A new focus on process in drug development is making it easier to move a product from the lab to manufacturing
IN SEPTEMBER 2004, the Food and Drug Administration's (FDA's) Center for Drug Evaluation and Research (CDER) issued an industry guidance titled "PAT-A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance." In it, the agency took aim at what it called "industry's hesitancy to broadly embrace innovation in pharmaceutical manufacturing," calling for "an integrated systems approach to regulating pharmaceutical product quality... based on science and engineering principles for assessing and mitigating risks related to poor product and process quality."
PAT, or process analytical technology-a system for building quality into products through ongoing chemical, physical, microbiological, mathematical, and risk analysis-was the focus of the guidance. But, more broadly, the issue at hand was taking drug development scale-up from a world of empiricism-simply repeating processes that had worked in the past (or avoiding those that hadn't) when it came time to move from the lab to the manufacturing facility-to a world of integration. In other words, scale-up needed to be part of the process well before it was actually time to scale up.
In the past, quality of scale-up was primarily established through inspection and sampling, says Bikash Chatterjee, president and chief technology officer of Pharmatech Associates (San Francisco), a full service consultancy involved in all phases of the drug development life cycle. "There's a fallacy there: in the absence of process control, inspection doesn't really provide you with much information. You may ask: if you revalidate lots, how can you have one lot fail? Well, because you haven't identified all your sources of variation, and one snuck up on you."
That process relied too much on subjectivity, Chatterjee says. "In 28 years of operations and product development, I can't tell you how many times I've sat looking at a chart that has three data points-and then a conclusion on a relationship. That's not statistically significant. Think of all the sources of error in scale-up: the test, the operator, the equipment, the raw materials, the process, the product."
The 2004 FDA guidance gave industry the flexibility and the impetus to use science to justify its conclusions both in terms of process controls and critical parameters, moving well beyond the "three data-point" system.
"It's quality by design," Chatterjee says. "How do you take your existing paradigm from product development and translate that into something that can more thoroughly characterize the process in a scale-up strategy?"
TURNING THE BATTLESHIP
Four years in, how is the new scale-up paradigm faring? Has scale-up changed from being an inappropriately subjective process tacked on at the end of the drug development cycle? Has it become a scientifically driven, quality-based element that is an integral part of a new compound's life from the moment it emerges from early discovery?
The best answer is "sort of." Many companies-especially the larger, top tier biotechnology and pharmaceutical groups such as Pfizer, Merck, and Genentech-have been pursuing such a strategy "in pockets" for years, according to Chatterjee. "Now, there is beginning to be a more continuous vision between development and commercial. RandD and commercial used to be totally separate, and now the commercial side realizes that it needs to use the RandD side's expertise to ensure their products get to market safely," he says. "But it's a challenging thing to change the culture. And especially in large companies, it's like moving a battleship. You need a five-mile warning to turn left."
In the past, we were product-centric. Now, we're process-centric. ...Scale-up today is very much focused on methodical milestones and very clear deliverables at each step of the process.
-Bikash Chatterjee, president, Pharmatech Associates
But the battleship does appear to be turning, says Lawrence Block, PhD, professor of pharmaceutics at the Mylan School of Pharmacy and Graduate School of Pharmaceutical Sciences at Duquesne University in Pittsburgh. "There's been a greater willingness to develop processes that are scalable, right at the outset. Certainly, we've seen more inclusion of process engineers, chemical engineers, and the like, individuals with insight into the scalable aspects of any manufacturing process, early on in the development cycle," he says. "It's not something that you can come back to as an afterthought later, which is how it used to be treated."
Not only are drug developers turning away from the empirical, "let's try this, let's try that" approach to scale-up, they are also beginning to realize that scale-up issues need to be addressed in a more transparent way.
"There's an increasingly greater appreciation of the need to share information with regard to general principles that influence the variability of the process," says Dr. Block. In other words, hard-earned knowledge about scale-up factors is no longer being treated as such a big trade secret.
"Some of the best examples in terms of problematic aspects-where scale-up failed or a difficulty was encountered-are still buried in files and will probably never see the light of day," says Dr. Block. "If a given process presented difficulties and you'd uncovered them or gotten around them, to publicize that was tantamount to giving competitors knowledge that had cost you dearly in terms of time, money, and expertise."
As an example of the changing paradigm, Dr. Block points to a paper on the scale-up of a coating process from laboratory to production published in the January-February edition of American Pharmaceutical Review by Genentech authors Samir Sane, PhD, and Chung Hsu, PhD. "A while back, that paper might never have seen the light of day. To see a paper like that, dealing with the strategy for a successful scale-up of lyophilization, that's relatively new."
