Moving Drug Manufacturing from Good to Great
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.
By Agnes M. Shanley, Editor in Chief
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.