Exploring AI with the newest ISPE Community of Practice

June 2025

ISPE AI Community of Practice Steering Committee members, Ben Stevens and Eric Staib, join the podcast to share more about the new CoP, the upcoming ISPE GAMP® Guide: Artificial Intelligence, and their thoughts on the current state of AI in the pharmaceutical industry.

• Guests

  

  Benjamin Stevens
  

  Director of CMC Policy & Advocacy

  GlaxoSmithKline

  

  Eric Staib
  

  Vice President, Corporate Quality

  Syneos Health

• Transcript

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  Welcome to the ISPE podcast, Shaping the Future of Pharma, where ISPE supports you on your journey,

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  fueling innovation, sharing insights, thought leadership,

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  and empowering a global community to reimagine what's possible.

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  Hello, and welcome to the ISPE podcast, Shaping the Future of Pharma. I'm Bob Chew, your host.

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  And today, we have another episode where we'll be sharing the latest insights and thought

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  leadership on manufacturing, technology, supply chains, and regulatory trends impacting the

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  pharmaceutical industry. You will hear directly from the innovators, experts, and professionals

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  driving progress and shaping the future. Thank you again for joining us. And now,

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  let's dive into this episode. Our topic today is the new ISPE Artificial Intelligence

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  Community of Practice and the associated upcoming ISPE GAMP Guide, Artificial Intelligence.

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  To share more about these exciting initiatives, I would like to welcome Ben Stevens,

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  Director of CMC Policy and Advocacy at GSK. Ben is the ISPE AI Community of Practice

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  Steering Committee Chair. I also want to welcome Eric Staib,

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  Vice President of Corporate Quality at Syneos Health. He is the ISPE AI Community of Practice

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  Steering Committee Secretary and the GAMP AI Guide Co-Lead. Ben and Eric,

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  welcome to the podcast. We're so glad to have you both with us.

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  It's a pleasure. Thank you. Appreciate it.

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  All right. Well, let's dive into this topic. The ISPE Artificial Intelligence Community of Practice,

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  also known as the ISPE AI COP, was launched last year.

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  Taking a step back, what is an ISPE Community of Practice?

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  Yeah, thanks, Bob. Maybe I can start with this and Eric can jump in. So, the COPs are actually a

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  pretty common thing within ISPE. And in fact, there's quite a few of them beyond the AI COP.

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  Really, what the COPs are, the Community of Practices, it's a way for ISPE members to come

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  and be a part of essentially a group that shares a common interest, has a passion for that area of

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  focus, and is able to get involved in ways around discussing key topics, especially as it interfaces

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  with regulatory aspects of that topic, but also technical best practices and other considerations

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  that are more broadly covering the industry interests. It's a benefit for ISPE members

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  because it's not limited to any specific group. It's really open for any ISPE member. And so,

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  really, when we went out to establish this COP, the idea was that we were trying to cast a very

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  wide net and get in a lot of folks who had a very broad interest in this really critical topic.

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  So, that's kind of, in a nutshell, what the COPs are all about.

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  I think that's a good description, Ben. And as you can imagine, with regards to the topic of

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  artificial intelligence, that broad net brought a lot of people across industry, Bob. So,

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  whether it be large pharma, biotech, clinical research, contract manufacturing organizations,

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  we've had a lot of interest since the inception of the Community of Practice,

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  and a lot of good work that has already been done within the COP that we can talk more about today.

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  But I think, Ben, it really covered it well with respect to what we represent and what we're trying

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  to do with regards to bringing people together on a like topic with similar interests and really

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  helping to shape and form that community with regards to education within ISPE, as well as

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  industry itself. So, there's many ways we do that within the Community of Practice, whether it is

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  through guides, such as the GAAP AI Guide we'll talk a little more about, or specifically blogs,

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  pharmaceutical engineering articles, podcasts such as this, and webinars as well.

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  So, with respect to this specific artificial intelligence COP, what are your goals,

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  what's your intentions, and what specific challenges and opportunities is the COP looking

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  to address? Yeah, I mean, we can talk a little bit more in detail on this, but, you know, right now,

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  I think one of the things we've been trying to do, you know, myself with Eric and Nick as well and

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  others, trying to kind of focus on building out some of those key topic areas that we know that

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  the community is going to be interested in. And, you know, as time goes on, I think that will

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  continue to grow. But right now, we've been focusing a lot on establishing subcommittees

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  that are actually, you know, driving some actual efforts in establishing content and also

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  plans going forward. So, for example, we have three subcommittees right now that are actively

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  engaged in areas around applications model and data preparedness and workforce and regulatory

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  more broadly. But, you know, as you can imagine, those are relatively, it's a relatively small

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  piece of the overall puzzle. And so, we expect that there's going to be a lot more of the

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  subcommittees that will grow out and become pretty engaged. And I think the other thing is,

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  and, you know, Eric obviously mentioned the connection already to GAMP, but we do have

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  larger groups that are already doing a lot of work that dovetails with this, you know, for example,

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  GAMP, but also the Pharma 4.0 group. And so, we're establishing a more, I'd say, you know,

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  direct and regular connection between all that existing infrastructure and ISP and making sure

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  that it's all kind of tied into a broader, you know, movement forward for AI as a whole.

