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Home > QbD Viewpoint
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10/16/2008
In the previous blog on "Transitioning to QbD," it was noted that QbD workers will need an accessible modeling and simulation environment for QbD to achieve a central role in pharmaceutical development. Discussions with workers indicate that they are currently using a bewildering array of software, which was not designed for QbD. Some software like DOE software is highly restrictive and performs a limited subset of QbD tasks. Other software like general purpose statistical analysis software is too broad and performs many more tasks than necessary for QbD. The net result is that today QbD work processes involve a complex combinatorial array of software to get the QbD work done.
Unfortunately cobbling together software from a variety of internal and external sources, illustrated above, has led to an array of problems and shortcomings including the following:
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QbD workers get a lot more features than they need for completing QbD tasks.
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There is huge learning and retention problem for QbD workers.
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QbD workers are faced with complex data integration and management issues.
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There is no audit trail of how the results were obtained.
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Assembling QbD results require inefficient and non-reproducible cut-and-paste processes.
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There is no easy way to extend the functionality of the software as functions are hard wired in.
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And, the situation is costly to set up and maintain.
My experience in developing and implementing electronic lab notebooks in pharmaceutical R&D as replacements for the paper notebook (P. van Eikeren, "Intelligent Electronic Laboratory Notebooks for Accelerated Organic Process R&D," Organic Process Research and Development 2004, 8, 1015-1023) made it clear that pharmaceutical R&D workers expect focused software tools, devoid of extraneous functions, designed to help them get their jobs done. General purpose tools simply won't do. In response to this need, Blue Reference has initiated a project, entitled Inference for QbD project, directed at development of a software umbrella appropriate for implementation of QbD practices. The Inference for QbD project, described on the website at www.InferenceForQbD.com, encompasses a number of novel elements including the following:
- A comprehensive software solution for the implementation of QbD practices in a pharmaceutical environment;
- It is being constructed on the patent-pending Inference platform developed by Blue Reference;
- Users access it through the easy-to-use and familiar user interface of Microsoft Office;
- It is being progressed from technical feasibility to development in a project funded by the National Science Foundation (NSF) Small Business Innovation Research (SBIR) program; and
- It is being development into a commercial product within the context of a consortium of pharmaceutical companies whom are providing guidance on requirements and testing prototypes in production settings.
 Pharmaceutical customers tell us that Inference for QbD should address the needs of a broad audience and enable guided decision making-that is, analyze development and manufacturing data within the context of QbD objectives with the intent to identify the best way to move pharmaceutical development forward. The resulting implementation, illustrated in the figure above, represents a greatly simplified approach to the implementation of QbD practices. Specific benefits include the following:
- Capabilities of the software are tailored to QbD requirements and can be further tailored to company-specific best practices;
- Users only see the functions that they need to get the job done without extraneous distractions;
- Users experience a shallow learning curve as a result of using the familiar Microsoft Office interface;
- Data preparation and management are tightly integrated;
- The software enables concurrent data assembly and preparation, analysis and documentation, thereby providing an audit trail of how the results were obtained;
- The software generates QbD results documents automatically with the press of a single button;
- The software is adaptable and extensible using the Inference platform SDK allowing for future extension and re-direction; and
- The software is inexpensive to set up and maintain because it is based on Microsoft Office, which is already deployed.
Demonstration of these benefits through relevant, illustrative examples will be the subject of future blog entries. 10/6/2008
Companies shifting to science-based manufacturing are taking their first steps by transitioning from quality-by-inspection to quality-by-design (QbD). From the FDA's viewpoint, the principles of QbD in pharmaceutical development are fairly straightforward. It requires establishing a clear linkage between the safety and efficacy of the drug product in the patient with its quality as defined by the attributes of the drug product and then linking it all the way back to the process for preparing the drug product. Specifically, QbD requires achievement of understanding at two levels as illustrated below:
- Clinical Understanding, which establishes a link between the attributes of the drug product and safety and efficacy in humans; and
- Process Understanding, which establishes a link between the attributes of the drug product and process parameters, process attributes and material attributes of the active pharmaceutical ingredient (API) and excipients that go into the drug product.
From a practical standpoint, process understanding, and the associated design space, entails:
- identifying and explaining all critical sources of variability;
- managing variability by the process via measurement and control of critical process variables; and
- reliably and accurately predicting and controlling product attributes within specifications (achieve quality).
Sounds simple enough, but arriving at process understanding and putting it to work remains a tall order. Towards that end, the FDA suggests that modeling and simulation must play an increasingly important role in science-based manufacturing-that is, from its current supportive role in empirical-based pharmaceutical development to a central role in QbD-based pharmaceutical development. For modeling and simulation to fulfill this promise will require development and deployment of three critical elements:
- A QbD Modeling and Simulation Infrastructure. An easy-to-use system for assembling, transforming, exploring, analyzing and reporting on QbD data.
