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Quality by Design

Wed, 02/20/2008 - 4:34am
Shortening the Path to Acceptance

By Ronald D. Snee, Philippe Cini, Jason J. Kamm, Chester A. Meyers, Tunnell Consulting


Consider the contrasting fates of two life-saving innovations. In 1601 British naval officer Sir James Lancaster discovered that citrus cures scurvy and recommended that lemons be kept on board British vessels. But it was not until 1865 - 264 years after the initial discovery - that the practice was adopted. By contrast, Joseph Lister's innovations in sterile surgery, despite some initial resistance, achieved almost universal acceptance within a dozen years of his first published findings. Like Quality by Design (QbD), both of these breakthroughs were techniques for pre-empting problems, yet one traveled a long and winding road to acceptance while the other enjoyed a far more direct route.

Because QbD, like Lancaster's and Lister's innovations, is science-based, its acceptance is likely to be less a question of whether than of when. But where QbD currently stands between the two extremes of adoption - unconscionable delay or immediate success - is difficult to say. But given the potential for value that QbD can produce for a beleaguered industry, it is clearly in the interest of pharmaceutical companies to shorten the path to acceptance. They can do so, first, by recognizing the value; second, by understanding the trends that are likely to give QbD additional momentum in the industry; and, third, by taking measures to overcome the technical, commercial, and psychological barriers that stand in the way of the opening that QbD offers to forward looking companies.
Recognizing the Value
The twenty-first century is shaping up as a difficult time for pharma. Patent expirations, thin pipelines, soaring manufacturing costs, and downward pressure on prices are brewing a perfect storm for many companies. While QbD may not be a panacea, the improved process understanding and more robust processes it promises can translate into significant business benefits, including:

Lower cost of quality:

Using the generally accepted figure of 25% of sales as the current cost of quality, the top 10 pharmaceutical companies spend, on average, $7.6 billion annually on quality (Kamm and Cini, 2007). Because quality is produced through extensive control the result is high cost. QbD, however, enables science-based understanding of processes, so that manufacturers can focus their control efforts on those factors that are critical to quality. Further, greater process understanding also enables more accurate and thorough validation than is now possible through the three-batch standard. Greater process understanding also means more robust processes that can accommodate the inevitable variations in raw materials that occur over time.

Better allocation of resources:

With greater confidence in the ability to maintain in-specification operations, companies can free resources for more productive investment. Reduced manufacturing costs: As QbD informs more and more processes, with greater control and greater robustness, immediate bottom-line benefits accrue from improved yield, increased equipment uptime and plant and capacity utilization, capital cost avoidance, and reduced rework and fewer rejected batches.

Greater speed to market:

By maximizing the probability that a product in development will make it smoothly and effectively through scale-up, technology transfer, and validation, QbD can greatly reduce time to market and speed up return on investment. In terms of revenue alone, every day that a blockbuster drug (defined as having annual sales of $1 billion) is delayed in getting to market, its manufacturer forgoes more than $2.7 million in lost or deferred revenue.

Reduced regulatory burden:

Because QbD enables the manufacturer to understand the design space, the manufacturing processes within that design space can be continuously improved without further regulatory review. The manufacturer gains more regulatory room in which to operate and the FDA can be more flexible in its approach, using, for example, risk-based approaches to reviews and inspections.
Understanding Trends
In addition to the business benefits driving acceptance of QbD, some other broad trends are also likely to give it added impetus. For example, medicines and therapies have become far more complex since the days when medications like antibiotics were taken briefly to treat acute and relatively straightforward conditions. Today whole new classes of drugs have appeared for chronic conditions. Further, with many of the easy therapeutic targets having been hit, pharmaceutical companies now often focus on far more complicated therapeutic areas like oncology, AIDS, and Parkinson's, requiring more complex medicines. QbD, with its ability to scientifically establish the complex multi-dimensional combination and interactions of input variables and process parameters that determine the quality of a product, works particularly well in complex contexts.

In biotech, for example, where testing a product is far more complex than with small molecules and where characterization of the final product is less developed and understood for biologics such as protein therapeutics and vaccines, QbD offers great promise. In biotech, the process is, in effect, the product; and because high levels of variation are often seen in biological processes, developing robust and reliable processes is inherently difficult. QbD, however, frees biotech companies to focus more on analytical tools to understand and control process development and manufacturing, which in turn leads to more information about the product (e.g. secondary protein structures, glycosylation patterns, etc.).

Further, because the biologic end product is often of high-value, biotech companies often incur greater manufacturing risk than is typical for small molecule pharmaceutical manufacturers. Biotechs can reduce this risk by systematically applying these QbD and PAT principles: * Define acceptable limits for the critical-to-quality attributes of the product. * Identify the primary sources of variability in those attributes in the manufacturing process. * Identify which of the sources of variability can be monitored and adjusted for better control during fermentation/cell culture, active agent recovery (cell separation, product extraction, downstream product purification), and filling, lyophilization, and final packaging.

In other words, biotechs can use QbD to define the design space and in conjunction with the tools of process analytical technology (PAT) keep the process within that space.

For example, the fermentation process must be monitored and controlled to maintain optimal cell growth conditions and predictable, reproducible product production with consistent, well understood impurity profiles. Modern approaches use, for example, automated spectrometers with probes inside the fermentor(s) to measure growth kinetics and substrate consumption. These are coupled with automated real-time nutrient and gas delivery systems triggered in response to the collected data. Mass spectrometers are typically employed to monitor and control gas streams while FT-NIR spectrometers are used to measure product concentration, nutrient concentration and biomass. These techniques, coupled with the measurement and control of input parameters such as temperature, pH, impeller speeds and gas rates, aid the process optimization and control requirements to ensure robustness in which the output of the process remains insensitive to variations.

