Trends, Challenges and Solutions in the Life Sciences Industry Podcast

Emerson Automation Experts

The Life Sciences industry has undergone significant changes over the past several years. From responses to a global pandemic to the advancement of personal therapies, the need for automation and effective data management has never been greater.

In this Emerson Automation Experts podcast, Dave Imming joins me to highlight some of these trends, the challenges they have introduced, and technology’s role in addressing them.

Give the podcast a listen and visit the Life Sciences & Medical section on Emerson.com for more information on the technologies and solutions to help you drive greater performance.

Transcript

Jim: Hi, everyone. I’m Jim Cahill with another “Emerson Automation Experts” podcast. The life sciences industry is undergoing a significant amount of change in recent years. I’m joined today with Dave Imming to talk about some of these trends. Dave is the vice president of marketing and sales enablement for Emerson’s life sciences business and can comment on how these trends are shaping automation and data management strategies. Welcome, Dave.

Dave: Thanks, Jim. It’s great to be here.

Jim: Well, it is great having you, so let’s dive into it a little bit. Dave, what are some of the recent trends in life sciences?

Dave: Well, Jim, I think the biggest change or trend in the life sciences industry is the increasing number of technology breakthroughs and new modalities for biopharma now and in the coming years. This includes both the expanding biotech capabilities as well as these advanced therapy medicinal products, frequently shortened to ATMP. These technologies include cell therapy, gene therapy, and tissue engineering and hold incredible potential for new ways to cure diseases and also to improve our health.

In addition to that, while data has always been very important to the life science industry, there’s a huge drive to better capture and manage that data. Part of this is to streamline the release process, which is tied to collecting and verifying the appropriate data, but also to have that data available for additional analysis. And specifically, companies are beginning to work to see how they can harness artificial intelligence, or AI, to pour through that data and bring out new insights, and identify ways of doing things even better.

Jim: Wow. That does sound like a lot is going on here. So how are the new ATMP processes similar to the traditional biotech and, in other ways, how are they different?

Dave: Well, Jim, there are a lot of ways where they are very similar to traditional biotech. In both, you know, ATMP and biotech, the process development activity has lots of flexibility. But then this flexibility gets narrowed down as you move towards commercial manufacturing. Of course, in commercial manufacturing, there has to be very strict adherence to process requirements and almost no flexibility for obvious reasons of quality and consistency. But likewise, the recipe management, batch records, equipment and material tracking, all those things are quite similar across the various types of life science modalities. But there are also many differences.

When we talk about advanced therapy medicinal products, this space tends to have many more but smaller batches. Each batch is created for a specific patient population, and the therapy is highly targeted, which is great, but it does lead to lots of smaller batches, and in the extreme, it is referred to as personalized medicine or autologous, which is one batch for one patient. And by increasing the number of batches, this has a huge impact on the data collection and management requirements. Each data set with all of the information on materials, equipment, process conditions, all have to be captured and managed. For personalized medicine, it would also include the tracking of the patient samples together with the data for chain of identity, chain of custody, chain of environment, and all the parameters that might affect a sample. The reality is that these new therapies are amazing, but they are making the supply chain for life sciences much more complex than in the past. And in addition, many of these new ATMP processes are more manual and less automated in the past. And this is due in part to patient variability that must be accounted for and adjusted in the processing.

Jim: Well, that’s no small set of challenges there. So, I imagine automation can play a role to help with that. But, you know, it’s what we’ve been doing for years, isn’t automation automation?

Dave: Well, in some ways, you’re right, it is more the same in some cases. But as I mentioned, many of these new processes are done more manually, but with a fair amount of human intervention. And again, as I said a moment ago, this accounting for patient variability. So, this can be especially true of the personalized medicine or autologous therapies. In those cases, the, you know, automation, if you will, tends to be more of providing a guided or prompted path for the operator where the automation lays out, “Here’s the next steps that need to be completed and the data that needs to be collected for that.” And then the operator would adjust these steps as necessary based on the patient sample characteristics.

So, another difference is that while traditional pharma and biotech certainly utilize skids, this is done to a much greater degree in the ATMP market, utilizing specialized skids combined together or creating a work cell for that particular therapy. Scheduling can be very challenging in this space, in part due to the bidirectional flow of samples and therapeutic products, personalized medicine, and allocating capacity against patient demand, and ensuring that the round trip from collecting the patient sample, processing it, and getting it back to the patient is all done within the allowable timeframe for that particular therapy.

