 Hi, good morning everyone. Thank you for joining us today in the sixth webinar in the ASIL webinar series. My name is Josephine Lam Bong and I'm the Senior Manager of Science and Industry Affairs at ARM. Today's webinar is on the topic of control strategy and will be delivered by Nolan Poulsen, who is the lead contributor of Chapter 10 of ASIL. So for those who are not familiar, ARM is the leading international advocacy organization in the benefits of engineered cell therapies and genetic medicines for patients, healthcare systems and society. As a community, ARM builds the future of medicine by convening the sector, facilitating influential exchanges on policies and practices and advancing the narrative of data and analytics. We actively engage key stakeholders to enable the development of advanced therapies and to modernize healthcare systems so that patients benefit from durable and potentially curative treatments. As the global voice of the sector, we represent more than 475 members globally, including emerging and established biotechnology companies, academic and medical research institutions, as well as patient organizations. As I'm sure everyone here knows that manufacturing for cell and gene therapy is a complex process and it's often a rate-limiting step in the commercialization of regenerative medicines. The industry overall is in the process of rapidly scaling up, and there are more phase three studies and more commercial product approvals that are anticipated. However, products are being held up at the VLA stage due to CMC issues. We see that advances are being made in the manufacturing technologies, and the experience in the workforce are overall improving. However, standards and best practices in the field are not advancing as quickly. So this is the pre-competitive area where ARM focuses our efforts in to further advance the process understanding in this industry to facilitate product development. ACE-CEL is a project that ARM undertook several years ago after feedback from our members about the CNC issues that the field as a whole is experiencing. It is a best practices document which incorporates collaborative case studies presenting the implementation of quality by design and cell-based therapies. And to make A-Cell an effective resource and reflective of the ongoing innovation, we chose to focus on immune cell therapy as the case study, and in particular, we further focused on autologous CAR-T cell therapy, given the recent approvals and the number of products that are available to the patients currently. The ASO effort follows the success of AGEEN, the sister project of ASO, which focuses on AAV vectors, and AMAB, which was instrumental in allowing advances to occur in the monoclonal antibody industry. We're very fortunate at ARM to have a large number of subject matter experts from our member companies. There are a lot of contributors for this project from more than 30 member companies, and this project is also supported by Nimble, who generously provided funding for ASIL. The document is intended to benefit the entire field, so it is available as open access in our website at the link provided here. This is what the contents of the documents look like. As you can see, it covers quite a big scope from regulatory considerations, comparability standards, all the way to facility design. Today's talk will cover the elements of control strategy that are laid out in Chapter 10. The webinar today is the sixth out of the nine-part webinar series for the ASEL educational rollout. The schedules for the webinar are provided in this link down here. Our hope is that ASEL, both the document and the webinar series, can serve as an educational tool to the community and we hope that the document can be used by product developers, educators at universities, and we also have the aspirational goal that this will be used to help educate the regulators. And before we move on, just one housekeeping item. You can use the Q&A function to ask the questions and we'll try to get to as many of them as we can. If we didn't get to your questions, we will compile them and post a response in our website. And with that, I'm sure you're not here to hear me talk, so I would like to introduce our expert speaker for today's seminar. Dr. Nolan Poulsen is the Vice President of Strategic Product Quality at GlaxoSmithKline, where he and his team oversee global end-to-end product quality of cell therapy, biologics, and small molecule programs during clinical development and commercialization. He is an experienced professional in providing program CNC leadership, product quality, and technical oversight during CNC development in support of commercialization efforts for large molecule and cell therapy programs to global markets. Nolan was instrumental in the successful commercialization of two cell therapy programs, Brianzi and Abekma, that were approved by the FDA in 2021. and Vecma that were approved by the FDA in 2021. He previously worked in the industry with increasing levels of responsibility within R&D, quality control, formulations, global product quality, and CMC leadership at Amgen, Janssen, Juno Therapeutics, Celgene, and BMS. He has a keen understanding of process development and considers quality as a strategic partner with the technical teams to drive holistic control strategies to ensure product quality and patient safety. So Nolan, thank you again for taking the time out of your busy day to be here today and I will turn it over to you to start your presentation. Thanks Josephine, I appreciate that. So very kind introduction. So let me see if I can get my able to share. We'll get this thing going. This will be good. So let me know if you guys can see this. OK. OK. Now, so thanks for the introduction, Josephine. This is a pleasure to be here. It's it to be involved in the A-Cell program. Certainly, you know, when I first started my career, looking back at the AMAP case studies in the biopharm space, it was amazing