THE CHICKEN AND EGG OF SCALE-UP
Scientifically driven scale-up focuses on developing meaningful specifications early on in the life of a new product. "As you're developing the product, you also need to develop the process and specifications and test methods to ensure it develops properly," says Chat-terjee. "It's a bit of a chicken and egg thing. As you learn more about the product, you learn more about the process and develop more meaningful tests to ensure it's scaling properly."
The key question: "What drives the variation in the process?" For example, in the past, if you had a caplet that had to have a particular dissolution profile in water in order to get X amount of the drug into the bloodstream, the scale-up question might simply have been, "How long does it take to dissolve?" If the answer was 15 minutes, that was the information you needed.
"You might never have stopped to ask, what is it in the manufacturing that can drive dissolution variation?" says Chatterjee. "Now, that won't fly. There is a need for statistically unbiased experiments that identify what drives variation. In the caplet example, I might find that mixing time with my lubricant does that. That's terrific. It means I don't need to spend as much time worrying about variability in the other unit elements. I understand what drives the variation and can focus on the blending step as I scale up the process, making it cheaper and faster with a higher probability of success."
In practical terms, how does PAT-and quality by design in general-change the world of scale-up?
For one thing, there are a lot more small-scale experiments going on early in the drug development process, says Deborah Barnette, PhD, senior director of process development and manufacturing technology at Talecris Biotherapeutics (Research Triangle Park, N.C.), which has products in immunology, pulmonary and critical care, and coagulation and thrombosis. "We're defining our critical parameters early on, so that we can build in the robustness work we need to understand the design space around scale-up issues."
Since Talecris researchers do a lot of protein purification in the creation of lyophilized parenteral products, for example, it's critical that they understand whether the rate at which the particular protein is frozen is the most important variable or whether it's the temperature during the secondary drying. "One may be very easy to do and give us a lot of flexibility, while the protein may be particularly sensitive during another, and understanding which are the most sensitive points for the particular protein is essential," Dr. Barnette says.
That's where small-scale experiments come in. "Once we develop a good small-scale model, we'll do multiple experiments looking at many individual variables as well as the interactions of those variables," Dr. Barnette says. "This gets us not only to the definition of what's critical, but how critical is it? Is the response like a cliff that you'll fall off of, or more rounded? We also spend a great deal of effort on bioanalytical tools to understand a molecule, its structure, its stability, and how it responds to different conditions."
Talecris is also using PAT to assess its infrastructure for scale-up-such as water for injection systems. "These are things that can be very sample-intensive to support, so we're looking at ways to do that online, so that we can take the data continuously rather than in discrete batches," says Dr. Barnette. "This type of effort is a lot less operator-dependent. You don't have to go in and take a bunch of samples, which always introduces some risk. Doing this on a continuous basis provides not only an additional level of separation between process and operator but also additional information that the operator can use in making decisions about how to move forward."
Some of the best examples in terms of problematic aspects-where scale-up failed or a difficulty was encountered-are still buried in files and will probably never see the light of day.
-Lawrence Block, PhD, professor of pharmacy, Duquesne University
In a quality-by-design environment, scale-down is as important as scale-up, says Dr. Barnette. "A lot of our vendors have recognized that having good scale-down equipment is critical to this business. We can't use one-meter columns to do any sort of experimentation on a reasonable time frame," she says. "We need small chromatography systems that are linearly scalable, and a lot of our vendors have been providing custom models like that over the last three to five years."
Dr. Block agrees equipment scaling is essential. "The whole issue of making a preliminary formulation on a lab scale and eventually transferring that process to the pilot plant and ultimately to full-scale production suggests that the best approach is to use equipment that involves the same general modus operandi from smallest scale to largest. You wouldn't use one type of mixer in one environment and a completely different kind on a different scale," he says. "This means that formulation and manufacturing have to get their heads together and figure out what the equipment is that should be used in the early formulation stages that would ultimately make it easier to scale up to full production. There may be equipment that looks great on a lab scale but is very difficult to replicate on a larger scale, if at all-and that's just not acceptable anymore."
If you simply have to make the lab-scale product using a piece of equipment that isn't available at commercial scale-which could be the case for a novel product-that introduces more risk that must be understood. "You have to characterize not only the product and the process but the equipment," notes Chatterjee. "If you do that properly, you will know what you need to control and what the commercial equipment needs to look like in order to perform properly. Many companies, early on, put an argument to the FDA: as long as I can control these critical output parameters and demonstrate that they're the same, then you let me monkey around with the equipment. If you can prove that to them, it lets them sleep at night."Ultimately, says Chatterjee, the new world of scale-up can be summarized in a fairly simple way: "In the past, we were product-centric. Now, we're process-centric. Before, if the product worked fine, we didn't care how you got there. Now we do. Scale-up today is very much focused on methodical milestones and very clear deliverables at each step of the process.