Informatics Engine Drives Pharma Development toward Quality by Design
Learning from other industries
|Figure 1: Knowledge Management Enables Continuous Process Improvement|
Analysis of QbD has identified many potential benefits. In terms of quantifiable benefits, value comes from four main areas: a reduction of Cost of Goods Sold (COGS) and capital expense, increased technical development productivity, improved quality (and lower risk) and increased sales. These four combined could potentially provide $20-$30 billion more in profit.1 While the pace has been slow, new International Society for Pharmaceutical Engineering (ISPE) Product Quality Lifecycle Implementation (PQLI)2 guidelines seem to accelerate adoption and are embraced by the FDA and the European Medicines Agency (EMA). Managing knowledge across pharmaceutical development studies, technology transfer activities, process validation studies, manufacturing experience, continual improvement and change management activities are complex subjects. This article explores the informatics journey and its role in driving QbD adoption within Pharma Development and gives some explanation of the concepts behind QbD.
QbD focuses on a better understanding of the processes and the product attributes that affect final drug quality. In doing so, regulatory and business opportunities are provided that can improve product quality and performance through the use of more effective, efficient and robust methods that bridge the entire product development cycle, from research through development to manufacturing.
Simple is Beautiful
Quality by Design is a simple concept. It asserts that inspection does not create quality products, processes do. Thus, design, development and manufacturing processes result in a predefined quality drug. To achieve this QbD requires a full understanding of how formulation and manufacturing process variables influence product quality. Done well, QbD confers competitive advantage. “Quality” becomes something that organizations define for themselves and strive to achieve during every phase of product creation — from research and development to manufacturing and marketing. Witness the near-constant retooling and reinvention of manufacturing processes in the automotive, semiconductor and electronics industries that started with Six Sigma now extending to and fully embracing QbD. The evolution to QbD is a logical next step since QbD is built on top of Six Sigma4,5 and extends into the design space. Quality that is embedded in the design phase of development is a mindset that supports continual improvement. This mindset must be reinforced throughout the company from senior management to the manufacturing shop floor.
|Figure 2: SIPOC process map helps to visualize the scope of a process|
The approach toward defects should be one of avoidance, prevention and resolution. The aim should be to do things right the first time — anyplace, anywhere, anytime — in the product quality lifecycle implementation. Successful QbD strategies include cross-functional development of people across departments, supporting communication and increasing the ability to think in terms of quality. When this attitude is applied throughout the process, defects will be reduced dramatically, and the output of any process will be improved. The farther downstream defects or design errors are detected, the more costly they will become to correct. In all cases, defects should be corrected during the design or manufacturing phase; there should never be a risk of product recalls or harm to the customer. The cost of resolving errors, referred to as the “Cost of Quality,” grows exponentially when detected later in the process — it clearly puts the focus on the design. With this in mind, well planned products with Quality involvement in design, as well as in production, will cost less to manufacture and to maintain. In addition, it will cost less to own or buy the product or service and will improve customer satisfaction.
Y = f(x1, x2, x3, .....)
Output measure or dependent variable (Y) is the lagging indicator while the X is the leading indicator, which means that any X will determine the Y, therefore, we only have control of the Y through the X. When we look at a process, it consists of many steps. The total variation in the output depends on the number of variables and their respective contribution to the total variation. In other words, to fight back the variation in a process, we have to know the different steps (the x’s) and the contribution of the variation of each of them to control the output. The total variation of the output is the sum of the variations of x.
|Figure 3: Creating knowledge from data at Accelrys|
Excellence = Quality * Acceptance. This simple equation illustrates the essence of successful manufacturing. Reaching the top in pharma manufacturing mandates a full understanding of all acceptance criteria, at all levels within the product value chain.
Exploring the Design Space with Informatics
The most obvious shift in thinking proposed by the regulatory bodies during the past decade has been the articulation, exploration and understanding of the therapeutic “design space.” The ICH guideline defines this space as “the multidimensional combination of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality”13 and stipulates that working within the approved design space is not considered a change, while movement out of the space can initiate a regulatory change. As its definition implies, development of the design space involves fully understanding the statistical relationships between input variables (e.g., raw material and active pharmaceutical ingredient (API) attributes), manufacturing process parameters and critical quality attributes (CQAs) of the finished drug product.