to kind of see what was done there and how much we were able to leverage that across the industry. And so I'm hoping that the contributions that were made, not just for me, but I'd say from the body of individuals and support team that I worked with at Juno Therapeutics, Celgene and BMS, and certainly different aspects here at GSK, contributed to all of this. So, you know, a big shout out and a thank you to all those individuals that were there, part of the team to support this. So wonderful, wonderful time. I wanted to start off, and this is just, for me, it gets very personal, right? Why is this matter to me? And this is really what's been foundational to me. I've been able to meet Emily Whitehead. This is a picture of Emily Whitehead on the left. She was the first pediatric patient diagnosed with ALL. whitehead on the left. She was the first pediatric patient diagnosed with ALL. And she was first dosed with Kimraya. And so the first cell therapy pediatric patient, and obviously, it's been amazing to me to see the progress and the life-saving capabilities of these cell therapy programs. Emily's the same age. I have a daughter named Hadley that's the same age. And I can't imagine going to the doctor when she was age five, wondering if she would ever make it home. And that's what the Whitehead family had to go through. And so I just, I can't help but feel inspired by that experience and what was done there by the doctors at UPenn. On the flip side, and this is one on the other side, this is a picture of my father on the right at age 78 was diagnosed with diffuse large basal lymphoma. And so the wonderful thing from my perspective is that he's since gone through chemotherapy. He's in remission right now, which is good. Based on his age and based on the cancer type, it's comforting to know that all the hard work, blood, sweat, and tears that went into supporting the commercialization of Branzi at BMS has the ability in the future to potentially dose and support and save the life of my parents. So to me, it really comes home to say this really matters, right? And the power and the concepts behind cell therapy is huge and it makes a big difference. And I just can't imagine a world without this life-saving medicine and the potential benefits that it has, not only for the two individuals here, but for countless number of individuals across the world. So certainly there's many challenges and certainly many opportunities in the cell therapy space. One is, you know, you're dealing with life cells. And so there are many, many different attributes to be characterized. If you look at the acceleration and advancement in the field of cell therapy, it's rapidly evolving. There's new technology being designed and implemented all the time, new processes, new analytics. And so we're always continuously learning about what makes the most sense, what is most important for patient safety, product quality, and certainly what is the most important from a regulatory agency perspective. What are those weak signals or hot buttons that we need to worry about? The one unique thing that's certainly a challenge and an opportunity is when you look at autologous therapy, and this is not necessarily to quite the extent in the allogeneic, but it's certainly principles apply, you have single patient to single clinical outcome. So you can really start looking at patient centric specifications, which have been a buzzword across the industry. This is truly the epitome in the autologous therapy space where you can directly correlate one clinical outcome based on a CQA profile from the actual drug product, a CQA profile from the actual drug product, which is huge, very powerful. And as you accumulate all of that data, looking at that clinical correlation of those CQA profiles, you can have a direct correlation and a direct understanding of does this particular CQA matter to the patient outcome and the benefit and have those direct conversations with regulatory bodies to understand is this a fluke? Is this statistically significant? And does this matter to that patient population? So it's certainly a very unique, and we'll get into that a little bit later too. And then certainly one of the challenges obviously is the source material, and I'll talk through this. There's a huge variability component based on the input material as well as how it's being managed, how it's being transported, stored, all the rest of that. So most of you, if you're on this call, you've probably seen the general CAR T cell manufacturing process, you know, starting with apheresis, going through transduction and reinfusion into the patients. And when you start to looking at it from the frame of the controls, you know, what other controls do you have? Obviously, you have drug product type, you know, different specifications, whether it be for the input apheresis material, your intermediate specs, your viral vector specs, your drug product specification. You have all sorts of in process controls critical process parameters that trying to manage the manufacturing process certainly there's it's embedded across the board to make sure you have a very thorough extensively characterized process to understand what's going on and then there's obviously the raw material and gmp controls and then certainly there's periodic controls as it relates to validation, comparability, and your product reviews and stability. So as we go through the control elements discussions today, we'll probably touch on quite a few of these as we go through this. If you look at the evolution of a control strategy, really it's your holistic integrated control strategy is created from smaller scale control strategies, basically process parameters, material attributes, procedure controls, and analytical testing. And so all of that underlays by your process understanding and your product understanding as it relates back to your QTPP, your potential CQAs, and through the course of characterization, you'll