Changing Pharma's DNA: An Interview with Paul McKenzie of BMS

The vice president and general manager explains how BMS is harnessing the power of simulation, process analytical technologies and high-throughput technologies to make the plant operator’s job easier, while reducing release and cycle times.

By Agnes Shanley, Editor in Chief

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Several years ago, former FDA Commissioner Mark McClellan challenged pharma to catch up with potato chip and soap flake manufacturers by modernizing operations and applying technology more effectively.

As risk-averse drug companies wrestle with the issues presented by manufacturing science, Bristol-Myers Squibb (BMS) has spent the past nine years quietly building a platform based on open control (particularly ISA’s S88 standard). The new platform utilizes automation and IT in ways that are novel to pharma, harnessing the power of simulation, process analytical technologies (PAT) and high-throughput technologies to improve process understanding, empower users and reduce cycle time.

BMS Vice President and General Manager Paul McKenzie’s goal is to have a single language for automation and IT that will allow data to flow much more efficiently between different operations, from the R&D lab, to QA and QC, to the plant floor.

S88 is often a hard sell in pharma, although many manufacturing engineers who have applied the standard appreciate its logic and agree that it simplifies operations, commissioning and validation. It requires stripping definitions of batch operations to their most basic level so that, for instance, the instruction “charge” will apply to a biotech, small molecule, or fill and finish plant. This is no easy feat, but believers say the front-end efforts pay off.

BMS has focused on applying S88, not in manufacturing, but to other areas further up the chain, improving the efficiency of drug development by getting scientists in R&D to understand the value of open control. “Rather than go to the fifth decimal place on the plant floor, I’d rather go to the first decimal point in the lab,” McKenzie told attendees at the World Batch Forum last May. Modeling is an extremely important part of the plan, and using models to predict, a priori, what will work in manufacturing, rather than learning the hard (and expensive) way, through wasteful “practicing.”

McKenzie and his team view S88 as much more than a mere standard. It’s really a DNA for the 21st century drug industry, McKenzie explains. “It provides a common vocabulary that can be used from the drug development teams all the way out to the commercialization teams,” he says. “It can be used as a common philosophy, not only in what historically would have been considered manufacturing events, but also in the lab events that sponsor manufacturing.”

BMS began working with S88 in small-molecule API development, as a way to improve tech transfer between R&D facilities in New Jersey and API manufacturing pilot plants in Ireland. Taking this approach for these construction projects removed roughly 9,000 signatures from the design and validation documentation. Now, the platform is being piloted in Puerto Rico for a fill/finish line, and rolled out in BMS’ new biopharmaceutical facility in Devens, Mass.

The key to success, says McKenzie, is not to think of automating for its own sake. “As Lynn Craig of the World Batch Forum has put it, S88 makes you think about where you should spend your automation money. It doesn’t mean everything should be automated,” he explains. “You must ask yourself: Where will a control valve or mass flow meter make the most sense? Where is the highest level of redundancy or unreliable operations? The ultimate goal is reducing data and enhancing its quality.”

Similarly, a paperless plant should not be the goal. “Electronic batch recordkeeping (EBR) is the natural outcome of a good execution plan, but it isn’t why you actually automate,” McKenzie points out. “We wanted to make the plant operator’s job easier and reduce release and cycle times.” Drug companies may be approaching EBR the wrong way, McKenzie suggests. “A lot of companies are migrating, in small steps, from the paper world to a paper-on-glass world, instead of sitting back and asking ‘How do I want to fundamentally change how I operate on the plant floor?’ ” he says. “S88 recipe execution allows you to address these questions. Good things come out of that, like EBR and reducing cycle times.”

In this interview, McKenzie discusses his vision, the origins of the platform and his views on pharma’s future.

PhM – Many of our readers are unfamiliar with S88, or consider it too cumbersome to use effectively. What led you and your colleagues to the standard?

PM – In 1998, we had the opportunity to build a new research pilot plant. I went out to benchmark, frankly, against everyone but pharma because pharma doesn’t use process control all that well.

PhM – Why is that?

PM – Historically, pharmaceutical innovation has been perceived to be at the chemistry and biology level. Engineering skill sets and applications and process control expertise have been out of its mainstream, at least compared with the petroleum and chemical industries.

As a result, pharma may do very well at introducing new medicines to the market, but it has done so relatively inefficiently.

PhM – Is automation based on S88 a prerequisite for efficient operations?

PM – At BMS, our vision has been “lab to plant,” where you work from the lab, collect the fundamental data you need, but from day one you’re using the vocabulary that is needed for targeting all lab development to successful commercial manufacturing implementation. This means not only higher quality drugs, but more efficient and environmentally safe processing.

PhM – How are you moving toward that vision?

PM – In 1998, we decided to use S88 to build a new small molecule pilot plant and a commercial API manufacturing plant. We harmonized across those two projects the S88 vocabulary, S88 approach, equipment P&IDs and codes. That gave us significant savings. Since those projects, we’ve used the platform when we renovated additional pilot plants in New Brunswick [N.J.], and we’ve moved it into our earliest stage kilo-scale lab. So, from our initial scale-up, we were collecting data in an S88 mindset.

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