Moving Drug Manufacturing from Good to Great

June 27, 2006
Pankaj Mohan, U.S.-based management leader for global process engineering at Eli Lilly, shares strategies that can help more drug manufacturers move from good to great.

In order to minimize costs and optimize product quality, drug company leaders must focus on the forest rather than the trees, suggests Pankaj Mohan, U.S.-based management leader for global process engineering at Eli Lilly. In this exclusive interview with Pharmaceutical Manufacturing Editor in Chief Agnes Shanley, Mohan shares strategies that can help more drug manufacturers move from good to great. He also discusses his book, "Pharmaceutical Operations Management: Manufacturing for Competitive Advantage," coauthored with Jarka Glassey and Gary Montague of the University of Newcastle, available through McGraw-Hill (

[Editor's Note: Whereas in most of our Q&A-formatted articles, we use P.M. as an abbreviation for Pharmaceutical Manufacturing, in this interview, A.S. stands for Agnes Shanley and P.M. stands for Pankaj Mohan.]

A.S.: What made you write the book, and what message do you want people to draw from it?

P.M.: I’ve worked in both sides of pharma: commercialization and manufacturing, and also taught as an academic. To date, there has been no comprehensive work that considers the entire drug manufacturing life cycle or presents a systematic framework of pharma’s new paradigm, based on productivity and quality. My coauthors and I wanted to help people see “the big picture,” and focus on issues such as quality by design, reducing speed to market, and reducing cost. We felt that, as the pharmaceutical industry moves toward a new paradigm, it would be very important to show how that paradigm will look.

A.S.: The book seems to follow the structure of the pharma value chain.

P.M.: We start with commercialization, focusing on the late stage, then move into new plant construction and process capability analysis, asking, once you operate a plant, then what are the operational concerns you have, especially regarding variability.

We then delve into discussions of productivity and the change in mindset as the drug industry evolves to a new system incorporating the concepts of Lean and Six Sigma.

We finish off with a discussion on the kind of leadership needed to transform pharma’s existing system into its new paradigm, focused on reducing cost and speed to market, to ensure quality medicine to the customers. [Editor’s Note: This chapter of the book is available on this website at "Three Keys to Productivity Improvement."]

A.S.: We’ve been surprised to notice that some pharma companies are still very reluctant to embrace the principles of “operational excellence.” Some have stated out right that Lean just isn’t for them. Can the industry afford to reject transformative programs, and is the key to change hiring people from outside pharma, to bring in fresh ways of thinking?

P.M.: First, we find that many pharma companies are embracing op ex. Lilly has always encouraged it and is at the forefront of implementing Six Sigma and so forth. When companies don’t see the value in such programs, it’s usually because their leadership fails to see the “big picture.”

I’ve known some pharma CEOs who have come from other sectors and done very well. However, I personally feel that pharma leadership should have some domain expertise. It’s a very long value chain, and having some knowledge and experience of this sector is very important. There’s no black and white answer. More and more, industries are thinking about training their leadership to think out of the box, and they’re collectively brainstorming to move to the new paradigm.

A.S.: We’ve noticed a “technology divide” in pharma — not only huge discrepancies between large and small companies, but major variations among plants operated by the same company. Some plants are paper based, others automated. Are standards for technology and IT needed to move industry forward?

P.M.: Two different questions come into play here. First, what is the optimal technology solution? Second, what is the business case for implementing it? I’m not a very big fan of driving one standard throughout any corporation. You have to balance new technology considerations against local needs.

Consider, for example, a case where you want to introduce more advanced control system to an existing process. We present such a case history in our book, examining an actual plant that was producing with factory losses due to quality issues and product nonconformance.

We knew that introducing a more sophisticated process control technology would resolve some of the problems, but we implemented it in stages. First, we did a cost/benefit analysis to understand what the financial return would be from each piece of the investment.

