The Data Difference

By Geri Studebaker, Aegis Analytical Corp. | November 9, 2012

How CMOs can use data to innovate for better sponsor collaboration

Until now contract manufacturing organizations (CMOs) have been treated as a somewhat homogenous group. The baseline benchmark for outsourcing companies has become delivering a quality product on time and within budget to sponsor organizations, and quality remains the top attribute of CMO selection criteria, according to recent surveys. As demand for life sciences industry CMOs has increased, FDA interest has also increased and the market — comprised both of newcomers and captive pharma companies with excess capacity — has become more crowded, CMOs are beginning to differentiate in numerous ways.

Some CMOs provide specialized services (e.g., drug development, testing or specialized types of manufacturing capabilities) while others have expanded organically or through acquisitions to provide full-service offerings, including development, manufacturing, packaging, formulation and testing services. CMOs also differentiate on their experience, expertise, price and proven track record.

More recently CMOs are looking for innovative ways to demonstrate business value to their sponsors. Likewise, pharma companies are looking less for a vendor-supplier arrangement and more at strategic business partners focused on delivering high-quality products, innovative services and responsive collaboration, allowing sponsors to focus more on their core competencies with reduced regulatory risks.

Big Pharma has demonstrated an increased need for process optimization, consulting and regulatory support, among other services.

We know innovation can come in many flavors. From breakthrough science competencies and better business practices to inventive problem solving and the use of technology, CMOs that embrace innovation for the sake of quality, risk mitigation and best practices stand to gain the greatest competitive advantage.

A recent industry article mentioned the concept of “Open Innovation,” a term coined by Henry W. Chesbrough, which describes the concept of innovation as “porous,” where ideas, innovations and actions flow inside and outside company boundaries to include customers, partners, suppliers and other organizations. Mr. Chesbrough1 discussed how companies are devising strategies to exploit the principles of an open innovation culture, including “. . . exploring ways in which external technologies can fill gaps in their current businesses and looking at how their internal technologies can spawn the seeds of new businesses outside the current organization.”

These open innovation principles apply to the life sciences industry in general and to the evolving relationship between CMOs and sponsor organizations specifically. One way CMOs can innovate is by using technology tools that aid in turning data into science-based knowledge useful for decision making. Incorporating automation tools and the best practices for using them effectively allows for comprehensive monitoring of complex processes in a meaningful context, with minimal human error and decreased resources. By developing such a “manufacturing process intelligence culture” CMOs create real business value for sponsor organizations as well as improve their own operating and quality processes.

In a “closed innovation” data model, both the sponsor organization and CMO keep process, quality and manufacturing data in disparate data “silos” and share only the data required by contractual obligations, without consideration for the changing expectations of regulatory bodies. Closed models are in place typically due to concerns about security and intellectual property, and extracting and analyzing meaningful data from them is a manual process that is resource intensive, time consuming and error prone. The closed model is outdated and insufficient for effective CMO-sponsor relationships. The FDA prefers outsourced manufacturing to be much more integrated into sponsor organizations’ operations, with quality audits conducted by sponsors at CMO sites.

Through an open innovation model, realtime data sharing can happen in one of the following ways:
  1. ACMO can present data to the sponsor using a database, which the sponsor uses with data hierarchies created for analysis and reporting.
  2. The CMO can collaborate with multiple sponsors to design hierarchies for data analysis and reporting as shown in the diagram below, with firewalls keeping restricted data confidential.
In this example, the CMO can leverage hierarchies internally for its own process improvements and to meet its sponsors’ requirements for realtime access to desired data. Both the CMO and its multiple sponsors can leverage shared hierarchies as foundations for analysis and reporting — from raw materials and process analysis to process outcome comparisons. Ideally, as Figure 1 illustrates, the shared data — like the process itself — resides on servers at the CMO site and the shared hierarchies sit behind firewalls between the CMO and its sponsors.

Figure 1: CMO/Sponsor Collaboration

For a CMO that works with multiple sponsors, each with its own set of processes and data requirements, the ability to work with critical process parameters (CPPs) and key performance indicators (KPIs) in a virtual manufacturing environment is extremely challenging yet valuable. CMOs that are able to share process and manufacturing data in real time internally and with sponsors are able to perform proactive process monitoring and investigative analysis collaboratively for better process understanding and control of variability.

Enabling and encouraging a manufacturing process intelligence culture is driven by sponsor needs for realtime process visibility and the need to leverage sponsor expertise quickly in the event of process upsets and inconsistencies. As CMOs work to provide X to sponsor A and Y to sponsor B, a parallel effort can be a full-time job for an employee who ensures the following attributes:
  • The ability to transform paper records from manufacturing processes into compliant electronic records readily available for analysis
  • Tools in place to aggregate and contextualize data from disparate systems (e.g., LIMS, CAPA)
  • A common collaborative technology platform for process intelligence and understanding (data in context in real time)
  • Secure access to data by specific sponsors while excluding access to the data associated with other sponsors.
Maximizing Return on Investment
Considering the large number of sponsors a successful CMO might work with, the volumes of process data can grow exponentially. Managing such a “data deluge” requires technology and human resources that demonstrate return on investment (ROI) for the CMO.

