IDBS Blog | 31st May 2023

Ensuring product, process and patient data integrity and traceability throughout the BioPharmaceutical lifecycle in the era of personalized medicine

By David Brick, Senior Director, Data Science, IDBS

The role of data integrity in personalized medicine

The pharmaceutical and biotech (“BioPharmaceutical”) industry is facing a multitude of growing challenges, including new approaches to support the growth in personalized medicine, managing external manufacturing, supply chain disruptions and more. These challenges increase cost, complexity and risk.¹

Contract development and manufacturing and contract manufacturing organizations (CDMOs, CMOs) form the kernel of the supply chain throughout the BioPharmaceutical — small and large molecule — industry. To deliver life-saving drug discoveries to the market, drug innovators rely heavily on the already-established manufacturing systems of CDMOs and CMOs, rather than implementing in-house manufacturing with additional validation requirements.

Credit: Science Channel (2018). How biologic medicines are made.

Despite the integral value of CDMOs to drug innovation and production, ensuring data integrity across multiple organizations remains a significant issue and makes the supply chain fragile. While each organization follows its procedures for the capture and review of electronic data, additional procedures must be in place to ensure data is transmitted securely, accurately and within agreed-upon time frames. When the partners are using different tools and different data structures, the transmitted data must be transformed – this must be done in a way that ensures data integrity is maintained and with a process that can be validated. Additionally, joint data governance is needed to ensure the content and context of the data are clearly understood on both sides and to manage change to the data-sharing approach over time. Of course, as the complexity of BioPharma products increases, so does the volume of data generated, increasing the difficulty of preserving data integrity while meeting agreed timelines.

When working with CDMOs and CMOs, sponsor companies provide critical information to ensure the integrity and quality of the drug, while maximizing yield from the manufacturing process. While the contract manufacturer sets out to deliver products that meet those standards, they also provide process and quality data back to the sponsor. When the manufacturer is able to share a more complete dataset in a timely manner, they can gain advantages from the sponsor’s experience and knowledge of the process. Sharing high-quality/high-integrity data in a timely manner can benefit all parties. When there is no system or failsafe method to securely exchange data, as might happen without agreements about exchanging data or in a one-time tech transfer process, both drug innovator and contract manufacturer risk errors or omissions that can affect the integrity, quality and yield of the drug. We explore the challenges of maintaining data integrity in the tech transfer process in greater detail in our infosheet. Moving forward into more personalized medicine models, errors or data integrity questions that affect timely batch release can have serious consequences for the patient.

Issues with data availability, data integrity or supply chain traceability limit the scope of knowledge and confidence the drug innovator has over the final product and can delay product release. Further, many drug innovators, with limited data access, cannot obtain the vital development and manufacturing information needed to drive future innovation.

This can create massive risks for the enterprise for several reasons:

A lack of data integrity can exacerbate regulatory issues that lead to approval or time-to-market delays. ‘Records not being concurrently maintained with the performance of each significant step so that all steps can be clearly traced’, and ‘failure to maintain required records’ are among the top reasons for an FDA Form 483 issuance in biologics manufacturing.²
A lack of data availability and/or data integrity issues or concerns in development can delay product launches while data issues are resolved. Post-launch, data issues can lead to additional costs and delays in process monitoring, product release, performance surveillance and ongoing innovation.
Problems can emerge with patent expiry – having partnered with a CDMO or CMO harboring specific processes and instruments, the drug innovator can face a challenge when reproducing the product, while the CDMO or CMO holding possession of the manufacturing workflow can introduce generic versions of the drug for lower prices.

These risks are elevated in the era of personalized medicine involving patient samples, blood and tissue to derive therapeutics. The added processing of individualized batches and the involvement of non-identifying patient data increases not only the intricacies of the manufacturing process, but also the data dimensions that must be captured, tracked and available for analysis. Therefore, the drug innovator must be fully acquainted with the entire BioPharmaceutical production line to ensure seamless product launch and successful ongoing patient engagement.

Setting up data solutions to overcome integrity and traceability issues

The issues outlined above raise the question of how startup biotechnology companies can best create a flexible yet compliant product, process and patient data management solution that can cope with the dynamic nature of personalized medicine and deliver relevant insights as they grow. Forward-thinking companies recognize the need to foresee the entire product lifecycle from development or cell apheresis/collection to manufacturing and then delivery to the patient, all while mitigating complications along the way.

The drawbacks of data solutions that are not scalable

Most companies start by recording key process data in MS Excel. This approach meets their needs until they start adding more scientists collecting the data and more users consuming the data. With multiple users and varying priorities, it quickly becomes impossible to ensure data integrity and traceability within a single company. As external partners are added, the data integrity concerns grow and new risks are introduced as data is distributed across multiple sources. An illustration of this complex interchange of data across the product lifecycle is illustrated in Figure 1.

To address those risks and to manage their data, many companies find that implementing a cloud-based digital data backbone that is built to facilitate the timely exchange of information between the drug innovator, the CDMO and all other parties integral to the supply chain is a critical step to ensuring data integrity and reducing risks inherent to collaboration.

