If your business has ever come under FDA scrutiny, chances are data integrity was one of the issues cited. More than half of FDA citations for pharma companies involve data integrity violations — and the number of warnings and citations has dramatically increased in recent years.
Although it’s easy for pharma manufacturers to treat data integrity as checking the box for compliance, the reality is that making this a priority delivers undeniable business value. After all, the ultimate aim of FDA policies is to ensure that all drugs brought to market are safe — and maintaining data integrity is an integral component.
When pharma manufacturers stall key digitalization initiatives that improve data collection, recording and quality control efforts, they’re playing a risky game. It could result in significant production quality issues, compliance violations that incur millions of dollars in fines, product recalls or delays in getting medicines to patients, not to mention loss of patient and shareholder confidence. In a worst-case scenario, a patient could be harmed by a product.
When harnessed fully, digital tools empower pharma manufacturers to gain more value from their data and optimize production output, product quality and efficiency. Moreover, by minimizing deviations, these technologies prevent audits from becoming a bottleneck.
Old roadblocks, new challenges
Data integrity includes comprehensive standards, such as FDA 21 CFR Part 11, and guidelines to ensure all data are complete, consistent and trustworthy. The FDA uses the guiding principles of ALCOA+, which states that data should be attributable, legible, contemporaneous, original, accurate, complete, consistent, enduring and available.
When the pharma industry was dominated by blockbusters, data were largely captured in a paper trail, which has a propensity for human error and quality assurance risk. Transitioning plants to new digital technologies, which could reduce these sources of error, required revalidation for compliance.
Revalidation can be a time-consuming and costly process, which leads many companies to defer the transition. According to research from AspenTech, more than half of pharma companies say regulations undermine their digital transformation goals. At the same time, improving regulatory compliance is also among the top reasons for pursuing digital transformation (Exhibit 1).
Today, pharma manufacturing faces increasing complexity — a diverse product portfolio, increased contract manufacturing and localized production, not to mention supply chain and inflationary pressure. These changes reinforce the industry’s adoption of Pharma 4.0 — a term coined by ISPE — with companies introducing smart manufacturing principles and integrated advanced technologies like AI, Internet of Things (IoT) and data analytics.
While Pharma 4.0 enables greater efficiency, consistency and cost-effectiveness across manufacturing processes, digital transformation comes with its own challenges. Modern manufacturing facilities face a deluge of data that needs to be managed, recorded and reported on an ongoing basis.
In addition, manufacturers rely on a mix of technologies across facilities, which may not easily connect with one another or integrate with operations and information technology systems. These digital challenges can lead to incomplete datasets which undermine data integrity. For instance, manufacturers rely on a variety of data sources — from different vendors and communications technologies — all with different levels of data integrity. This mixed environment makes it challenging to have reliable data in the right place. The data may exist, but is it accessible when it’s needed? If not, then it can’t be used in decision making or to build alignment across different groups and functions.
One example of this is technology transfer — scaling up the production for commercial manufacturing. Effective tech transfer requires data sharing across departments, sometimes with an external partner. Successful tech transfer should be efficient and without compromises in product efficacy or safety. It’s always possible to prevent shipping bad product by manually rejecting a batch during product release, but that is expensive, wasteful, and undermines efforts to get medicines to patients faster.
Modernizing data integrity
Ideally, Pharma 4.0 efforts improve data integrity and simplify compliance. These practices also make industrial data more valuable and actionable throughout the manufacturing life cycle by driving greater assurance of product quality and increased production efficiency.
The following guidelines can help an organization modernize and improve data integrity practices:
Stay ahead of regulations
It is a best practice to stay ahead of regulators in the ever-changing pharma landscape.
Over the past few years, the FDA shifted guidance to support digital transformation in manufacturing. For example, the FDA increasingly expects manufacturers to provide data generated through process analytical technology (PAT) and simulation to justify process design decisions. Manufacturers relying on physical test methods alone will face greater scrutiny during audits and approvals.