So advanced control techniques is another area that can be leveraged in some cases, and analytical capabilities like PAT [process analytical technology] or Spectral PAT data can be used as soft sensors, which allows operators to have real-time information on some of their metrics where they normally have to wait for a lab sample to be processed. So having that real-time metric using spectral PAT technology can be a real benefit in some cases.

And another thing is that with many of these batches, when they have more of them, it’s smaller in size, while the automation is similar, the throughput of just the number of batches that they’re dealing with increase significantly. So, it’s important that your automation system is up to that task of the higher capacity. Some of our customers that we work with are increasing their capacity in multiples of 10x, you know, each year as they increase the capacity for these new therapies. It also dramatically increases the demands on your data management system, as you might imagine, to capture all of that data from these mini batches, set the data in context so that it can be easily retrieved, consolidated, and as well, compared and analyzed in the future for new learnings.

Jim: Wow, that 10x growth made my eyes widen in there. Growing that kind of capacity in there sounds like scheduling of all these batches, particularly for ones of those autologous…and I think I just learned a new word here today. Those autologous therapies would be challenging, is it?

Dave: Oh, yes, it absolutely can be. As I mentioned earlier, there’s the challenge of moving the patient samples to the operational area and then back, all while tracking the environmental conditions in both directions and getting it completed within that allowable time that has been put forth for that particular therapy. So, it makes it especially important that things run as smoothly as possible in the operations process. For example, Emerson’s DeltaV Real-Time Scheduling has been used in this space, cell therapy offerings, to manage the schedule and keep things on track for the production process.

DeltaV Real-Time Scheduling is extremely dynamic and can respond and adjust or alter the overall plan schedule based on real-time conditions. This might include variability in processing due to cell growth. It might be due to unplanned events, like a specific skid or piece of equipment having a fault, or perhaps being unavailable for some reason. So now what? Well, the “now what” is handled by DeltaV Real-Time Scheduling, which takes the event into account and then adjusts the schedule automatically with the least impact on production. There’s also a companion product [DeltaV Discrete Event Simulation] to the scheduler for capacity planning and debottlenecking. This product can be extremely helpful to help users see and understand where the dwell times are in the process and what opportunities they might have to reduce the cycle time and, therefore, increase their production capabilities.

Jim: Well, those technologies sound like..I just envisioned the big spreadsheet and trying to keep up with it manually and optimize as best you can so to have that online and be able to say, “Okay, now here’s the schedule,” that seems like that can go a long way for the efficiency of the operation. Now, Dave, you had mentioned data management and real-time release. Can you elaborate a little more on those?

Dave: Sure. And so, what I’m talking about there is that when they do the production of these, you know, drugs or therapy products, before they’re able to release them and get them shipped out, they have to go through a quality review or release process to do that. And, of course, a lot of what drives that process is data. So, they have to make sure that they can sync up all the data associated with that particular production batch. And in the case of personalized medicine, that would include the patient samples being synced to the processing, to the materials used, the equipment used, and put all those in context with the batch information. And then any exceptions that occurred during the production process need to be captured during that process and then resolved prior to when they can do the release.

One of the offerings we have is called Quality Review Manager, which helps customers review and resolve captured exceptions so that they can go through those, figure out what’s happened, and then resolve them and release the material more quickly, which again, that helps a company be much more financially successful. And of course, for these autologous or personalized medicine processes, the timing is often crucial, which relates directly to capturing the right data in real-time and getting it released and shipped because the shelf life of these treatments can be quite limited.

Data is also critical in both directions. When a sponsor company uses a contract development and manufacturing organization, or CDMO, the data on how to do the therapy must be transferred to the CDMO. And then when a batch is executed, the CDMO typically transfers the batch data, either some part or all of it, back to the sponsor organization for them to confirm the release. So again, the supply chain is much more complicated in these new therapies. And everyone in the industry is moving towards more sophisticated data stores, sometimes replacing, but more often augmenting their time series historian with something that can accommodate many more data types in which can set the data in context, which helps them make the post-processing analysis just that much more valuable. And as mentioned earlier, people are beginning to explore what artificial intelligence can do for them to gain more insights from their data or help them do their operations work.

Jim: Yeah. It just sounds like the complexity is all around that and trying to manage it and make sense of it and all is just a growing challenge. So, what product offerings do Emerson and AspenTech offer to help with all this?