QbD is also likely to gain added momentum as the pharmaceutical industry continues to globalize, thus giving added urgency to goals of harmonization embodied in ICH Q8 (2005), which lays out the framework of QbD and suggests adoption by the regulatory bodies of the European Union, Japan and USA. Further, as more and more companies adopt QbD and it increasingly becomes a prominent component of new drug applications (NDAs), it will reach a tipping point at which both companies and the FDA will be fully committed to the practice, allaying fears that the agency might stop short of fully implementing it.
Overcoming Barriers to Adoption
Despite the value that QbD promises and the trends that are likely to give it further momentum, significant barriers stand in the way. Those barriers and the means to overcome them include:

Technical - it won't work here.

Statistically-based improvement methods like SQC, SPC, Six Sigma, and Lean have been demonstrably effective in improving process performance in many industries (Snee and Hoerl 2003, 2005). Moreover, in recent years, the power of these statistical methods has been dramatically supplemented by a new breed of widely available, easy-to-use statistical software that puts the ability to do sophisticated calculations at virtually anyone's fingertips. Nevertheless, because such key statistical tools as design of experiments (DoE) originated elsewhere, some people don't believe they will work in the pharmaceutical industry. In fact, DoE, which has been used in the chemicals industry since the 1950s, is perfectly suited for performing the kind of multi-variate analysis required to uncover design space and reap the operational, regulatory and business benefits such knowledge offers.

For example, a new solid-dose, 24-hour controlled-release product for pain management had been approved but not yet validated because it had encountered wide variations in its dissolution rate, which presented issues of safety and efficacy. The manufacturer did not know whether the dissolution problems were related to the active pharmaceutical ingredient (API), the excipient, or to variables in the manufacturing process - or to some combination of these factors. Frustrated with the results of one-factor analysis and seeing an opportunity to take advantage of the power of designed experiments, the manufacturer narrowed the range of possible causes of the unacceptable dissolution rate to nine potential variables - four properties of the raw material and five process variables such as temperature, feed rate, and screw speed. From this technologic space - the possible combinations of variables most likely to affect the dissolution rate for better or for worse - the team used a DoE to screen out irrelevant variables and to find the proper values for critical variables, thus accomplishing screening and optimization in a single step (Kamm 2007).

(Click image for larger version.)
Figure 1: Contour Profiler Matrix Plot Shows the Combinations of Raw Material and Process Variable Levels for which Dissolution is Predicted to be within Specifications (Green Area) and those Regions in which Dissolution is Predicted to be Out of Specification (Red Area)


The analysis showed that one process variable exerted the greatest influence on dissolution and that other process and raw material variables and their interactions also played a key role. The company was then able to determine the design space: the various permutations of the settings for the all of these variables that still result in an in-specification rate of dissolution (and other product properties). They then used advanced statistical modeling software to get a clear picture of that design space in a "Contour Profiler Matrix Plot" (Figure 1) created using the optimum settings for each of the two significant raw material (RM) variables and four significant process variables (PV). The X and Y axes are made up of the DoE variables, and the Z axis (the contour curves) represents dissolution (the response variable). In the red regions, dissolution is out of specification and in the green regions - the design space - it is within specification. Keeping the process operating "in the green" by using the flexible parameters that were optimized in the course of the DoE study, which is precisely the kind of approach envisioned in ICH Q8, the company successfully validated and launched the product.

Financial - we can't afford it.

As with any major change in the approach to the development of products and processes, many companies worry that the cost will simply be too high. In fact, the cost of implementing QbD is almost negligible, especially when measured against the return on investment. Nevertheless, some large companies may view wide adoption of QbD as too expensive because they have extensive resources sunk in the old methods. Meanwhile, many small biotech companies, focused on getting to clinical trials as soon as possible, give other priorities a backseat and hesitate to invest scarce resources in new methodologies like QbD.

The key to overcoming this obstacle is to rigorously translate the cost of QbD into its financial impact. Consider, also, the opportunity costs of not pursuing QbD. Until the manufacturer of the pain management drug executed the DoE study, the dissolution problem had delayed the launch of the product for several years. QbD can help deliver the kind of speed to market that still constitutes a real competitive advantage in the industry.

Psychological - it's too painful to change.

In some ways, this is the most formidable obstacle of all and yet it is the least concrete. Change inevitably means loss - of familiar ways of working, of comfort, of stability - and such loss is painful. And there is no question that the more holistic way of working required by QbD entails great change. From the QC and analytical labs to process development to manufacturing and regulatory submission and compliance, people across the enterprise will have to work together with the concept of the design space as the framework for their common efforts. Contract manufacturing organizations will also have to be able to handle QbD or their more advanced customers will look elsewhere for partners.

In the face of such sweeping change, many people may understandably resist. In fact, change is so painful that technical and financial objections to change are often really masks for what is at bottom fear. Overcoming it means examining those objections rigorously and honestly and disentangling them from psychological motives. Further, there are proven techniques of change management that not only efficiently implement improvement initiatives like QbD but also help negotiate the tricky psychic terrain that change brings. Organizations that hesitate to confront change may find themselves forced to do so anyway when industry conditions make the status quo more painful than the alternatives.
A Postscript
In what some consider the first clinical trial, James Lind, almost 150 years after Lancaster, prescribed differing diets for several groups of sailors dying of scurvy aboard the Salisbury. In just six days, the sailors taking citrus fruits returned to duty. It is this kind of science-based empiricism that forms the tradition of the pharmaceutical industry and that, in the end, will determine the length of the path to acceptance of QbD. Embracing their scientific tradition, companies will no doubt take a rigorous look at the risks and rewards of QbD and make their decisions based not on artificial barriers but on the evidence, and when they do so they are likely to move rapidly ahead.
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