The Informatics Journey to Improved Quality by Design
The journey begins with the transition from paper to digital, which includes both the transfer of paper-based processes to “glass” and the identification and adoption of information and process standards to harmonize data exchange. The completion of this step propels organizations down a path towards clean, tractable data, drives out human variability and improves data integrity. Automated laboratory systems speed execution by eliminating the need for scientists to document what they are executing. And vast, rich data sets can be mined and modeled to enable data-driven, predictive control. This helps organizations better understand and describe the variables affecting the critical quality attributes of a product and, ultimately, optimize processes to achieve product and organizational goals more quickly and efficiently — the essence of QbD.
With an informatics system supporting QbD in place, scientists can resolve critical regulatory and QA/QC bottlenecks through the use of existing informatics tools. For example, the informatics system can capture electronically all information describing both the process and the product, including automated audit trails and version control.
Streamlining Standards-based Technology Transfer
The Electronic Laboratory Notebook (ELN) is evolving to adopt compliance with international industry standards such as ANSI/ISA-88 (covering batch process control) and ANSI/ISA-95 (covering automated interfaces between enterprise and control systems), both of which are commonly used in manufacturing. By incorporating these standards and structuring data in a fully searchable format, the ELN enables scientists to mine information from development and manufacturing for improved process and product design. In addition, information is more readily transferable between systems. For example, a recipe delivered in early development can be rapidly transferred to a Lab Execution System for API manufacture and then to a Method Execution System for mainstream manufacturing.14
Addressing Critical Quality Issues – An Example from Biologics
The goal of a successful QbD informatics strategy is efficient, consistent, accurate data capture and re-use that enables an organization to transform data into information, knowledge and, eventually, product and process wisdom. For informatics to support QbD, especially in the process and product development space, organizations need to move away from the paradigm of “How” a product behaves to answering “Why” a product behaves the way it does. This not only moves organizations to understand more deeply what type of variation affects quality; it also provides important clues as to what is the optimal product from a cost, quality and product efficacy standpoint.
Adopt a New Mindset for Quality
Good leadership, outstanding processes and quality-driven culture are the ingredients for making companies successful. Technology just enables the achievement of that goal. Develop people cross-functionally to spread quality across departments, investigate root-causes and learn from quality events. The common element that will catalyze the greatest benefits throughout the development lifecycle is knowledge management. ICH Q10 is a model for a pharmaceutical quality system that can be implemented throughout the different stages of the product lifecycle.15 The integration of meaningful quality metrics such as the Cost of Poor Quality (CoPQ) can substantiate the ROI of adopting the QbD mindset and culture.
1. FDA, Understanding Challenges to Quality by Design, Final deliverable for FDA Understanding Challenges to QbD Project, December 18, 2009.
2. Product Quality Lifecycle Implementation (PQLI®) from Concept to Continual Improvement www.ispe.org
3. FDA, Understanding Challenges to Quality by Design, Final deliverable for FDA Understanding Challenges to QbD Project, December 18, 2009.
5. Ealey, Lance A. (1988). Quality by design: Taguchi methods and U.S. industry. Dearborn, Mich.: ASI Press. ISBN 9781556239700
6. Rosenau, M. (1996) “choosing a development process that’s right for your Company The PDMA handbook of new Product development (77-92 , new York John Wiley Sons).
7. Eckes, G The Six Sigma Revolution. New York John Wiley and Sons.
8. Hauser, J.R., & Clausing , D “The House of Quality”Harvard Business Review 66(3), 63-73
9. Project Management Institute: www.pmi.org
10. MIL-STD-1629 Procedures for Performing a Failure Mode and Effects and Critical Analysis
11. The School of TRIZ http://www.triz.minsk.by
12. Taguchi, G (1987) System of Experimental Design, Dearborn MI
13. International Conference on Harmonization (ICH) Guideline on Pharmaceutical Development Q8 (R2), August 2009, pg. 7.
14. Contribution of Accelrys
15. Guidance for Industry ICH Q10 Pharmaceutical Quality System – April 2009
Peter Boogaard is the founder & CEO of Industrial Lab Automation, and Hans Griep is a quality manager for LabVantage and a six sigma BlackBelt. They may be reached at editor@ScientificComputing.com.