TECHNOLOGY

The IT Landscape in Life Science Manufacturing

The move towards process improvement and PAT is encouraging companies to re-think their systems strategy for infrastructure and applications integration.  The previous focus on validation of systems to ensure accuracy, repeatability and stability has enhanced reliability but it has come at a cost.  Once a system has been implemented and validated, there has been a general reluctance to change it unless absolutely necessary.

The requirements for systems validation/qualification (IQ, OQ, PQ) are based on sound principles of software development and implementation.  However, the FDA and the life science industry know that it is time to implement changes that focus not just on the control of systems within a GXP environment but changes which will also enable process understanding and improvement.  As a result, the systems landscape is changing because of the following:

• The realization that shop floor data must be made available to transactional systems, analytical systems and business intelligence tools
• The development and implementation of application program interfaces (APIs) within software packages based on industry standards such as ODBC, OLE-DB and OPC to overcome the myriad of proprietary systems
• The developing interoperability of shop floor controls with ERP solutions known primarily for transactional processing systems
• Hubs or middleware layers “directing” information flow, e.g. IBM (WebSphere), Oracle (Fusion) and SAP (NetWeaver)
• Enhanced capabilities of network operating systems in the area of security, user authentication and management, inter-domain operation and data transmission speeds across LANs and WANs, etc. 
• The development of, and access to, data historians which aggregate data from the controls of production equipment
• Sixty-four bit CPUs, multi core processors and faster disc speeds which have enhanced processing speeds and enabled more effective processing of continuous data

Many pharmaceutical companies are standardizing the desktop and back office environments in order to gain better control and consistency.  As the industry moves towards analyzing more process data (including continuous data), there are new challenges to these standards and some modifications are required.

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Integrated Data Access and Analytics for Process Improvement

An important section of the FDA’s PAT Guideline for process improvement points to the value of continuous learning that comes from the on-demand analysis of process data when coupled with systems that support the acquisition of knowledge from that data:

In fact, the data to which the FDA refers comes not only from process instruments making real-time measurements on the current batch, but also from off-line measurements of the current batch as well as on-line and off-line measurements of previous batches.  All these data from the current and previous batches are essential components of the knowledge base that can be tapped by life science manufacturers to optimize process development and full-scale operations.

Furthermore, these data are fragmented because they have accumulated in the operational data stores of many different systems.  In many life science companies, much of the data also resides on paper records.  These systems have evolved to serve the needs of groups of professionals with training in quality, engineering, operations, technical support, biology, chemistry, etc.  These groups are normally trained in disciplines other than IT and today they rely on others with IT skills to get their data for them.  This causes delays that can often interfere with their effectiveness in developing the understanding of cause-and-effect relationships needed to reduce variability and increase process predictability.  These realities must be taken into account in the process of “knowledge acquisition” to which the FDA has referred.

Although much attention has recently been directed at PAT and quality by design, there are in fact many initiatives underway that share the goal of reducing the variability, cost and risks of life science manufacturing processes.  Despite their different sounding names and different styles of execution, at several points along the way they all depend fundamentally on identifying cause-and-effect relationships among Critical Process Parameters and Critical Quality Attributes using this process understanding to improve process control.  Thus, one of the most critical aspects of many process improvement initiatives is a single point of easy on-demand access to all the relevant data in a context meaningful to diverse groups of users and fully integrated with a collaborative, graphical data investigational analysis and reporting environment for identifying and understanding cause-and-effect relationships in the process data.  This is shown in Figure 1.

Figure 1

All the different data types must be easily accessible in a way that automatically accounts for their different formats and naming conventions, as well as their intra- and inter-batch genealogies.  The access method must let users move directly from on-demand data access into identifying and understanding cause-and-effect relationships between Critical Process Parameters and Critical Quality Attributes without spending excessive amounts of time on programming tasks, and manually collecting and reconciling data.

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Discoverant in the Manufacturing IT Landscape

System interfacing and integration considerations applicable to addressing process improvement initiatives using Discoverant’s on-demand data access and analytics capabilities are shown in Figure 2.  Note that once a model of the relationships between critical process parameters (CPPs) and critical quality attributes (CQAs) has been developed using Discoverant’s cause-and-effect data analysis capabilities, it can be made available in real-time via Discoverant’s Communications Center for access by the manufacturing control systems using web services for real time process adjustment and control.  Note further that meaningful deployment of dashboards requires that the CPPs best monitored in the dashboard must first be identified using Discoverant’s investigational analytical capabilities.

Figure 2

As manufacturing companies adopt a PAT process improvement approach, there is increasing convergence of technology and application integration.  This, in turn, is driving more effective risk-based controls which have a direct positive impact on industry economics through:

• Increased predictability of manufacturing output and quality
• Reduced batch failure, final product testing and release costs
• Reduced operating costs from fewer deviations and investigations
• Reduced raw material, WIP and finished product inventory costs
• Greater understanding, control and flexibility within the supply chain
• Faster tech transfer between development and manufacturing
• Faster regulatory approval of new products and process changes
• Fewer and shorter regulatory inspections of manufacturing sites

Achieving these process improvement and PAT outcomes requires the standardized platform that Discoverant provides for managing the manufacturing process and enabling investigational analysis of the resulting data to improve the predictability and quality of operations and products.  The key is Discoverant’s on-demand access not only to the summary production data but also the individual underlying data elements in a context that is natural to (non-IT) process experts, so that they can quickly identify and understand the underlying cause-and-effect relationships that drive process outcomes.

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Discoverant® Version 3.X Hardware & Server Configuration Recommendations

Discoverant® version 3.X:
Some things you can control.  Other things you must control.  In process manufacturing industries like pharmaceutical and biotechnology, your ability to manage performance within strictly defined boundaries is a prerequisite to success.  Discoverant provides you with the data availability and the means to make informed, proactive decisions that drive operational efficiencies…and the validated environment you need to ensure regulatory compliance. 

Client Hardware

Software
- Operating System Requirements

• Discoverant Client certified for use on Windows 2000 and Windows XP Professional.

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Server System Requirements

Server Hardware

Discoverant does not require the use of RAID, mirrored disk subsystems, nor of fault tolerant hardware systems.
Intelâ Hyper-Threading technology is not supported.

Tablespace sizing is built into the Disk Space estimates above for PRIMR and other Discoverant features requiring the use of a database (such as Auditing).

Software

Operating System Requirements

• Discoverant Server certified for use on Windows 2000 and Windows 2003 Server – Standard or Enterprise.

• Oracle version 9.2.x (9i) or 10.2.x (10gR2)
• Dedicated Oracle Database instance recommended
• Any additional hardware or software requirements required by Oracle

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• IT Landscape
• Integrated Data Access
• Discoverant in the IT Landscape
• Discoverant Server Configuration
• Client System Requirements
• Server System Requirements