For just one sponsor, for example, a CMO might run only three campaigns per year, with a total of five batches: a small portion of the CMO’s volume, but extremely critical for the sponsor and its end consumers. Setting up a central, collaborative data management and access system for process monitoring and analysis across all sponsors can maximize ROI and minimize CMO time spent.

The following scenario demonstrates the payback a CMO might see for implementing the Process Intelligence platform capabilities in this article for just one process2:

  • 100 unique data/analysis tasks per year per process
  • Each task consumes four hours to perform at $100 per hour
  • 20 processes at the site
  • Data collection and analysis accounts for 80% of the time
  • Technology tools make 75% of the necessary data available
  • Technology tools provide a 90% reduction in the amount of time needed to collect and analyze the data
  • Cost of data/analysis tasks currently = 20*100*4*$100 = $800,000/year
  • Cost savings of tasks with technology tools = $800,000*80%*75%*90% = $432,000/year
  • Greater than $400,000/year potential realized value to the business
  • CMO staff is more productive and less frustrated with the task of gathering data.
  • Staff time is freed up to work on other activities.
  • More timely analysis to meet sponsor requirements.
  • Additional value is realized each time data is accessed to support internal CMO needs or sponsor needs.
How To Transition to a Manufacturing Process Intelligence Culture
With potential ROI in mind, CMOs have a significant incentive to adopt a manufacturing process intelligence culture. CMOs have long been able to gather data for investigational purposes to answer, “What happened?” however time-consuming and error-prone that process might be. A manufacturing process intelligence platform, built with end users in mind, is a fundamentally different approach to manually gathering and using data on a crisis-driven basis. In addition to providing the look-back view, it answers the questions, “Why did it happen,” “What is happening right now,” and, “What is normal?”

Technology enables process intelligence, but a process intelligence culture goes beyond technology to the end users, including sponsor organizations, who are empowered to use data when and how they need it. A company with an advanced process intelligence culture is fundamentally different from organizations that are reactively contending with “spreadsheet madness” or waiting months for resource-constrained IT departments to generate reports.
Enabling a process intelligence culture calls for new ways of thinking about how business groups interact in a virtual manufacturing network. Key attributes include:
  • Effective collaboration between corporate groups (e.g., Manufacturing, Quality, Process Development, separate manufacturing sites) and organizations (CMO-sponsor)
  • User-centric access to different types of data on demand (e.g., discrete, replicate, event-based and continuous) from various sources
  • Agile, forward-thinking organizations that use data-driven science to make technical and business decisions
Process intelligence must be a natural part of day-to-day activities just as much as data collection from on-going operations. Rather than compiling data into fixed reports, it must be available at the click of a button, because process intelligence is needed in real time and it’s as important as the physical pieces of equipment involved in producing a drug batch.

Benefits of a Data-Sharing Culture
CMOs that leverage technology to turn data into process intelligence in real time can differentiate their services in the following ways:
  1. Increased batch yields mean fewer supply chain interruptions and greater profitability to both CMOs and sponsors. Technology tools enable companies to engage in joint cost-reduction initiatives.
  2. Realtime data sharing provides a competitive advantage to sponsors and thus to CMOs (e.g., faster to market, lower overall product cost, avoidance of stock outs, reduced regulatory risks).
  3. Companies are able to collaborate more easily on scale up, process optimization and troubleshooting, quickly identifying root cause for corrective action.
  4. Sharing data and process intelligence greatly reduces risk and leads to operational excellence.
  5. Companies are able to prepare Annual Product Reviews (APRs) and other regulatory filings with significantly less time and effort.
  6. Companies are able to move more effectively with the evolving regulatory guidance for greater collaboration between sponsors and contactors, and for implementation of Stage 3 of the most recent Validation Guidance Update3.
  7. CMOs can better ensure that product delivery meets client and regulatory requirements.
  8. Data sharing across a virtual manufacturing network supports a broader Quality by Design program designed to improve manufacturing processes based on new realtime process measurements and by taking advantage of previous experiences with similar processes.
  9. Greater transparency with sponsors about all aspects of processes and products leads to longer-lasting business partnerships and increased loyalty.
The life sciences industry is experiencing a paradigm shift characterized by pharma companies increasingly utilizing CROs and CMOs for development, manufacturing and other services to supplement and complement their own core competencies. Successful CMOs — across life sciences and other industries — can differentiate themselves by shifting to an open innovation model where realtime data sharing through the use of technology tools provides greater process understanding and science-based decision-making capabilities within their own companies and to sponsor organizations for improved collaboration, increased time to market, cost savings and risk reduction. 

  1. MIT Sloan Management Review, “The Era of Open Innovation.” By Henry W. Chesbrough, Winter 2011. http://sloanreview. mit.edu/files/2011/06/INS0111-Top-Ten-Innovation.pdf
  2. “Aegis Discoverant Manufacturing Intelligence Business Case, Use Case and Payback Playbook,” Aegis Analytical Corp., 2010.
  3. “Guidance for Industry – Process Validation: General Principles and Practices,” U.S. Department of Health and Human Services, Food and Drug Administration, et al., January 2011. http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatory-Information/Guidances/UCM070336.pdf.

Geri Studebaker is vice president, customer operations for Aegis Analytical Corp., a provider of Process Intelligence (PI) software that provides on-demand access, contextualization, analysis and reporting for life sciences manufacturing, quality and process development data. She can be reached at gstudebaker@aegiscorp.com.