How Data Integrity Helps CDMOs Attract Partners

Data Analytics

Dec 17, 2020 | 10 min read

In spite of the increasing level of importance regulatory agencies place on data integrity and security, many small and large CDMOs don’t have the right systems in place to manage their data properly. Continuing to manually extract, manage and share data with partners isn’t going to be sustainable or viable in the long-term. So how can CDMOs move to the next level and digitalized their data management and processes?

^{_{This article is posted on our Science Snippets Blog.}}

Moving Towards Digital Data Management

From manually extracting data to doing batch review processes on paper a lack of efficient and digital data management processes can hold CDMOs back in terms of achieving trust and data transparency with their partners.

In addition to operational inefficiencies, this can lead to a delay in business-critical processes, including areas such as tech transfer, transition to scale-up, IP transfer between partner and CDMO, as well as regulatory approvals. Lack of data accessibility can result in inefficiencies that lead to things like longer cycle times, lower yields, and ultimately reduced throughput of a facility.

Data integrity and transparency is also becoming more important as part of CDMO selection criteria for pharmaceutical companies. A reputation for transparency and integrity of data, as well as providing access to data all the way through the process, can be essential to succeed in this increasingly competitive marketplace.

Try the ROI calculator

Assigning the Right Data Models When it comes to creating data models in the life sciences arena, there are three key dimensions that come into play. These include:

Equipment Model. It’s important to consider the different type of tools, equipment and systems that will be used and will need data integrated. For example, the data coming from an Ambr^® system in PD, a chromatography skid in manufacturing, or a laboratory test can be fundamentally different in types, formats, and volumes.
Product Lifecycle Model. When it comes to process and product development, tech transfer and getting to market as fast as possible are very important for CDMOs. As the time-to-market pressure increases, CDMOs are forced to continuously speed up all operations – including data transfer.
Process Model. Here you want to ensure that you follow industry standards like S88/95 and consider the entire value stream of the process. You should be able to identify a batch, sub-batch or subunit, and follow the drug substance from the early stage all the way through to, for example, a prefilled syringe, and have the data to manage full traceability and full serialization through all the steps.

For many pharma companies and CDMOs, being able to digitalize all their data from all different types of systems and structures in a fully hierarchical way is the first step of a larger goal. Becoming a fully digital facility, or even taking this a step further by creating a digital twin system.

OSIsoft PI System for Data Management

OSIsoft has been on the market for more than 40 years and has a strong installed pharma customer base with a proven data collection, storage and contextualizing infrastructure that can handle very large amounts of data from various areas from R&D to production.

The OSIsoft PI system is used for connecting, storing and enhancing the available data from many different systems and solutions, including components of LIM systems and IOT platforms. OSIsoft helps to aggregate data, and even the metadata, in a way that it can be used to translate information into action and create value from it.

Integrating Global Data

One example of creating data transparency through digitalization at a truly global level comes from Johnson & Johnson with a case they presented at the PI World User Conference. Johnson & Johnson reported that they had to manage over 2000 SKUs and recipes and track more than 18,000 raw materials. Additionally, they had to manage operations at 29 sites internationally, along with 130 CMOs and CDMOs – a very complex chain, where no single product is completely produced inhouse.

Using the OSIsoft PI system, they built an integrated data management system that provided full access to all the different pieces. They were able to gather energy data, maintenance data and quality management data, and even develop additional analytics applications on top of these assets that were available for research, troubleshooting and reporting.

The Steps Involved

So how can you get there? To simplify it, there are three steps involved.

Simplify data collection. The first step is to connect all the equipment. You need to make everything visible and have access to all process and performance data.
Enhance accessibility. Improve the data quality and data accessibility so that every person who needs to use and consume the data has access. Build trustworthiness.
Increase productivity. Use the data to deliver increased productivity, improve quality and performance through analytics. An example of this is using a tool like SIMCA^®-online for golden batch prediction as well as prescriptive analytics.

Watch the webinar

Creating Simplification

When it comes to creating simplification, let’s look at another example is from OSIsoft and AstraZeneca/MedImmune where they set up a fully cloud-based system to connect a number of different sites from Cambridge, UK to Gaithersburg, MD. One requirement of this system was that they needed to collect data from all their different plants, including their R&D sites, and from all the different types of equipment (such as bioreactors, freezers, and chromatography systems). This data needed to be contextualized into one node of data and shared as single source of truth for all the different applications.

Using OSIsoft PI System, they were able to convert all the data into a system that is fully validated and fully compliant to meet the different type of requirements for visualization and troubleshooting. In this case, the simplification of data helps AstraZeneca in reducing time to market and establishing very robust processes for operational excellence. The next step for them is real-time data monitoring with SIMCA^®-online

Data Integrity

Data integrity is essential for regulatory compliance. For many pharma companies, making their central data collection system a single source of truth is a way to become fully compliant in meeting different types of requirements, like CGMP for example.

Using the PI System together with SIMCA-online, BioMarin moved from a paper-based environment to full electronic batch recording, allowing them to automatically generate exception-based reports along with details for investigations or out-of-specification reports. This includes details for audit trails and audit trail reviews, as well.

This type of data integrity is essential for pharma companies, and many are requiring their CMO or CDMOs to be able to support this level of data.

Read more: How to ensure data integrity and compliance of your data analytics systems

Productivity

WuXi Biologics, one of the world’s leading CDMO vendors, initiated a digital transformation project with a focus on using data to increase productivity and improve quality.

