There are few industries that have as many regulatory challenges involving how to process and control its products as the life science industry — pharmaceuticals, biotechnology, medical devices and the contract companies that support them. Some companies may be able to remediate their existing legacy Enterprise Resource Planning (ERP) systems to meet compliance mandates; however, most “IT systems” simply will not support new compliance or global business mandates without significant total cost of ownership challenges. This has led many life science organizations to implement new purpose-built, commercially available software or “secondary solutions” and integrate them into existing ERP technologies. This approach, as opposed to costly development of custom-coding ERP software into operational systems, provides a “best-in-breed” holistic IT system for operational excellence on a global scale. Typical “secondary solutions” include Laboratory Information Management Systems (LIMS), Electronic Notebook Systems (ELN), Lab Execution Systems (LES) and/or Electronic Batch Record (EBR) systems for quality and manufacturing operations.
The controlling system with respect to final product manufacturing and release to the marketplace is the ERP system; however, this system must be data-fed by other workflow automation systems to capture, catalog, specify, track/trace and approve data through the entire raw materials to in-process to final product manufacturing process. In fact, this product lifecycle management process starts at the development stage of the formulation, synthesis or bioprocess and analytical methods creation stages and has an increasing importance with respect to downstream Quality by Design (QbD) operational needs of the industry.
Operational Excellence Requires Operational Data
The mantra in the C-suite for life science companies is “Operational Excellence” from all segments of the supply chain, both internal and external. As companies initiate “lean” or “six-sigma” programs and begin the now-popular externalization of processes that previously were performed in-house — from R&D through pilot operations and now into full CMO-based API production and packaging — executive managers are becoming increasingly aware that their information/data management infrastructure requires updating. Past practices of patching custom-coded business practices into years-old existing ERP and LIMS systems are fast becoming a bottleneck with respect to both the time and costs necessary to complete the task and the compliance overload it creates for validating any custom programming effort.
A key strategic element to a successful Operational Excellence effort is capturing and cataloging the experimental and operational data streams as the product transitions from early phase development through pilot and into commercial operations. These data constitute the foundation for true data management transformation to operational wisdom (see Figure 1).
Figure 1 – The data management requirements for global operational excellence begin at the early stages of a product’s lifecycle and continue through full commercial operations.
Custom-Coding vs. Purpose-built Solutions
The data feeds for operations and the source for operational excellence programs, generally come from a master ERP system and a LIMS, as well as from a large array of paper-based “systems,” be they in Microsoft Word, Excel, lab notebooks or point-workflow logbooks. The data can be difficult to access and generally does not correlate with any true context of daily workflows. Often there is a lot of manual approvals and manual transcription of data/information into other electronic systems. In the life science industry, this translates into a host of compliance risks for data accuracy and integrity and is often the cause of deviations to cGMP guidance. In fact, the majority of FDA-related 483 observations occur because personnel do not accurately follow written procedures.
Often, IT management initiates a large investment in customizations or configurations of existing systems (ERP, LIMS etc.) in an attempt to automate the data capture processes. Again, these customizations require specialists, often from outside the organization as consultants, to define, custom-code and implement solutions that will automate the workflows and data capturing processes and define the compliance and validation tasks required by cGMP regulations. The bottom line is these customizations are difficult to implement and are often too costly in the long run to maintain.
The solution is to seek out purpose-built “secondary” IT systems that are already complete and are installed and validated in a few months versus the year or so needed for customized “solutions.” These secondary solutions include process development ELNs, QC/QA LES, LIMS applications designed for the life science industry, and EBR systems. These product-based solutions versus project-based customizations are the key to short-term success for any operational excellence initiative (see Figure 2).
Figure 2 –Product-based IT solutions outline data capture and databases across the process development and execution environment and search tools to access and report information across the continuum. Product and lot releases from ERP are governed by these “secondary IT systems.”
Quality by Design Provides Agility
A recent operational efficiency initiative, endorsed by regulatory agencies, is Quality by Design (QbD). Under the QbD process, an operational “design space” is developed by using the development data to support an operational window, allowing production to modify or adjust process conditions (i.e., temperature, pressure, pH, etc.) to account for variations in raw materials or process conditions that fall within the operating guidelines of the design space. This provides the ability to adjust manufacturing processes, as close to real-time as needed, to bring product-critical quality attributes (CQAs) into alignment without notifying the agency for approvals. This alone provides operational excellence conditions that did not exist a decade ago. The key IT component for QbD is the development data containing cause and effect relationships useful for QbD correlations during plant operations and events.