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By Agnes Shanley, Editor in Chief
This is the fifth year that Pharmaceutical Manufacturing has surveyed readers on their automation and process control practices. This year, 102 people shared their views. Results suggest that little has changed during this period. In fact, if this year’s responses are any indication, resistance to such FDA-supported initiatives as Quality by Design (QbD) and Process Analytical Technologies (PAT) continues to be seen, due to cost and time concerns.
In this article, we summarize this year’s key survey results and compare them with results from previous years. These are not direct comparisons because the survey questions and format changed slightly from year to year, but some trends are still visible. Pharmaceutical manufacturers’ use of process capability analysis and statistical process control appears to be growing but far from widespread.
Other trends include:
We asked readers whether continual improvement and operational excellence programs such as Lean Six Sigma were guiding, or at least in synch with, automation and IT goals. This year, 16% of respondents said this alignment was a top priority, down from 19% last year; however, 26% said they had achieved some level of alignment, up from 15% in 2009 (Figure 1).
We asked about PAT and the broader Quality by Design (QbD) approach and whether companies were implementing either or both approaches. Our first survey, in 2006, was published roughly two years after FDA published the PAT guidance, and before QbD was formally introduced. At that time, 20% of respondents said they did not plan to implement PAT. This year, 32% said they had no plans to implement either PAT or QbD; cost and time were the greatest obstacles (Figure 3).
There are many reasons for reluctance, said consultant Pedro Hernandez-Abad, who had championed PAT and QbD at Wyeth before and after the merger, and who spoke about some of these issues during a recent PharmaManufacturing.com webcast. First, there’s fear of change, and a loss of control. “When quality moves from lab to the manufacturing line, there’s a learning curve,” he said. “QbD and PAT cannot be done part-time. You need to have champions who can drive the effort, but the big question is, ‘How much is it going to cost?’ ” he said.
“If I put $500,000 into equipment and training, I need the money back by the end of the year. But with new approaches such as QbD, an exact number is not always possible to predict,” he said. More and more companies are doing real financial viability studies for PAT. However, there are hidden costs, Hernandez said, such as support systems. “You’re not just talking about an instrument on the line but an IT infrastructure and meaningful sampling strategies,” he said. Finally, there’s the question of which metrics will be most meaningful for a whole new approach.
“A lot of folks are trying to push QbD through their organizations. They oft en stress the idea of regulatory relief and real-time release,” said PRTM principal Sam Venugopal. “But it can require some significant investments to get to that point.” Some companies are breaking the problem down into increments, he said, asking, for example, “Can we look at how we are capturing information within our organization, and, at a minimum, understand our processes better . . . and maybe get to regulatory filing faster?”
J&J’s Pharmaceutical R&D is building QbD from the ground up, says Paul McKenzie, global head of pharmaceutical development and manufacturing sciences. At this point, he said, “We’re ensuring we have the pillars to make QbD successful, but we need to invest in those pillars, first, before we prescribe doing QbD across our whole portfolio,” he said. “Our manufacturing and development groups agree that we need to define platforms and technology to get the same base vocabulary across the two areas. As we do that, we can than start driving, internally, the business case for QbD,” he said. “Then the question becomes: ‘How do we convince ourselves and regulators that we have a firm understanding of our complete design space?’ That then allows us to consider different approaches to product portability, process and/or raw material changes.”
The first step, McKenzie says, is establishing the foundation for a platform with each area. “Once we have a continuum of information, we will need to marry that space with the clinical space, so we can really dial in on the impact that process changes will have on safety or efficacy. If we can build that integrated process, analytical and clinical design space, it will make it much easier for us to adopt and make a strong business case for QbD.”
Thinkers within industry and FDA who had encouraged pharma’s use of modern industrial engineering techniques say there has been little change since Ray Scherzer made his benchmark presentation on QbD to the FDA in 2002. “At this point, we have to ask, is it Deming or damning,” joked U.K.-based consultant Ken Leiper. This year’s survey results suggest slightly greater use of process capability analysis and statistical process control, but univariate SPC dominates, even though drug manufacturing is multivariate (Figure 4).
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