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  I would add to what kind of Ben, what Ben has already said, because we're in such a highly

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  regulated industry, a lot of people like to jump directly to the compliance. And how do we ensure

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  that regulators, that industry is going to be accepting of such innovative technology? And

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  that's, you know, the big question, you know, the elephant in the room most of the time. But this

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  community of practice is much broader than that. As Ben mentioned, we started with really three

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  subcommittees, and it's beyond just the compliance aspects of AI and machine learning itself as well.

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  So, really focusing on larger type concepts, use cases within industry, you know, whether it be

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  regulated or non-regulated, GXP or non-GXP, and things such as he talked about in one of our

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  subcommittees around workforce preparedness. So, there's a lot of companies now that are really

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  facing, and I know my own company, is how do you get people to understand how to use AI? What's the

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  best approach? There's a learning curve, especially when it comes to generative AI, large language

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  models. Prompting is a big thing, right? I'm sure all of us have experienced it with chat GPT with

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  respect to how do you give the large language model the right prompts to return the information

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  you're looking for, right? It can be challenging, just like we've used with Siri and other things

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  like that. You have to prompt it correctly. So, getting people to understand within your company

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  how to use it, how not to use it, and how to be most effective in using it is extremely important

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  as well. So, going back to individuals and their interest level, you mentioned subcommittees.

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  What does it take for a person who, let's say, is listening to this podcast for the first time,

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  is interested in this community of practice? What are the steps that person needs to go through

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  to really get engaged with, say, a subcommittee? It's actually pretty straightforward if you're an

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  ISPI member. So, there are obviously a number of folks who are directly, you know, leading those

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  subcommittees. Peter Gordon and Prem Iyengar for the applications model and data.

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  Robert Jacinich, Richard Jacinich, Robert Parks, and Jason Schneider for the preparedness and

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  workforce subcommittee, and Kabir Aiwal and Gert Throe for regulatory. But if you need some

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  direction to actually get, you know, set up into the groups that you're interested in, you can

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  essentially reach out to the contact information that's provided on the ISPI website for the COP,

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  and they will guide you through the process of making sure that you're involved in all the areas

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  that, you know, you might have interest in. So, as I mentioned before, as a member, you can be on

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  all the subcommittees. You can actually propose, if you're interested, to start up a new subcommittee.

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  There's lots of ways to get involved. So, you know, that direct link is, you know, straightforward to

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  make. And, of course, you know, obviously, myself and Eric can also be, you know, directly approached

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  if we need, if you need some assistance with that. Absolutely. Nick Armstrong's also the visor or

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  co-chair, if you will, of the steering committee as part of the three leaders. So, Ben and myself

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  and Nick. But also, with respect to that, thinking about timing, now is a great time to get involved

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  in this COP. It has been established for a little while now, but we're really kind of storming and

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  norming within these subcommittees, really looking at what are some of their own objectives and what

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  do they want to do to add to this community of practice? What are they doing in order to bring

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  knowledge and understanding to industry, to the ISPE community itself? So, there's no time like

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  the present to reach out to one of us, to go to the ISPE website, select the AICOP as one you're

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  interested in, and get involved. And really find your way and find your place that fits the

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  need and the interest that you have. And as Ben also said, we're willing to expand, right? We started

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  with three subcommittees because that's where we kind of coalesced around general interest with the

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  people we have. But it doesn't mean that we can't have additional subcommittees or people that spin

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  out of one subcommittee into another subcommittee. So, we're very open and dynamic at this point in

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  time. Again, storming and norming and really figuring out what some of those deliverables

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  and things are going to be beyond just what we started with already.

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  Great. Well, hopefully people now have a better understanding of how easy it is to get involved in

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  the AI or any community of practice with ISPE. So, getting technical for a minute,

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  artificial intelligence, machine learning, and digital twins each involve sophisticated

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  statistical techniques as their foundations. What's the difference between these technologies?