Then, we also looked at modern tools such as Real Options. [Editor's Note: For an example of how some pharma companies are using Real Options, see the unrelated white paper "Using a Real Options Approach to Achieve Manufacturing Flexibility."] We broke down the project into smaller chunks and analyzed the risks entailed in each so that we could mitigate the financial risk as we took the project forward.

The approach taken to optimize technology at any given facility or operation will vary from case to case, but success generally requires a marriage between a strong business case and a strong technical project layout.

A.S.: We’ve seen that pharma manufacturing remains hampered by “siloed” information and lack of integration between different databases and IT platforms. Although some companies are investing the time and effort in building bridges between these platforms, more are not. Will this problem only be resolved by the availability of more commercial “off-the-shelf” solutions?

P.M.: It’s true that pharma companies are collecting a lot of data, but on an “FYI” basis. The industry needs to convert data into knowledge, and I have yet to find one comprehensive commercial solution that will make it easier to move from data on the one end to knowledge at the other. There are many intermediate steps. First is data filtering, to ensure that you are not looking at “outliers,” and so that you can distinguish between noise and a true process signal as you analyze the data. This is very important.

Applying process analytical technologies (PAT) is the next step. One must ask: “What kind of technology will be used to collect the data?" Then you must determine how to analyze the data, such as how often to average, etc.

The drug industry collects a lot of data and spends a lot of money to get information, but it still doesn’t have a true knowledge engine that would allow it to transform data into knowledge.

I see various commercial process control packages that can handle some of the tasks required, but using them requires pulling various packages together, from data analysis to knowledge extraction, storage and artificial intelligence. So far I haven’t seen one company provide a comprehensive solution that integrates various layers between data and knowledge. The industry is not getting maximum value out of the data it collects, for process optimization, or proactive troubleshooting.

A.S.: So, companies must be prepared to do the work themselves? What suggestions could you offer?

P.M.: Well, the first thing to realize is that, as information moves to knowledge, using it becomes multifunctional activity. Instead of each different department looking at information differently, a very focused, multifunctional “task force” should look at data collectively and on a regular basis, with the specific intent of generating knowledge.

Secondly, it’s important to avoid data overload. Companies should not record data that won’t be used. If you have a lot of data coming through in a GMP setting, FDA might start looking at that and ask, “Why aren’t you putting these data to use?” If you don’t intend to use data for extraction or real-time batch info analysis, don’t just make every data point available.

Finally, integrate technology and methodology that is available. Multivariate statistical process control can serve as a knowledge engine, and as a tool to help you understand which factors, among the hundreds of existing variables at play in your operations, will have the greatest impact on profit margins.

A.S.: What skill sets will be required to improve pharma manufacturing operations? The industry still seems to be comprised mainly of chemists who may not have the knowledge of control or modeling required to do all this.

P.M.: Well, to be fair, pharma does employ some ChemEs. I am one myself. But, typically, what can add horsepower to any pharmaceutical manufacturing site or development unit is the involvement of statisticians.

Some companies are now developing strategies in which scientists and engineers become “secondary loop” specialists. Primary loop people ensure that the process works as it should. More-senior people at this second level take a step back, and look at the data that are coming out of the plant. They provide systemic or breakthrough improvements, or suggest changes that need to be made at the laboratory level. These people need specific knowledge, training and experience, and must be connected, internally and externally, to a network of subject matter experts.

A.S.: What is needed to ensure success with PAT?

P.M.: The way I look at it, PAT must be integrated with an overall control strategy. Suppose you have a process for which you’ve identified critical process parameters. Ideally PAT should be applied before the process hits manufacturing, so that, once the process is “locked down,” PAT becomes an integral part of the control strategy. Integrate it with various control philosophies to ensure your process is capable and in control.

A.S.: Will PAT eliminate the need for old-fashioned quality testing?