Dave: Well, there’s a number of things that we have, we have a very comprehensive portfolio for our life science customers. And in the area of data management, you know, some of the ones that come to mind immediately are this Quality Review Manager that I mentioned, which is part of our DeltaV MES [Manufacturing Execution System], or formerly known as Syncade MES offering. We have IP21 [Aspen InfoPlus.21], which is a time series historian. And then we have [AspenTech] Inmation, which is a data management product from our AspenTech partner. And Inmation is an extremely flexible database which can handle all sorts of data types and also put them in context, which helps people utilize the data, find the data, retrieve the data much more easily than without that context, and knowing the relationships between data that then later AI analysis can create new conclusions and new insights by looking at those relationships between the data, that’s all based on the context that Inmation provides. So those are probably the three top offerings that would come into play in this data management space.

Jim: Yeah. It sounds like having that glue to take these different sources and methods and all that, make sense of it, and then add the AI on top of it to help make sense of it and point you in a path in the right direction, that does seem helpful there. So, does Emerson have anything to help life science companies with managing how they transfer data or data management, I guess, you know, products to help get these products to market faster?

Dave: Yeah. Absolutely. That is a huge area of interest. Jim, as bad as the pandemic was, one of the good things that came out of that experience for everyone in the life sciences industry was to see what is possible in terms of accelerating the pipeline and moving something from laboratory into production, and get all the checks done very quickly and efficiently. And so, that’s really started a lot of wheels turning in terms of how we can do that better.

Emerson has a number of things going on in that area. One is in the area of process knowledge management. In that category, we have an offering called [DeltaV] Process Specification Management, or PSM. And Process Specification Management helps a life science company gather, collect, store, and organize all the information as they’re doing drug development. And they help pull all that specification information together so that it can be submitted for approvals to the regulatory bodies and also that it can be passed downstream when they need to run pilots for clinicals or when they need to transition to commercial manufacturing. And this is something where making that tech transfer much faster than it has been historically.

In the past, this has taken years to move something from the lab into commercial production. And Emerson has created a consortia of companies to work together with Emerson to tackle this problem and come up with some really game-changing technologies to make that tech transfer much, much faster than it’s been in the past. And it’s a combination of some of the current offerings, like Process Specification Management. There’s also [DeltaV] Process Risk Assessment to help you look at the risk associated with various techniques as you’re doing your development, together with this tech transfer effort that we’re doing with the consortia to see if we can really cut that time by, like, an order of magnitude as we go into the future.

Jim: Now, you talked a little bit about collaboration in that area, but I understand there’s also collaborative efforts going on in the life sciences industry. Can you talk a little bit more about that?

Dave: Yeah. Absolutely. There are many different organizations, industry groups in the life sciences industry which help companies, both end users as well as some of the vendors that serve this market, share ideas and help tackle some of these problems together. BioPhorum is one of the ones that we spend time at their meetings. I’ve been to a number of their sessions. Really, really good collaboration with other folks in the industry and talking about some of these problems and how we can make things better. NIIMBL is another one that we work with very, very closely. ISPE, another great organization that helps move the industry forward. And there’s others, but those are some of the big three that Emerson and AspenTech work closely with.

They work on things like standards, ontology, the nomenclature and the data structures that we wanna have a common way of talking and passing information back and forth, which can make things much easier. We work on pilot programs, we work on proof of concepts to take some of these new technologies and find a place to try them out, and then publish that so that others can benefit from that research. So, a lot of great work being done by those organizations, and we’re very happy to be a part of those efforts.

Jim: And I invite our listeners, if you’re on the blog, do a search on, like, BioPhorum, because I know our subject matter experts get involved in a lot of what gets developed and then published as part of it. So, we’ve recap several of the things they put out over the years, so I invite our listeners to go find that. Well, this has been a very educational discussion and one I really appreciate you sharing, Dave, but where can our listeners go for more?

Dave: Well, yeah, we have quite a bit of information up on the website, as you might imagine. If you go to Google and you google “Emerson” or “Emerson Aspen Tech and life sciences,” you’ll very quickly find our Life Sciences and Medical page, and from there you can find more about, you know, some of these ATMP therapies or markets and the different products and solutions that both Emerson and AspenTech have to help these customers and our life science partners deliver some of these to patients.

Jim: That’s great. And some of the products you mentioned along the way, I’ll make sure to include hyperlinks to where there’s more information available on all of those. Well, Dave, this has been great and a lot of fun. Thank you so much for joining us today.

Dave: Absolutely. Happy to do it, Jim.

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