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  Yeah, I can take this one. But just to say right up front, as we talked about, the COP is a very

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  diverse group. And so, I am certainly not a deep subject matter expert on this. But I know from some

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  of the discussions with some of the really brilliant folks that work very deeply in the area,

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  some general aspects of it. So, I can touch on it a little. So, AI is really, I'd say, the broadest,

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  you know, delineation of the technology, right? It's essentially the computer science field that's

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  focused on actually generating solutions that will actually perform tasks that are very much like

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  human intelligence, right? And so, when we talk about AI, it's incredibly broad, right? You can

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  talk about, you know, for example, large language models, but you can also talk about, you know,

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  self-driving cars or even things that are relatively mundane. In some cases,

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  when we get into specific applications of that technology, for example, when we talk about

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  machine learning, we're talking about really a subset of that AI, actually a quite narrow subset

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  of it, where that is a set of algorithms that's actually using data to train the model to perform,

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  to learn like a human, right? And actually to start to be able to improve its performance over

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  time. And that's, I think, a key attribute of the machine learning-based models, because

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  many of the models which we use historically, particularly in manufacturing, were oftentimes

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  incapable of updating themselves over time or evolving, right? They were usually static models,

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  and you had to do quite a lot of work to actually update them. Or they were based on, you know,

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  physics and mechanistic-based models where it was really locked into your understanding of a

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  particular natural phenomenon. Digital twins are actually not so much AI directly, but it's really

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  just a digital representation of something physical in its broadest form. But where it

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  dovetails a little bit with AI and machine learning is that in a lot of cases now, companies like mine

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  and many others are using digital representations of manufacturing processes or, you know,

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  components of that manufacturing process to then essentially couple that to machine learning

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  models where you're able to use that digital representation and then couple it with a

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  machine learning model that's able to take in data from that digital representation and actually

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  update that representation. And then using that data, it can make real- time projections on how

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  certain things that are happening based on a digital representation are likely to make

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  downstream effects on, for example, product quality, right? So in the most, I'd say,

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  direct and obvious case for where you could see benefit here, you can directly couple that

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  prediction to an active control loop. So that information that's coming in from your process

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  real-time is informing the digital twin as being coupled with that machine learning model, which is

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  providing essentially direction to the process, is able to move that process to make sure that

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  the predictions are going to give you the most optimal outputs at the end. So it's more of, I'd

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  say, a part of how AI models are being used. It's not actually artificial intelligence itself.

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  Hopefully that's, you know, somewhat helpful to folks out there. I'm sure some of the subject

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  matter experts can give a much more deeper dive and be more informative on the subject.

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  And if folks are interested, you know, come to the COP, and I'm sure we can do some deeper

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  discussions on the topic. Well, I know that the aircraft industry has used digital twins for a

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  long time to model jet engines, failure modes and effects, reliability, and predictive maintenance.

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  And over the last several years at various ISPE conferences, I've certainly heard case studies

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  of digital twins being used in pharma. Do you know of any significant early adopters

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  of digital twins or AI? Yeah. I mean, I'd say actually quite a large percentage of the

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  companies that I know, including my own GSK, is actively using these models in various aspects of

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  their, both their development process, but also their actual manufacturing as well. So for example,

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  you know, we touched on a little bit before about, you know, the part that becomes

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  critical for sort of addressing regulatory considerations. There's a whole, you know,

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  big component of this that can happen and is happening right now that really, I'd say,

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  to some extent, is not, is not sort of within the scope of, you know, regulated processes or

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  needs to be, you know, a concern from the regulatory standpoint. So in particular,

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  when we talk about things like process development, you know, or early R&D development activities,

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  we can use a lot of these, we are using a lot of these models to gain insight from our

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  our previous data sets and to help us design, you know, new manufacturing processes. There are also

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  some cases, for example, right now that are active even for manufacturing processes at my company,

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  for example, where we're using it more for the purposes of monitoring and not so much to actually,

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  you know, itself directly impact an ongoing process. So good example, we call it

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  multivariate statistical process monitoring or MSPM. And so you can use essentially a twin of

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  your manufacturing process, have that model be predicting where certain elements of that process

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  are maybe going to lead to, for example, an excursion in a certain critical process parameter

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  or a critical quality attribute. And you don't have to have that model do anything itself to

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  the actual control of the process. It can be completely separate from that. And so it's not

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  actually a part of your GMP process, but it can be a nice tool for, for example, operators who

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  are working on the line who have, you know, set protocols and things they need to do.