P.M.: FDA is pushing the use of PAT for “in-process” control, so that critical process parameters and quality indicators are measured and controlled during the manufacturing process. This approach would reduce lab and end quality testing, and give more confidence in parametric batch release. The question for many companies today is, “Is PAT an add-on to what we already do, or will it make things simpler and easier?”Some people today are using it as an add-on, which is a good intermediate step, before eliminating any end-testing procedures.

A.S.: Will artificial intelligence and such things as simulation and expert systems ever be applied routinely in pharma? Right now they seem like science fiction to a lot of companies.

P.M.: Most pharma companies have gone up to the level of model predictive control. Beyond that, more advanced systems seem too much like a “black box” for many companies. The idea of adaptive control, and concepts such as fuzzy logic, require a whole new approach to computer systems validation, which is output-focused.

A.S.: On the surface of it, biopharma seems more reluctant to embrace advanced control. There are very few biotech representatives on the ASTM E-55 committee at this point, for example. What’s the bottleneck?

P.M.: I disagree. Particularly at the bigger companies, biotech seems to have more PAT applications than the typical pharma centers. If you’re fermenting something, you’re using MassSpec to analyze gases. You’re analyzing various media components within the fermenter itself. I personally feel that larger biotech is more advanced.

A.S.: Will any biopharma processes be running continuously in 10 to 20 years?

P.M.: In biotech, fermentation processes could run continuously. Now, we’re seeing a trend where fermentation is being run semicontinuously. This approach is especially useful where changeovers lead to downtime and problems. Basically, continuous processes can be applied, like very long versions of batch processes, in some fermentation processes.

In purification, processes could be run continuously with multiple fermentation batches hooked up in a series. Purification can run as any chemical process can. But fermentation can be semicontinuous.

A.S.: So why are so many people in biopharma and pharma “against” the idea of continuous processing?

P.M.: There are some practical questions that need to be addressed. For example, quality organizations and FDA are pushing for lot segregation. If you ever need to recall a product, and you’re going to tag that batch with continuous processing, the challenge will be how to segregate batches.

A.S.: We recently surveyed pharma companies on the state of their operational excellence programs. Results suggested that some companies are having trouble building bottom-up support and doing cross-functional training. What advice would you have?

P.M.: The key is knowledge management. Because pharmaceutical manufacturing is such a cross-functional discipline, the key to improvement and survival is to optimize information flow between key players, and building a “learning curve” within your organization.

You also need to address the silo mentality (“I only know and care about X”). Silo-less synergy is critical. If you’re building a function, you’ll have to combine functional excellence with cross-functional integration. The two must be carefully balanced — too much integration leads to mediocrity, too little continues the silo culture. Various techniques can be used to foster cross-functional integration around key themes.

In our book, we offer an interesting case history involving the optimization of a process that required a large number of people from different disciplines. We exacted knowledge from each function and compiled a holistic knowledge base to drive improvement forward.

I would love to see pharmaceutical companies establish a department with core data expertise, whose sole role and function would be to synergize knowledge across multiple disciplines. You can’t train everyone to learn all about everything. Some companies are quite good at this.They’ve set up knowledge centers or centers of excellence, but pharma companies are relatively new to this.

A.S.: What changes will be needed to improve pharmaceutical manufacturing training on the academic side?

P.M.: Purdue University is now thinking about developing a new pharmaceutical engineering curriculum, because academics feel that the knowledge and experience that’s being imparted to students today is drifting away from the industry’s real needs. For instance, if I come from a ChemE school, I won’t be taught much about solids processing, so I won’t know much about solid flow patterns.

In the same vein, if I’m a pharma scientist, I’m not taught about the unit operations that go into manufacturing or into process development.

Lilly and other companies must — and do — support efforts to change training so that the industry creates the pool of talent it will need to move to the new paradigm.

A.S.: Is big pharma becoming a dinosaur?

P.M.: Both in perception and in reality, productivity will be key. Right now, most of the biotech drugs are coming from universities or small scale research firms. Adopting the productivity model would make pharma operations leaner.

About the Author

Agnes M. Shanley | Editor in Chief