Quality by Design is Essential in the New U.S. Regulatory Environment

The United States Food and Drug Administration (FDA) has challenged the pharmaceutical industry to achieve a level of process understanding consistent with controlling process variability and assuring product quality in “real-time” while a batch is being manufactured (RTQA). Ideally, the appropriate level of quality must be assured by the process in real-time despite variations in materials and processing. In the past, such variations would have resulted in unacceptable product batches that were prevented from entering the market only by laboratory testing of the finished product.  In other words, the ability to achieve the appropriate quality outcome must be designed into the process itself rather than relying on final product testing.

This increased emphasis on “Quality by Design” (QbD) requires pharmaceutical manufacturers to make larger investments earlier in the product life cycle during process development in advance of approved commercial operations. The goal is to develop a sound scientific basis for a “Control Space” that accommodates a range of defined variability in the commercial process materials and operations and still produces the right product quality outcomes. 

It is expected that companies who adopt QbD, together with a quality system as described in the draft International Conference on Harmonization (ICH) Q10 document, “Pharmaceutical Quality Systems,” will achieve this “desired state” of pharmaceutical manufacturing. 

The new regulatory environment has highlighted innovative ideas regarding process development and manufacturing and forced us to think about the practicalities of implementing them. It has also focused renewed attention on two very important areas of the process sciences that have been somewhat neglected in the pharmaceutical industry: 1) Design Space Development and 2) Design for Manufacturing.

Design Space Development in QbD

Process development results in the definition and approval of a “Control Space” within the universe of possibilities about a process called a “Knowledge Space.”  The approved manufacturing process can be operated within the Control Space to produce material that meets the required specifications for identity, potency, quality, etc.

As the product matures in its life cycle, scale-up, economic and/or other factors can require changes in the control scheme for the process, moving it from Control Space 1 to a new Control Space 2. The scientific basis for Control Space 2 is usually developed out of necessity to cope with process shortcomings long after the original process development work was done. It needed to be reviewed and approved by the regulatory authorities before the process could be operated commercially in the new Control Space. This could be a costly and inefficient process because it could trigger the need for new clinical studies. Furthermore, many of the expert resources that developed and gained approval for Control Space 1 could have moved on by the time Control Space 2 was needed. (Shown in Figure 1.)

Figure 1

With the publication of ICH Q8, the opportunity now exists to develop an approvable Design Space in advance of commercial launch that anticipates and accommodates more than one Control Space. This allows manufacturers to make changes that move the process from Control Space 1 to Control Space 2 as necessary without the need for regulatory approval, provided that both Control Spaces remain within the approved Design Space.  (Shown in Figure 2.)

Figure 2

To realize the benefits of such a Design Space approach, the process development and manufacturing teams must develop and document in the Chemistry Manufacturing and Controls (CMC) section of the regulatory submission the scientific basis for approval of the Design Space. The information used for this work comes in part from appropriately designed experiments that define and test the outer limits of the intended Design Space to understand the effects on the Critical Quality Attributes (CQAs) and define and characterize any new Critical Process Parameters (CPPs) that might arise in a new Control Space. At the time of process start-up and tech transfer, data and information about the Control Spaces – as well as the Design Space within which the Control Spaces – operate must be readily available so that it can be transferred to the manufacturing operation.

Design for Manufacturing in QbD

Developing and gaining approval for a Control Space and/or a Design Space depends on fully utilizing the prior knowledge and experience of the process development and manufacturing teams. Data about the way previous processes behaved when subjected to the constraints of full-scale commercial operations is a vital source of guidance for designing the next process to operate successfully within those constraints. The FDA has coined the term “Design for Manufacturing” to describe the utilization of this process information to consistently achieve an acceptable quality standard.

This type of information is derived from accessing and analyzing the actual data about prior manufacturing processes operating under the same or similar conditions as the process currently under development. These analyses monitor, identify and correlate the relationships between CPPs and CQAs under the well controlled, full-scale operating conditions used during commercial manufacturing.

Other sources of valuable information used in Design for Manufacturing are data about the relationships between CPPs and CQAs collected during process upsets. Such upsets are the practical equivalent of full-scale experiments that can also reveal previously unrecognized CPPs, the control of which also needs to be built into the next process. Failure to utilize these valuable sources of information to design the next process appropriately for full-scale manufacturing conditions is generally due to 1) databeing not collected and/or not made available for analysis, 2) the fact that CPP information is too deeply buried in the oceans of in-house data or 3) the fact that  a culture of collaboration and continuous process improvement is absent or lacks the proper supporting technologies.

Achieving QbD through Process Understanding and Technology

Successful approaches to real-time quality assurance require that the CPPs driving variability in the CQAs be identified and understood during process development so that they can be measured and controlled in real-time during the manufacturing process. This is what the FDA means by “Process Understanding.” It requires a culture of continuous improvement without the need for regulatory intervention to approve changes, and close collaboration between the process development and manufacturing teams along with deployment of appropriate enabling technologies. As an added benefit, this collaboration has the potential to drive the adoption of better practices and sustain the business benefits of higher process predictability and quality compliance across the entire global manufacturing network.