- A QbD Work Process. Guidelines and work processes for deciding where and how modeling and simulation should be carried out in QbD. Such work processes typically involves risk assessment, experimental planning, prioritization, and data analysis and documentation.
- Organization and Culture. Establishment of integrated, multidisciplinary, multifunctional development teams trained in the use of QbD modeling and simulation for decision making.
Transitioning to QbD is reflective of a larger business trend aimed at fact-based decision making. In Competing On Analytics, Thomas Davenport of Babson College and Jeanne Harris of the Accenture Institute of High Performance Business outline a roadmap to company competitiveness by wielding analytics. Access to data is not enough. "Even if an organization has some quality data available, it must also have executives who are predisposed to fact-based decision-making." And, management support is critical. "A data-allergic management team that prides itself on making gut-based decisions is unlikely to be supporting. Any analytical initiatives in such an organization will be tactical and limited in impact." 1/29/2008
The International Foundation Process Analytical Chemistry (IFPAC) completed the first day of their 22nd International Forum on Process Analytical Technology covering a full cross-section of subjects focused on the application of Process Analytical Technology to QbD. Leading the Plenary lectures was Dr. Janet Woodcock, Deputy Commissioner, FDA on the subject of the FDA’s Critical Path Initiative.
Woodcock provided new, sobering statistics on the state of today’s pharmaceutical industry including:
- Mergers and acquisitions have decreased the number of new drug candidates because post-merger, similar candidates are dropped.
- New compounds entering Phase I clinical trials have only an 8% chance of reaching the market versus a 14% chance 15 years ago.
- More worrisome is the fact, even after significant investment and “sunk costs,” the Phase III failure rate is now 50% versus 20% a decade ago.
- Costs continue to escalate and companies are less willing and able to bring new candidates forward.
- Today 65% of all prescriptions are generics and there are tremendous challenges in getting them to market and ensuring that they are truly equivalent to brand-name drugs.
- Although there has been huge private and public investment in basic research and specific product development, there has only been minor investment in tools and public standards to aid development.
Woodcock indicated that in 2004, the FDA concluded that “…basic research isn’t enough. We have to look at the critical path that a product must follow before it is introduced to market...The science required to evaluate new product safety and efficacy and to enable manufacture is different from basic discovery science.” The outgrowth of that effort was the Critical Path concept, with quality-by-design as a core feature.
Woodcock made a number of suggestions for insuring the success of the Critical Path concept including:
- We need to build a generalized knowledge base of manufacturing science.
- Industry needs to share their existing knowledge and databases. This information should not stay within the walls of one organization, but must be shared to develop generalized knowledge.
- Sharing will be required in order to develop public standards that will enable evolution of the field for everyone to use.
Woodcock explained, the 21st Century GMPs document was a prototype for the larger Critical Path Initiative. “We are accelerating the pace of introduction of new science and technology,” she said. “PAT was the poster child. It may be a small piece of the picture, but is emblematic of the problems facing the industry.” She added, “We want to move from empirically derived trial and error methods (e.g., formulation, excipient selection) to rigorous, mechanistically based and statistically run processes. We need to break down silos between R&D and production and to be less conservative.”
She noted that the challenge of linking product attributes to clinical outcomes continues. There remains “a disconnect” between clinical and manufacturing sides, Woodcock said, adding, “We still don’t know how the attributes that you measure and control for pharmaceuticals actually control the clinical performance, or the extent to which manufacturing failures adversely affect clinical outcomes.”
Today, the drug safety debate is centered on intrinsic safety problems inherent to the drug itself, rather than errors in manufacturing. However, Woodcock said that suboptimal formulations occasionally get to market and fail to improve over time. She noted that “…for science-based manufacturing, you need to have a better idea of how parameters impact clinical performance and to exercise tighter controls.” 1/28/2008
The FDA has recognized that product development is now the weak link in the “critical path” from scientific discovery to commercial product. In response, the FDA has instituted sweeping changes that are beginning to have a tangible impact on the way pharmaceutical developers and manufacturers conduct their business. FDA’s “Pharmaceutical Quality for the 21st Century—A Risk-Based Approach” squarely takes aim at the current state and replaces it by the desired QbD state focused on product and process understanding.