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  But it can also get an early warning sign from these models to let them know something may be

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  going in the wrong direction. And so it's a really great tool to have. I think a lot of companies

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  are using it already, even though it may not be something that they're, you know, submitting,

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  for example, to regulators. There are also companies, you know, who are, you know, very

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  much also working with some of these models and actually actively engaging with regulators on

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  more advanced and more, I'd say, what you can call higher impact applications as well.

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  So the whole scope, I think, is being explored. And again, GSK and many other companies,

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  Roche, for example, is leading one of the sub teams. They're actively involved in this,

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  a lot of players in the area. You know, you mentioned multivariate

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  process control or statistical analysis. I have a close family member who 30 years ago,

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  working for Unilever Foods, modeled a pizza sauce process using multivariate statistical techniques

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  to predict how that process was behaving. So we've come a long ways in 30 years.

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  And I think you've already given two good examples. Yeah. I mean, it's been getting

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  used for a long time, right? Very quickly. You saw things like automation

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  is the statistical process controls and those sorts of things and bots. And that was more

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  kind of static systems that were programmed, right? So that was some of the early stages

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  leading into where we are today with true AI and more dynamic models. And I think

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  a lot of the early adopters kind of used that crawl, walk, run mentality where they

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  were more controlled, a lot more human oversight, more static type systems than moving into more

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  dynamic type systems that are really thinking and making some decisions and changes on their own.

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  So I always encourage people to use that approach, right? To start small,

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  begin with areas that are first non-regulated to understand what your scope is and what some

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  of the outcomes may be before they start using that analysis to make regulated or regulatory

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  type decisions. But you've seen a lot now, especially I come from a clinical background

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  and laboratory background. So GCP, GLP is a lot of the generative AI, large language models

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  to establish study protocols for clinical research, patient enrollment, enrollment

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  and recruitment at sites being used that way. But you've also seen it on the other side as well

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  from a machine learning. And the big difference here is really the data, the data to train the

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  algorithms and then the data that provides the output or the intended use of the system. And

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  you're seeing that with regards to pharmacovigilance. So areas of greater efficiency

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  to make the industry more efficient and cost effective, as well as safety from the perspective

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  of processing things like adverse event reports much quicker and drawing comparisons between what

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  might be minor adverse events, but pile up over time, right? So it creates a dynamic where you

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  can more easily process them more quickly, meet the regulated timelines for reporting of serious

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  adverse events or reporting of those, as well as looking at them across the board. So improving

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  efficiency and improving safety of the subject or of the patient as well.

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  So what's holding industry back from faster and more broad industry adoption of AI?

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  Are devaluation and change management a concern? And I think it's the guy in the dress.

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  Yeah, it's been a real scary industry with respect to, hey, is this going to be accepted, right? Are

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  people going to really flock to this and are they going to embrace it? And I think it's building

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  that confidence there first needed to be, again, a slow approach to building confidence and knowing

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  what you're being provided and that it's accurate and precise. And then moving from there and being

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  able to demonstrate that the intended use is indeed being fulfilled and you have the appropriate

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  evidence and documentation for regulators or in my case for a sponsor company, right?

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  So I think that was an initial reluctancy to do some of this stuff, but it's much more

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  pronounced today and really started that I feel is probably one of the highest risk areas is

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  probably around medical device, medical devices that are treating and helping deliver medications

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  and so forth and diagnosis for patients in a medical setting. But that's coming around and

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  people are starting to grasp and get more comfortable and understand the technology better

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  and things that we're doing within the COP and the new gap guide on AI that's coming out where

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  we address some of those things for people, I think to give them a better level of comfort.

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  Well, great. Sort of coming to a wrap up here, what key initiatives and topic areas

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  is the community of practice focusing on and also any further topics you want to discuss

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  from the GAMP guide on artificial intelligence? Yeah, I was missing a year. Do you want to talk

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  a little bit more about GAMP? Sure. I'm happy to talk a little more about the AI guide that

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  is coming out. So it's really focused around addressing the GXP compliant design and development

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  operation and use of AI in industry, in particular, also machine learning as a subset of AI.

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  But it really covers and really ties together and pulls together concepts that have been within

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  industry itself from a regulatory perspective, as well as within ISP as a community and their

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  communities of practice. So GAMP, looking at risk-based computer systems validation,

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  or today CSA, to ensure that the systems that either use AI or are AI- based systems themselves

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  meet and are applicable to their intended use. So verification from that perspective.

