illustrates the approach to automating and seamlessly integrating business and manufacturing processes that have the most impact on improving a company’s operations. While the process of hyperautomation is rapid, it is not random but follows a well-defined strategy.

Figure 2: Hyperautomation. Figure courtesy of the author.

Model-based design. At the core of Pharma 4.0 lie model-based technologies like AI, machine learning, and digital twins. These digital models act as virtual replicas of the physical equipment and processes involved in drug manufacturing. By simulating how these processes work, Pharma 4.0 can optimize and troubleshoot them throughout the design phase and even identify issues within the broader Pharma 4.0 ecosystem.

Navigating the frontiers of Pharma 4.0

The path to Pharma 4.0 is thrilling but presents a new challenge: validation. While traditional validation remains valuable for individual Pharma 4.0 components, the interconnected nature of these systems demands a fresh validation approach. This new approach must consider how effectively the components integrate, ensure seamless communication (interoperability), and deliver optimal performance.

Some Pharma 4.0 building blocks are not static. They adjust on the fly, requiring equally agile validation methods to ensure accuracy and effectiveness within the ecosystem. Traditional validation methods suffice for static systems, but they fall short of the dynamic, ever-evolving model-based components of Pharma 4.0. To address this, one must shift from static validation to continuous monitoring and validation. This ensures that these dynamic elements, which update in real time, operate optimally. If their performance deviates during operation, real-time data are essential for swift investigation and prompt corrective action to guarantee peak functionality and maintain the highest standards of quality and reliability.

Building upon the idea of Pharma 4.0, ISPE introduced Validation 4.0 (7). This new approach emphasizes a holistic, risk-based method for validating processes throughout a product's lifecycle. It prioritizes real-time quality checks over simple documentation. However, Validation 4.0 does not delve into specific validation methods for Pharma 4.0 building blocks.

Let us explore these building blocks and how they can be validated and tested.

Data engineering. Data engineers play a crucial role in bridging raw data to actionable insights. They cleanse the vast amounts of information stored in data lakes and transform it into a usable format for analysis. Additionally, they design efficient data pipelines that seamlessly move data between various systems. These data-centric designs are captured in a user requirements specification (URS) document. Unlike a traditional URS, which focuses on processes and systems, a data-centric URS delves deeper into data flow, including data flow diagrams and dependency diagrams. These diagrams aren’t directly tested using conventional techniques like unit testing or integrated testing. Instead, specific tests, such as walkthroughs and test case mapping, ensure that the diagrams accurately represent the data flow. Another test method is impact analysis simulation, which evaluates how changes in data affect downstream processes.

Data lakes. The significance of data lakes in enhancing business performance is increasingly acknowledged. A well-designed data lake maintains clear rules for data types and value ranges, consistent schemas, and improved data reliability. These rules, value ranges, and schemas should undergo validation. Validation also includes verifying data quality through checks such as completeness, integrity, consistency, and format/schema accuracy. Furthermore, validation encompasses data governance aspects, including:

• ensuring effective security and access control schemes for data lake access
• compliance with regulations like General Data Protection Regulation, the Health Insurance Portability and Accountability Act, and others
• user interface testing.

Validating these aspects allows organizations to rely on their data lake for informed decision-making and business success.

Data integration. By testing data integration, organizations can ensure that their data are accurate, complete, and reliable, supporting informed decision-making (8). Steps in data integration validation include:

• performing data quality checks for completeness, accuracy, consistency, and validity
• verifying data transformations, such as data type conversions
• conducting data reconciliation to compare integrated data with source data for accuracy
• implementing manual review for spot checks on critical or high-stakes data integration.

As with any validation process, detailed documentation and reporting of data integration validation results are essential to maintain transparency and accountability.

Advanced analytics. Validating advanced analytics is crucial to ensure the accuracy, reliability, and effectiveness of the insights derived from data. However, validating advanced analytics packages poses challenges because they do not operate solely on static data sources, but also on dynamic and real-time data inputs. Hence, testing should encompass both data inputs and outputs, making data validation techniques critical in this context (9). Various tools are available to validate advanced analytics software packages.

Using data analytics effectively depends on several factors, including the democratization of data ownership and access, where data are available to everyone who needs them. Effective data governance is crucial here, ensuring that company policies specify responsibilities for data definition, creation, verification, curation, and validation—whether by business, IT, or the analytics center.

Additionally, data analytics is typically performed by user-developed or standard off-the-shelf software packages. These are validated using existing computer system validation practices and/or vendor qualification.