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Aspect |
Current State |
“Pain” to industry |
Desired QbD State |
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Pharmaceutical development |
empirical; typically univariate experiments |
unscientific; relies on intuition; high risk of failure |
systematic; multivariate experiments |
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Manufacturing process |
locked down; validation on 3 batches; focus on reproducibility |
ignores variation; high risk in moving to manufacturing |
adjustable within design space; continuous verification within design space; focus on control strategy |
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Process control |
in-process testing for go/no-go; offline analysis |
slow response; lost batches; drug development delay |
PAT utilized for feedback and feed forward in real time |
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Product specification |
primary means of quality control; based on batch data |
difficult to achieve right-first-time production |
part of overall quality control strategy; based on product performance |
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Control strategy |
mainly by intermediate and end product testing |
delays batch release; low equipment utilization rate |
risk-based; controls shifted upstream; real-time release |
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Lifecycle management |
reactive to problems & OOS; post-approval changes needed |
inefficient, costly processes; discourages changes |
continual improvement enabled within design space |
Quality by Design (QbD) provides “a framework for allowing regulatory processes to more readily-adopt state-of-the-art technological advances in drug development, production and quality assurance” and shifts focus from “quality by testing” to “quality by design”—that is, build quality into the process rather than rely on resource-intensive quality control systems to prevent defective products from leaving the factory. QbD practices are implemented at four levels:
· QbD Level 1: Process Understanding. Develop end-to-end process understanding based on multivariate analysis of designed experiments and/or historical data. Includes: (a) identification and characterization of critical-to-quality process parameters (CPP) and (b) identification of root causes of variability.
· QbD Level 2: Quality by Design. Design a process defined by a design space that is robust and where the variability is controlled.
· QbD Level 3: Monitor, Predict and Control. Monitor CPPs via off-line, in-line or on-line analyzers. Use real-time monitor feedback in conjunction with prediction to achieve process performance/quality supervision via real-time intervention of CPPs.
· QbD Level 4: Continuous Improvement. Use accumulating manufacturing data as the basis to modify and improve the process within the design space.
At this stage, the underlying concepts and rationale for implementing quality-by-design practices are well understood and accepted. Despite this progress, there remain critical impediments to QbD implementation including the following:
· From a practical standpoint, what comprises and how does one acquire process understanding?
· From a practical standpoint, how does one decide that a process parameter is critical?
· And most importantly, how do we know that the identified design space of the process links to the clinical design space of the patient? After all, the aim is to design a process that meets the needs of safety and efficacy for the patient.
These key questions will be the subject of subsequent posting. 12/13/2007
According to findings of the largest empirical study ever performed on the interplay of pharmaceutical manufacturing and the Food and Drug Administration (FDA), the pharmaceutical industry is wasting more than $50 billion a year in manufacturing costs—costs that could be better applied to lower prices or increased research and development. The study, conducted jointly by Olin School of Business at Washington University and McDonough School of Business at Georgetown University, received no funding from either the pharmaceutical industry or the FDA.
The goal of the study was to understand how the FDA regulates pharmaceutical production and to see where there may be conflicts that inhibit advances in manufacturing. The researchers collected data from 42 manufacturing facilities owned by 19 manufacturers. They studied the companies’ manufacturing performance in terms of cycle time, frequency of deviations, reasons for deviations, yield, and improvement rates on key manufacturing metrics.
The FDA, to its credit, has openly acknowledged that historical compliance prescriptions have had unintended side effects—namely, discouraging manufacturers from embracing new technologies and process improvements that address the findings above. The costs and risks associated with change, as a consequence of stringent validation and regulatory re-filing requirements, have been perceived by manufacturers as being too high. In response, the FDA has recently instituted sweeping changes that are transforming the way life science companies look at their research, development and manufacturing process engineering organizations. Central to these changes is the overarching Quality-by-Design paradigm, a centerpiece of the FDA’s “Pharmaceutical Quality for the 21st Century—A Risk-Based Approach.”
However, despite the bad news regarding manufacturing inefficiency, the study did identify two positive factors that temper manufacturing inefficiency: lending support for implementation of Quality-by-Design initiatives by using a comprehensive solution like Inference for QbD. Specifically,
· Application of information technology correlated with superior manufacturing metrics. Companies that employed information technology to electronically track and report on manufacturing (people, processes and deviations) and centrally stored all their data, uniformly displayed superior manufacturing performance relative to those not using such information technology.
· Driving decision-making down the ranks results in higher overall manufacturing performance. This important goal is supported by Inference for QbD. Increased capacity for employees in lower ranks to make decisions directly correlates to gains in manufacturing performance. This is especially true when considering matters related to deviation management, lot failure, lot review and process validation.
Reference: J. Macher and J. Nickerson, “Pharmaceutical Manufacturing Research Report: Final Benchmarking Report,” McDonough School of Business (Georgetown University) and Olin School of Business (Washington University in St Louis), September 2006. 12/11/2007
Welcome to QbD Viewpoint, a blog focusing on how Research, Development and Manufacturing in the process industries affect and are affected by the Quality-by-Design paradigm.
Biotech and pharmaceutical firms, who operate in the stringent environment of FDA regulations, will be the primary focus. However, I will aim to cast a wider net to include other relevant industries as well.
Quality-by-Design has received a strong shot in the arm by the FDA’s 21st Century GMP initiative. The business needs for Quality-by-Design and the FDA’s regulatory response to those needs have been relatively well spelled out. However, the devil is in the details. What is not well spelled out is how one implements and executes a program in Quality-by-Design to achieve both business and regulatory objectives. This blog will be focusing on those issues. |
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