truly understand what's most important. through the course of characterization, you'll truly understand what's most important. How do you refine and adjust your process studies, your analytical capabilities? What's the most important from a material attribute perspective? And then certainly there's an overlying risk across the board. And I think that the most critical piece, in my opinion, is making sure that there's that feedback loop from the clinical output to make sure that you're constantly reviving and understandingments and opportunities for improvement so that we can, by the time we commercialize any product, have the most highest likelihood of success and the best product that you can have being presented. Certainly, there's a level of risk. And just like what Josephine had mentioned mentioned there's many other chapters that go into many different topics so I'll just preface this there's lots of strategies that are that will be discussed today that there are many different and more extensive documentation on in the earlier chapters compared to the chapter 10 control strategy discussion but in the earlier chapters compared to the chapter 10 control strategy discussion. But if you look at the control strategy, obviously the key is to make sure that we have a risk-based approach for the control strategy definition. You're really trying to understand the science. You're really trying to understand what makes the most important. You're trying to minimize variability and really trying to make sure that you have a clear understanding of what your set of controls, what do you need to have to make sure you have product and process understanding to ensure product performance and product quality, and making sure that you have a consistent process that continually delivers efficacious and safe product to the patients every single time. And so every single component that we're looking at from a product quality control strategy has to consider risk. And there are many different aspects of risk and how you're documenting that. And we'll go through some of those here as well. So obviously, the real question is, during a manufacturing process, what are the specific controls? What are you trying to look for? And how do you establish a suitable control strategy? And really understanding the correlation between your process and your CQA profiles is essential to make sure that you have a well-characterized process and that you can deliver a safe and efficacious product every single time. Obviously, those tools, there's flow diagrams, SIPOC, IPO, which is the example to the right, your process inputs, your process and your outputs that really frame up what does your process look like? What are the potential inputs? What are the potential control mechanisms? And what are the outputs that you're expecting? And how would you measure those? And so this is a set of controls that can be put in place just to really understand the process from the holistic fundamental understanding to really define what's likely important and what's most important. And obviously, the criticality of process steps is determined by the impact to your CQA profiles. And when you start looking at this, well, how do you do that, right? And there's a whole host of activities and characterization studies that can be done. And so if you look at the figure on the right, you start looking at all the different inputs and testing and analytical strategies as it relates to phenotype and function transduction efficiency, as well as just the entire production. And so there's a holistic way of looking at all of these different parameters, and you can do them one side by side, single attribute at a time, which is a very slow and methodical process, very thorough, but it's not very cost effective. Or you can use a design of experiment type approach where you're looking at multiple inputs, trying to deconvolute the assessment of practical impact of a particular parameter as it relates to your CQA profile. And so certainly, you know, if you were to go through and say, what's the best that you could probably do, making sure you have a very well robust understanding of design of experiment methodology, DOE approaches, and leveraging those to support your process characterization. Obviously, if you have FME analysis, and then really you're trying to make sure that what you do is you're making sure that you have normal operating ranges as well as proven acceptable ranges to understand is your process as it's running in a state of control, obviously things happen where you will have issues and there will be challenges and something may happen and you may have a deviation. You have to make sure, is there any practical impact around this proven acceptable range to your CQA profiles? And all of that leads you down to understand what's the most important from a CPP perspective or in process controls. The one thing that I wanted to mention on the FMEA risk assessment, obviously this facilitates an increased level of characterization based on your potential risks. But I would say making sure that you have a very thorough understanding of your potential failure modes and is very comprehensive is going to set you up for success so that you can understand, okay, have we captured everything? Have we thought of everything? Doing an FMEA that's just there to check the box and doesn't necessarily get into the level of detail that you'd expect may drive you into a situation where you're starting to identify issues, challenges, or gaps, either during the review process with the regulatory agencies or during late stage validation activities that may set you back and it may delay the commercialization efforts. So making sure that you have a thorough FMEA risk assessment tool to truly understand what's going on at each particular unit operation. And the risk assessment is described in a separate chapter as well. So when you start looking at process validation and process