Additionally, FDA now focuses more on risk-based assessments for software validation, with the intent to minimize real and perceived obstacles to digital transformation. In 2022, the FDA published draft guidance for computer software assurance (CSA) in manufacturing which focuses on risk-based testing where software functionality directly impacts product quality and patient safety. CSA is intended to supplement previous guidance — computer system validation (CSV) — which required extensive, scripted testing of all software features, resulting in an unnecessary burden necessitating excessive time and documentation.
Keeping up with regulatory changes is clearly critical to maintaining compliance, but the recent changes go beyond a mere process change. Recent regulatory guidance requires a cultural shift as well as significant change management.
Prioritize digitalization efforts
Investments in digital transformation pay dividends down the road, enabling the end goal of simpler data management, easier adherence to regulations and more robust drug safety monitoring while minimizing disruptions to production.
Automating data capture and embracing digital systems to manage data sources minimizes human error and the risk of deviations — improving data accuracy and reliability, while also reducing manual labor. Likewise, automated audit trails that track original data and subsequent changes ensure data processes are accessible, transparent and readily available for reference when it’s time for an audit.
For example, in 2019, McKinsey reported that pharma quality control labs that were early adopters of advanced analytics, automation and data connectivity saw a 65% reduction in deviation and over 90% faster closure times for identified issues. These data integrity improvements resulted in more than 50% savings in quality control costs.
Far too many companies still rely on paper records, manual data transcription, and other paper-based processes that are not only time-consuming and inefficient but can make it difficult to control user access and track changes. Instead of searching piles of paper records, digital systems make it possible to generate audit reports with all the necessary information in one place, thereby saving time and reducing the likelihood of incomplete reports being sent to regulatory bodies.
Put your data to work
Pharma manufacturers have not begun to fully tap into the potential of the rapidly growing volume of operations data — be it predictive maintenance or process improvement — let alone leverage learnings across a global enterprise.
One of the challenges is the time required for data preparation. Operations data have evolved over time and now include a web of different technologies and data formats which can lead to siloed data. According to the AspenTech survey, data silos impede cross-functional collaboration for nearly half of pharma manufacturers and limit data-driven decision making. For large organizations with over $1 billion in revenue, that number is slightly higher (Exhibit 2). ‘Trapped’ data requires extensive maintenance in the long term, as efforts are required to build and maintain data source connectivity. Data scientists can spend approximately 80% of their time preparing data before it is ready for analysis.
Pharma manufacturing needs to invest in centralized systems to connect data from various sources, systems and users across an organization. These systems will improve data integrity by ensuring data remains consistent, accurate and actionable. A centralized operational data hub also reduces the burden of data reporting and allows organizations to aggregate data via a single audit point rather than manually merging data from various digital systems. It empowers various parts of the organization to more easily collaborate, generating valuable insights and analytics that can scale across multiple plants or manufacturing processes.
Train and invest in people
Digitalization has made it easier for companies to meet ALCOA+ requirements, but software alone cannot make a company compliant. Even with the right digital tools, employees ultimately execute procedures and make decisions that can impact data integrity. It is vital to invest in people and training that help all employees understand the FDA’s policies and the impact on their organizations’ standard operating procedures (SOPs) and quality management systems (QMS).
After initial compliance training and education, consistent reviews and internal audits are integral to ensuring employees are empowered to uphold data integrity best practices in their daily work. Fostering a culture that is laser-focused on these processes is paramount and will help ensure the safety of drug products.
Guaranteeing high quality
The question isn’t if an audit will happen, but when it will happen. However, beyond avoiding FDA citations and compliance breaches, remember that data integrity helps guarantee high quality pharmaceutical products that maintain public and shareholder confidence. And when done right, digital tools and sound industrial data practices empower organizations to keep up as pharma enters its next phase of digital innovation.