Many pharmaceutical companies have been standardizing the desktop and back office environments in order to gain better systems control and consistency. As the industry moves towards collecting and analyzing more process data (including continuous or on-line data), new challenges to these standards are requiring new thinking.

One trend of note is data warehouses. Typically, these are not real time and do not contain continuous data. However, they can contain significant elements of data useful for identifying trends or causes of process variability. Process improvement initiatives must be able to take advantage of the information content of this warehoused data together with the data from newer (PAT) instruments and other on-line measurements.

Process improvement and PAT need a framework for managing the manufacturing process and enabling collaborative investigational analysis of the resulting data to improve the predictability and quality of operations and products.  The key is to provide on-demand access to not only the summary production data, but also to the individual underlying data elements in a context that is natural to users who are (non-IT) process experts. This improves the speed and depth with which they can identify and understand underlying cause-and-effect relationships.

Three important technology aspects to consider for successful QbD include PAT, on-demand data access and collaborative analytics.

1) PAT

Achieving QbD may involve the use of instruments more sophisticated than those currently used in pharmaceutical manufacturing processes. Some of these instruments have been used for decades in other industries, but have not yet been applied to pharmaceutical production processes. Some of the newer instruments available to life science manufacturers make relatively simple measurements like effusivity. Other instruments make much more complex measurements like Near Infrared (NIR) absorption. In many cases, these instruments are capable of measuring the CPPs and CQAs in real-time. Such instruments generate large amounts of data that must be understood if the measurements are to be useful.

The usefulness of any PAT or other process improvement initiative in QbD depends on all the data (discrete, replicate, continuous and paper-based) and the right process trending, reporting, descriptive analysis, univariate and multivariate cause-and-effect analysis, and parameter relationship modeling capabilities all being easily available on-demand to users in the same integrated environment. Users must be able to work with continuous, discrete and replicate data together for quantitative analysis. 

2) On-demand Data Access

A critical success factor for process improvement is easy, on-demand access to all the data and data types in the infrastructure systems, including Supervisory Control and Data Acquisition (SCADA), Laboratory Information Management Systems (LIMS), Enterprise Resource Planning (ERP), Manufacturing Execution Systems (MES), and Electronic Batch Record (EBR) systems and other manufacturing control and data acquisition infrastructure systems. On-demand access allows a multi-disciplinary team of users can extract the information in context and use it to understand cause-and-effect relationships. Thus, the technology platform needed for QbD and its associated process improvement initiatives must allow immediate user-centric access to all the process development and manufacturing data sources and data types so that their value can be leveraged together with data from newer (PAT) instruments. The data must be available on-demand to end users in the same working environment with the analytics, visualization and reporting capabilities that allow exploration of cause-and-effect relationships in collaborative multi-disciplinary teams of process development, manufacturing and quality users that work across geographic locations.

The different types of data 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 data access method must let users move directly into identifying and understanding cause-and-effect relationships between CPPs and CQAs without spending excessive amounts of time on manual programming tasks or manually collecting and reconciling data. This is the modern replacement for what is so often the “spreadsheet madness” that occurs today when things go wrong and the process needs to get back on track under crisis conditions.

3) Collaborative Analytics

Collaboration between individual participants in the process development, manufacturing and quality teams requires that the technology platform they use to access data and share ideas must be useful to the diverse group of users found on these teams - production engineers, statisticians and quality professionals -who are typically not programmers.

Practically speaking, it is not sufficient to provide capabilities that only those comfortable with writing their own SQL queries or thinking in n-dimensional space can use.  Basic as well as advanced analysis capabilities must be available in the same environment where the data is accessible on-demand directly by end users. This collaborative environment must also allow users to share their results easily and build on each other’s findings regardless of where in the world they are each located. In this way, the best thinking can be harnessed to the work of QbD and the results can be more easily institutionalized so that “the wheel does not need to be re-invented.”

The Bottom Line Benefits of QbD and Process Understanding

When fully implemented, QbD means that all the critical sources of process variability have been identified, measured and understood so that they can be controlled by the manufacturing process itself. The resulting business benefits are significant:

• Reduced batch failure rates, reduced final product testing and lower batch release costs
• Lower operating costs from fewer failures and deviation investigations
• Increased predictability of manufacturing output and quality
• Reduced raw material, WIP and finished product inventory costs
• Faster tech transfer between development and manufacturing
• Faster regulatory approval of new product applications and process changes
• Fewer and shorter regulatory inspections of manufacturing sites

These benefits translate into significant reductions in working capital requirements, resource costs and time to value. The bottom line gains, in turn, pave the way for additional top line growth.

For more information please visit www.aegiscorp.com or email info@aegiscorp.com.