comparability, obviously there's a lot of different CAR T considerations. Obviously you've got the FDA guidance on process validation, where you're looking at process design, truly understanding and characterizing the process, doing the qualification or the validation activities for stage two, and then making sure that in stage three, whether it be through annual product reviews or whether it be through periodic CPV type studies, you're continually operating in a state of control. And that would be critical there to make sure that your process is in a state of control. I would also emphasize making sure that your analytical methods are in a state of control, so you don't start to see drift or shifts, or when you start changing critical reagents or reference standard materials that you start to see drifts. You've got to make sure all of that's built into this CPV process so that your process and your analytics are operating in a state of control. Obviously, comparability is critical. I think everybody probably could describe many different comparability strategies, but making sure that pre and post change are comparable in terms of safety, purity, and efficacy is critical. And I think when you start looking at, well, what does this mean from a CAR T consideration? Really it's a matter of making sure that you have the similarities established using a paired run approach. And I'll get into this a little bit, but essentially what that means is when you start looking at healthy donor materials, and this is an example, when you look at donor to donor variability, it's quite extensive. And this is for healthy donors in the autologous cell therapy space. And this was a study done at BMS previously. And so if you start looking at the donor to donor activities, you start to see many different, you know, significant shifts. And when you start to look at the variability from donor to donor versus, you know, in this particular case, it was, you know, condition one, two, three, four, and five. But you could imagine if this was, you know, in this particular case, it was, you know, condition one, two, three, four, and five. But you could imagine if this was, you know, pre-change, post-change, what does that look like? And are you truly assessing comparability? And so a paired run approach basically says, or approaches a comparability strategy where you have a healthy donor, their apheresis material is split into process one and process two. And so then you're looking at pre and post change, whether it be on vector, whether it be in process improvements, whether it be in analytics. So you can really try to deconvolute what the process differences might be compared to patient to patient variability. One thing that may be a unique challenge is when you start looking at this, obviously in the biofarm space or in the small molecule space, you're looking at PPQ and doing this on three batches and calling it good. Because you have a potential contributing factor for patient variability, you may have to do much more than just n of three to make sure that you have statistically significant ability to evaluate that process one and process two change, and basically doing a mini comparability assessment in multiple donors, and then trying to deconvolute that variability from the donor versus the process. Obviously, the material attribute controls are critical. So obviously there's a whole chapter around material and starting materials. Obviously looking at, you know, what is the potential impact on chemical and biological impurities, adventitious agents, tumorigenic impurities, as well as any other materials that may cause an immunogenic reaction. And then similar to what I had mentioned before, patient material is critical. And I have another slide on this as well. Obviously, making sure that how you're collecting the apheresis material, whether it be frozen, whether it be shipped within a certain number of hours, making sure you have validated transportation activities or processes to collect, manage, and maintain chain of custody of that patient material is critical. Because if those aren't correct, you start to induce additional sources of variability that may hinder you to truly evaluate whether or not you have a controlled process or whether or not you're simply detecting variability embedded in your upfront collection and sample handling. Obviously Obviously the health of the cells and the patients. So you start looking at prior treatment options from the patient. What are their disease state? What kind of prior illnesses? All of those impact the patient material and increase the ability or potential for source variability. Obviously, your vector controls, all of those are critical to making sure that you have a well-validated, suitable control strategy there as well. And then, ancillary materials. So I couldn't say enough about single-use systems, vendor qualifications, supplier chain vulnerability. And I kind of look at the supply chain vulnerability just with what we've gone through in the last few years with COVID. And you start looking at that. And even at GSK, we've had instances where there's been challenges because single-use systems are either sole-sourced or sourced from various regional jurisdictions throughout the world, some of which are more challenging to get than others. Some may have various to get than others. Some may have various quality standards than others. And so it really creates a potential vulnerability for the industry, making sure that you have a robust supply chain. You've got systems that will be able to be leveraged and utilized in the time when the patient needs them. I think car team manufacturing and distribution and disposition and support and reinfusion back into the patient is critical. And so any delay, whether it be one day, one week, or one month in any of these materials has the potential to impact that patient in the end. So making sure that we've got that robust supply chain and the attributes