Hyperautomation. Hyperautomation represents a comprehensive approach to validation, leveraging a multifaceted combination of technologies including AI, RPA, and production management and process control, among others, to achieve end-to-end automation. Each component can introduce errors when operating together, despite functioning correctly individually. Holistic validation, including reviewing the company’s hyperautomation strategy document, helps identify and resolve such issues preemptively to avoid faulty outputs or flawed decision-making. Steps for hyperautomation validation include:

• reviewing and qualifying process maps to identify bottlenecks and analyze data flows
• testing to verify suitability for specific tasks and seamless integration with existing systems
• ensuring data accuracy for data-driven processes using tools for data profiling and cleaning and implementing checks for completeness, consistency, and adherence to business rules
• non-functional testing to validate aspects like performance, scalability, and security of the automation.

Implementing a thorough validation strategy throughout the hyperautomation lifecycle ensures the success of automation initiatives, delivering maximum business value and impact.

Model-based design. AI and ML models constantly evolve with new real-time data. These models are validated using a combination of techniques, such as:

• cross-validation (dividing data into training, testing, and validation sets)
• hyperparameter tuning for optimal performance
• edge case testing against unusual or extreme inputs
• bias testing to ensure fairness and non-discrimination.

Validation should also include reviewing documentation that describes the basis of model design, the development approach, performance testing strategy, version control, associated model risk assessment, and risk control strategy. Monitoring plans assess ongoing model performance and recalibration frequency for model parameters and hyperparameters.

Digital twin models have become crucial in advancing manufacturing. Unlike AI/ML models, digital twin models undergo credibility assessment through verification, validation, and uncertainty quantification (10).

Conclusion

The life sciences industry stands at a pivotal moment in its adoption of Pharma 4.0, poised for significant advancements. To fully harness its potential, a paradigm shift toward more science- and technology-driven validation practices is imperative. Instead of relying solely on exhaustive regulatory documentation, embracing a proactive approach is essential.

Another prerequisite for maximizing the realization of Pharma 4.0 benefits is the engagement of skilled personnel equipped with the necessary background and experience. Today, quality assurance personnel play a pivotal role in overseeing the implementation efforts of Pharma 4.0 and are expected to possess the requisite background in science and technology along with expertise in compliance (11).

In addition to chronicling the industry’s ongoing journey with Pharma 4.0, this article has outlined a robust framework for validating its technologies. Future efforts will refine this framework, exploring Pharma 4.0 applications in emerging fields such as gene therapy and personalized medicine, while addressing ethical and societal implications. The validation concepts outlined here are foundational to Pharma 4.0’s widespread adoption, promising a more efficient, effective, and patient-centric pharmaceutical industry transformation.

References

1. FDA, Pharmaceutical CGMPs for the 21st Century — A Risk-Based Approach; Final Report; FDA, September 2004. fda.gov/media/77391/download
2. FDA. Focus Area: Advanced Manufacturing. FDA.gov/science-research, updated Sept. 6, 2022 (accessed Sept. 18, 2024).
3. FDA, Draft Guidance for Industry, Advanced Manufacturing Technologies Designation Program Guidance for Industry (CDER/CBER, December 2023). fda.gov/media/174651/download
4. Winston, H. AI vs. AGI. theaimatter.com, Oct. 21, 2019.
5. Sacolick, I. How to Validate Data, Analytics, and Data Visualizations. infoworld.com, Feb. 28, 2019.
6. Gartner. Hyperautomation. Gartner.com (accessed Sept. 18, 2024).
7. Bennett, C.; Heesakkers, H.; Horneborg, S.; et al. Industry Perspective: Validation 4.0 – Shifting Paradigms. Pharm. Eng. 2020, 40 (6) 52–54.
8. Dethlefsen, F. Popular Data Validation Techniques for Analytics and Why You Need Them. amplitude.com, Dec. 14, 2020 (updated Dec. 18, 2022).
9. Chin, J. K.; Hagstroem, M., Libarikian, A.; and Rifai, K. Advanced Analytics: Nine Insights from the C-Suite. McKinsey.com, July 5, 2017.
10. Shao, G.; Hightower, J.; Schindel, W. Credibility Consideration for Digital Twins in Manufacturing. Manuf. Lett. 2023, 35, 24–28. DOI: 10.1016/j.mfglet.2022.11.009
11. ISPE, Baseline Guide Vol 8: Pharma 4.0, First Edition (December 2023).

About the Author

Chinmoy Roy is a Life Science industry digital transformation consultant with over 38 years of experience in manufacturing process automation, digital transformation, CSV/CSA, Data Integrity, 21 CFR Part 11 and Annex 11. He is a member of ISPE’s Data Integrity Special Interest Group (SIG) and is a senior industry consultant for ValGenesis, Inc. He has worked for and consulted with several companies, including Genentech, Roche, Bayer, Novartis, Gilead, and others. He travels the world to train industry personnel in the areas of his expertise, as well as to conduct data integrity audits.