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  It also touches on and brings and pulls in concepts of knowledge management,

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  critical thinking, data integrity, all of those things that have been out there for quite a while,

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  but haven't been essentially geared towards AI specifically. So that's what the guide is really

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  pulling together, things that have been out there from a lifecycle perspective, how to validate,

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  how to test, how to do verification of some of these systems that have an AI or machine learning

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  component to, again, build that trust and that confidence within your organization and with the

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  regulators as well. But really taking what we've already known and applying it to now this

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  innovative technology in a manner in which it meets, again, those industry and regulatory expectations.

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  All right, crystal ball moment. You know, new technologies, there's early adopters,

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  and then the dam breaks, and all of a sudden everybody's doing it, and then you kind of reach

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  a mature stage where there's continuous innovation improvement, but you've gone through that

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  change phase. Do you see that same sort of adoption curve happening with

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  AI and related technologies, or will it be very cautious, tepid, crawling for quite a while yet?

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  What do you think? I'm pretty optimistic, actually. I mean, I think the, you know,

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  it's been pretty clear, at least from, particularly from the FDA side, but also from, you know,

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  EMA and outside of the U.S., that they're very supportive of the technology. And I think,

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  you know, to some extent, the, you know, as Eric was kind of mentioning before, like,

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  the barriers that people are sort of seeing are often, I'd say, more anticipated barriers. And

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  we just are trying to kind of make sure that we're kind of proactively working with some of these

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  folks on, you know, the side of the regulators because they want the same as us, which is to,

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  you know, responsibly deploy the technology in such a way that we can really speed up,

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  you know, the ability to benefit patients, but, you know, ultimately,

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  you know, get the most value out of it, too. And so a lot of what I think we're, you know,

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  would be some somewhat of the initial slowdown phase, because we're all just kind of trying to

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  figure out that initial, you know, the ground game, will, I think, to some extent, you know,

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  get sorted out as long as we keep having those types of dialogue. You know, there are things

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  that are coming out of left field, you know, legislation is something like that, right? You

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  can't always anticipate, you know, the way legislation will work. And we obviously will

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  continue to sort of try to work, you know, to understand how that is going to adopt or impact

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  the field more generally. But even in those situations, I think, you know, the regulators

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  often are very good at, you know, helping us to kind of find paths to, you know, both be continue

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  to be compliant, like Eric was saying earlier, but also to try to, you know, minimize the amount

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  of impact that we get, you know, from a day to day basis. Frankly, I think a lot of the,

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  you know, the sort of trajectory may largely be due to, you know, to some extent, trying to apply

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  it to lots of things that it ultimately may not be a great application for and sort of figuring

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  out where you get the most value out of it from the industry standpoint over time. And then seeing

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  where some of these models continue to evolve to be really, really helpful and get better. Other

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  ones maybe have some limitations that ultimately don't carry them through to more general use,

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  but I'm pretty optimistic about where things are going to go with it in general.

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  Eric, what's your prediction?

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  I think we're going to continue to be in this growth phase for quite a while in a very exponential

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  type of environment. I think it really got kicked off with generative AI and large language models.

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  Some of these barriers are sort of falling, especially with more regulatory guidance

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  coming out now as of recent, as well as the experience and the learnings that are taking

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  place in industry and things like the GAMP AI guide that's coming and more that will come out

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  of this AI COP itself with an ISPE. So I think we're going to be there for a while. It's not

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  going to slow down anytime soon. There's a large motivation to continue to use AI to help industry

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  again to deliver products faster to market as well as safer, more effective products. And I think,

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  you know, that's going to continue for a while. I'm not seeing any slow up.

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  I'm seeing things continuing to accelerate for quite a while yet.

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  Well, it's a very exciting time that we live in, and I'm really appreciative of the fact that we've

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  got this ISPE AI community of practice and the new GAMP guide on artificial intelligence.

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  It's great to see industry really moving forward with these technologies.

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  We heard that it's pretty easy for individuals to get involved in this community of practice,

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  and so I encourage everyone to take a look at either this one or some other

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  community of practice that relates to a topic of interest for you. Eric and Ben, thank you.

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  So thank you both for your insight and your leadership on this topic of artificial intelligence.

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  That brings us to the end of another episode of the ISPE podcast, Shaping the Future of Pharma.

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  A big thank you to our guests, Ben Stevens and Eric Stibbe, for joining us and sharing their

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  thoughts about the new ISPE Artificial Intelligence Community of Practice and the

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  upcoming ISPE GAMP guide, Artificial Intelligence. Please be sure to subscribe so you don't miss

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  future conversations with the innovators, experts, and change makers driving our industry forward.

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  On behalf of all of us at ISPE, thank you for listening, and we'll see you next time as we

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  continue to explore the ideas, trends, and people who are shaping the future of pharma.