Their efforts focused on how to reduce the downtime of equipment and improve their overall data integrity. The project looked at a number of plants in China and abroad, streaming both GMP and non-GMP data. The plants varied in their processes for pre-fermentation, fermentation, harvesting and downstream operations. Utilizing the PI System, Wuxi was able to follow each process step and sub-batch. The data generated in each process step was available in real-time, and historical data was accessible for additional analytics and reporting applications.

At Wuxi Biologics, the PI Asset Framework allowed for data collections from over 100 different systems and contextualized the data for analysis and visualization.

Another benefit of this level of data modeling is the ability to predict when equipment maintenance needs to be performed. At Wuxi Biologics, rather than running on a standard maintenance calendar schedule, they were able to perform and track maintenance based on specific usage metrics (such as number of times a certain valve was opened). This was only made possible by having a systemized way to store and track their data.

At the 2019 PI World Regional Meeting in China, WuXi presented that as a result of implementing a smart data management system, they were able to improve internal productivity significantly. In their next steps towards plant digitalization, Wuxi Biologics plan to implement more advanced data analytics tools like AI and real-time statistical process monitoring.

Watch the webinar

Using Data for Expedited Batch Release

Finally, let’s take a look at a case study published by FUJIFILM Diosynth Biotechnologies in BioProcess International. It discusses challenges the company had during the chromatography review process. Process verification and batch review are often lengthy and resource-intensive processes. This process is made even more difficult when a facility doesn’t have any sort of electronic data management system and relies on paper records that need to be manually reviewed.

At FUJIFILM Diosynth Biotechnologies, the standard practice of chromatogram review relied on “trending univariate parameters (e.g. chromatographic peak asymmetry and product yields) and on performing a qualitative visual comparison of chromatography profiles against a reference batch” [1]

In order to address this challenge, FUJIFILM Diosynth Biotechnologies used SIMCA and SIMCA-online (with the PI System as the data backbone) to enhance their review process using multivariate data analysis (MVDA).

Benefits of a Data Analytics Approach

The benefits FUJIFILM Diosynth realized by enhancing their chromatography review process with data analytics were:

Shortened review times
Optimized resource expenditures
Reduced paper footprint ~10,000 sheets per year
On-demand data accessibility and sharing capabilities with their partners
Increased partner trust and collaboration opportunities

FUJIFILM Diosynth Biotechnologies, as well as a number of other Umetrics^® users, utilize MVDA to analyze chromatography data because it allows for the detection of small process deviations and peak shapes, where the manual traditional review process falls short. SIMCA^® can pick up deviations in batch consistency, cycle consistency and also to identify any process failures or other trends before they become problematic, in other words, before they produce impurities or in the worst case, cause batch loss.

Integrating data analytics tools like MVDA within the quality assurance framework helps to bring companies one step closer to achieving real-time release testing (RTRT).

Interested in learning more about how MVDA can be used to optimize your chromatography review?

Read this Umetrics User Meeting Presentation from the Pall Corporation.

Integrating Data Management and Data Analytics at CDMOs

The first step in digitalizing a CDMO plant means implementing a framework that includes both data management and data analytics tools. For many companies, there will need to be a shift in culture, new data-driven departments must be formed, and leaders must incorporate data-driven insights as a key enabler for the success of the organization.

Here’s what this framework for implementing data-driven insights with Umetrics looks like:

Model the historical data. Use SIMCA^® to develop reference models based on historical process data (such as control data, analytical data and raw material data), which helps expedite root-cause analysis investigations and batch review processes.
Connect real-time data. Use a data management system such as PI to collect data in real-time for process monitoring and connect it to the SIMCA^® models based on the original data sources.
Monitor in real-time. Use SIMCA^®-online to monitor incoming batches so operators can track the process in real time and identify any process deviations from the plant floor or from a control room.
Share data online. Using the web client, share SIMCA^®-online dashboards with CDMO partners, providing them with a rea-time window into their processes.

SIMCA multivariate data analysis software can enable digital chromatogram review, facilitating simultaneous analysis of multiple chromatography phases and parameters.

Figure: System and data communication diagram; historical data available in the data historian are used as reference batches and to build the digital chromatogram project. During production, data ingested from the manufacturing SCADA system to the data historian (OSIsoft PI) are transmitted to SIMCA-online through SimApi at real-time frequency. The visualization dashboards are made available to operators, quality assurance personnel reviewers, and the partner. (Image source: BioProcess International)

At FUJIFILM Diosynth Biotechnologies, partner trust and collaboration is an integral part of their mission. Their chromatogram dashboard is therefore easily accessible not only internally but also to their partners. Having a clear strategy when it comes to data connectivity and transparency is a key enabler for this level of partner trust.

With a connection to the data sources and clear transparency between the process, the data sources and the analytics layers, you have a true sense of putting your data to work in a transparent and efficient way.

ROI Calculator for Advanced Service Offerings

Sartorius Data Analytics has developed an ROI calculator that allows CDMOs to calculate the ROI by implementing SIMCA-online as an advanced service offering for their partners.

Wondering how to calculate your ROI?

Try the ROI calculator

Acknowledgements

Thanks to Martin Jensen and Ricardo F. Caroço from FUJIFILM Diosynth Biotechnologies in Hillerød, Denmark.

References

Enabling Digital Chromatogram Review for a Faster and More Reliable Operation, M. Jensen, R. Caroço, BioProcess International, Industry Innovators, p. 30, 2020-2021

Webinars for CDMOs

Watch a webinar to learn more about various service models and ways to achieve ROI.

How Data Analytics Can Boost the Bottom Line at a CDMO

Watch webinar

How CDMOs Can Leverage Their Most Important Asset: Data

Watch the webinar

Register for next webinar: How CDMOs can Adopt a Modular PAT Strategy