are critical for CAR T systems. And so when you start looking at patient heterogeneity, it's the primary source of variability for cell therapy programs. And so obviously the autologous cell therapy is certainly impacting the most in this particular realm because you have direct patient to patient heterogeneity, whereas the allogeneic type program, you've got multiple pools of healthy donors. And so you can reduce that potential variability. But certainly when you start looking at this, and this is an example of during this study where you had multiple donors and multiple conditions that were being studied, you start to look at the donor variability and you look at that and the donor variability was actually 82% of the total variance observed in this particular study. And then you start looking at process variability is 12.2%, analytical variability is 5.5%. And so it really demonstrates the fact that it is a challenging field and you have to be ready to make sure that all of your statistics and evaluation and analysis takes into account you may have a large portion of variability due to the patients and therefore you've got to make sure that your process and your analytics are tight and truly understand this in a statistical manner. Now, obviously, there's a lot of controls as you look at this from a procedural and a facility perspective. Looking at CGMP, and I look at CGMP, the most important is C, which is current, obviously. at CGMP, the most important is C, which is current, obviously. And if you look at the general GMP principles, obviously there's many different aspects that make a well-rounded, robust control strategy embedded in the manufacturing site. So obviously aseptic processing as well as GMP procedural controls, not only in your process, but also on your equipment, your environmental monitoring and facility design is critical as well. And on the triangle on the right obviously talks about your design, your cleaning, disinfectant studies, as well as your environmental monitoring program are critical to make sure that you have that foundational element for the controls internally. Obviously, you know, the most critical or one of the most critical is personnel training and using aseptic techniques. There may be some processes. I think most processes within the CAR-T space are designed to be closed systems. There are instances where they're not. And so making sure that you've got very good aseptic technique to minimize potential sources of variability in training. Obviously, contamination controls, whether it be through raw materials or any other environmental controls, and then making sure that you've got GMP batch records and execution. So you've got all of these embedded, a well-rounded structure that creates a foundational element so that your procedure, your facility, your documentation, your GMP-esque type approach is critical to making sure that you have a well-rounded, very robust system. That way, when you're undergoing scrutiny by the agencies during the review or during an inspection type process, you can demonstrate, look, we've got all sorts of controls, not only what's on paper, not only what's on, you know, based on the characterized and the technical understanding, but from a GMP element, we also have additional procedure controls that protect patient safety and product quality. Now, obviously, there's analytical testing controls that are critical throughout the development. Obviously, you're going to have phase-appropriate analytical testing strategies. They will evolve over time. Your selection of assays, your robustness around the qualification and validation of those assays will continue to improve throughout development. So obviously, what you're using in a preclinical non-GLP tox study will be very dramatically different versus what you're going to file in your commercial control strategy for your commercial filing. So obviously, all of these need to take into account, you know, what kind of method robustness do you have? Is it suitably qualified? Is it suitably validated? What kind of repetition do you have? What kind of acceptance criteria do you have? All of those are critical in making sure that we have, you know, as we go through development, which may take, you know, one to two years, depending on how this goes from a CAR T space, obviously adapting and making improvements to your methods as well as processes is critical. And then adapting to those, making sure that you have, you know, routine release testing as well as characterization embedded throughout. And so the other piece that I wanted to mention too is if you look at process analytical technologies, this is a powerful tool where you can leverage inline or atline analytical technologies to increase your analytical results, decrease your transcription time, increase your vein-to-vein time, because then you're getting outline testing that you don't have to send to a lab and test and then get results back. It's immediate results that'll help support a robust and efficient process. So anywhere where you can leverage that certainly is critical. Obviously, this outlines some of the analytical testing that is used throughout in-process production, and this is embedded as a part of the analytical chapter in the ASEL program. So the one thing that I wanted to mention here too, is when you start looking at analytical methods and development, really the need for innovation is critical. And obviously there's complex cellular phenotypes. You're looking at all sorts of cell health, phenotypic compositions, antigen function. So really want to make sure that you have a truly powerful technique that can leverage those new technologies. Certainly there's rapid and complex methodologies that are being developed all the time. You look at the Acelix platform, which is a cartridge-based system, which is an amazing system that can be leveraged as long as it's, you know, adapted and validated to support the actual intended