What are the ideal characteristics of a cloud-based digital data backbone in personalized medicine?

Ideally, your choice of a cloud-based digital data backbone will have the following key features:

Flexibility: Product and process data management solutions should be flexible enough to accommodate various data types. Both the R&D and the manufacturing phases encompass input not only from manually-curated, paper-sourced data but also from automated high-throughput equipment and data generated offline in various labs. An integrated data backbone must be compatible with manual entry and bulk data uploads as well as automated extraction from other data systems and instruments.
Versatility: It must boast the versatility to not only retain the existing data context but also allow for contextualization and utilization of data on the fly. When the system provides dynamic data exchange to ensure consistent expansion and modifications in the product dataset, it opens doors to a more transparent and trust-based relationship between innovators and manufacturers. Such versatility is a key asset, particularly in personalized medicine.
Expandable data dimensionality: Finally, to be prepared for the thriving personalized medicine (also known as precision medicine) lifecycle advancements, the digital data backbone must be able to handle patient-specific data so that measurements such as blood type, oxygen levels and age can be incorporated into the dataset and contextualized with the manufacturing process data.

Data management pitfalls to avoid

Data lakes without built-in data contextualization fall short

An alternative trend we sometimes encounter among emerging BioPharma startups is the creation of data lakes or lakehouses for data management. These tools usually gather and categorize complex datasets into an intermediate data repository. Frequently, these solutions are built to satisfy certain business needs; however, if standalone, these tools can lack the data contextualization and genealogy that helps scientists and quality managers gain valuable insight from their process data. This is not a problem that is confined just to startups – at larger companies this approach can make data access even more complex. IT may pool all data into one repository with the hope it makes it easier for scientists to access and find the data they want, but this can lead to data integrity issues as larger repositories are more difficult to navigate. Their breadth can make it harder to ensure that users are selecting, not just all of the appropriate data, but also only the appropriate data for their analyses.

Software built to capture and maintain data within the manufacturing context overcomes this issue by allowing the integration of new data along with the extraction of legacy data, simultaneously providing the data to users within a familiar structure; while a secure cloud-based solution enables data aggregation and verifiable transformation across both the innovators and contract manufacturers. The combination of context-based software with a cloud-native architecture can deliver the outcome that both IT and the users are seeking – user-specific access to reliable scientific, processing and quality data in a timely manner. Unlocking the key data from a range of sources ensures the availability of the most relevant and beneficial process information to drive innovation.

Ensure digital data requirements are included in partner contracts

With the setup of a centralized data management platform to collect, analyze and exchange critical product, process and patient data, startups, emerging and global drug innovators must also upgrade how they handle legal matters with the CDMO. Due to the ambiguity around the type and size of data needed, there might be discrepancies between what the innovator expects and what the CDMO delivers. Therefore, supply and quality agreements must not only define the specific data that the CDMO shares with the drug innovator but also emphasize the dynamic nature of data exchange. For example, the contract must anticipate that novel critical process parameters (CPP) could be discovered during manufacturing as the product gets refined and that the drug innovator gains access to improvements. This agreement must be made from the get-go so that both parties can accommodate flexibility in development and manufacturing. With a contractual digital data management and governance agreement, companies can gain more control over their data requirements from the CDMO rather than relying solely on the CDMO’s standard practices; while they can also provide more flexible tools to simplify the adoption of needed changes.

Taking a holistic approach to data management throughout the BioPharma lifecycle

As the pharmaceutical and biotech industry undergoes evolution in the face of external pressures, the ability to speed both innovation and time-to-market becomes the critical success factor separating the winners from the laggards. Leading BioPharma companies have a strategic and holistic business mindset that comprises all aspects of the product lifecycle: from initial formulation to IT systems and from manufacturing to marketing. This holistic approach is the only way to gain control at every step of the product lifecycle and build the foundation for lasting competitiveness. Cloud-based digital transformation of data management that creates an end-to-end, contextualized view of the whole product and patient lifecycle will be the cornerstone of this foundation.

An example of how key data can be captured into an end-to-end contextualized view of the product lifecycle is illustrated in Figure 2.

About the author

David has more than 30 years of experience in consulting, project management, data management and data warehousing for reporting and analytics applications. He has spent more than 20 of these years focused on pharmaceutical and biotech manufacturing and process development. As part of Skyland Analytics and IDBS, David has been responsible for data management, connectivity and sharing aspects of PIMS as well as technical implementation project delivery. Prior to joining Skyland Analytics, David served as Director, Professional Services for Dassault Systèmes BIOVIA (and its predecessors Accelrys and Aegis Analytical) where he had responsibility for all implementation activities for Discoverant®, the world’s leading informatics software for Life Science manufacturers, and for the Nexus data access and aggregation components of the product. His clients included more than 50 process development and manufacturing facilities worldwide. David has both a BSc in Applied Mathematics with University Honors and a MSc in Statistics from Carnegie Mellon.