use. So, but I think, you know, when you start looking at all of the analytical method improvements, the whole desire here is to minimize the vein-to-vein time. And so by the time you look at some patients, and this is what was interesting during development of branzine abecma, you get to the point where you start manufacturing a drug product. And a week or two into the manufacturing process, suddenly you get a notification that says, you know what, you know, suspend that process. The patient didn't make it. and make it. And you start to think about that and you start to realize and understand every day counts and every little bit of time to minimize that fine-to-vane time gives extra hope for the patients over time. So anything that we can do to leverage and increase the use of new technologies, drive automation, and increase use of digitalization to reduce that time is all the better. And so these are just some examples of flow cytometry or, you know, assays, qPCR, endotoxin methods, or sterility, where you say, okay, what's the current versus what's potentially new technologies. And it's amazing to see the power of some of the technologies. And so, you know, you look at the flow cytometry from a conventional platform versus a SELEX is a very dramatically different. Obviously, same thing for immunoassays, qPCR, austerility, and endotoxins. So anything that can be done, should be done and leveraged, you know, to support that. So now one thing that I wanted to talk through is really this coil of analysis. And there's been obviously there's been many different presentations on this across the industry, many different papers published on this. But I wanted to make sure that this was critical. published on this, but wanted to make sure that this was critical. And I think this is probably one of the most important aspects of building a meaningful control strategy in an autologous space is making sure that you can drive and align the quality attributes to the patient-centered specifications based on clinical output. And so obviously you can go through and look at all of your purity, identity, strength, potency, safety, cell health type studies and analytical tools and making sure that the results of those, those product attributes can be tied to patient factors as well as correlated to clinical outcomes. And then theoretically you could build those into translational markers as well as correlated to clinical outcomes. And then theoretically, you could build those into translational markers as well. And so obviously, you know, in a routine clinical study, you're looking at safety, efficacy, and pharmacokinetics. And so all of those are able to go through and really is a powerful tool during the review process to be able to go through and say, look, we have a wide variety of CQA profiles. What we have identified is that even, you know, even the ones that were extremely low or even beyond the clinical specifications, actually out of spec, actually provided some clinical benefit to that particular patient. And there was no safety impact. And so it's a powerful tool where you can go through and leverage that. I think the real question, and this is, you know, this is kind of gets to the same kind of thing, you know, what's the most important as you look at what's the appropriate specification, right? So if you look at your clinical studies and you look at your example CQA profile, you may say, okay, is your blue line, should that be your specification based on a conventional statistical analysis that might be expected in the biopharm space? And then the real question is, okay, well, given the fact that you have all these out-of-spec results that are between the blue and the red line or below the red line, right, should your red line be a more appropriate specification because you've demonstrated that you have appropriate clinical safety and efficacy for those particular patients and therefore you can leverage a slightly different tolerance interval approach. And that's where, you know, the real question is, are these outliers? Are they true, you know, random patient variability? Are they true outliers? Is it based on the disease state? All of those things need to come into consideration as you start looking at all of these different components of your control strategy. And this is an example where you start looking at the Kaplan-Meier curves, and this is built into quartile. So the lower quartile of a CQA type distribution, lower middle, upper middle, and upper. And this is where you can start to look from the clinical perspective to say, okay, well, based on all of your upper and lower quartiles, do you see any practically significant difference in your clinical output? And so obviously the scenario one essentially demonstrates that your lower middle, your upper middle, and your upper quartile distributions of your CQA profiles essentially give you the same clinical benefit. And so that might be an argument where you go in and say, look, there is no practical impact or adverse impact to patient safety or product quality and therefore we believe that the entire distribution of your cqa profile could be leveraged to support your commercial control strategy and then certainly on the right you've got scenario two where you start to say okay well what are those critical important positions and you start to see where there's almost an edge effect where that lower quartile, the line in red, starts to demonstrate some adverse impact or a potential different impact compared to your lower middle, upper middle, and your upper. It may be obviously that if you start looking at this, your lower quartile doesn't mean that it's bad. It just simply indicates that your clinical benefit is slightly different. It doesn't necessarily mean that it's adversely impacting compared to the patient. So anyway, so these are just two different ways of looking at how you can correlate your data, how you can break it up, how you can look at your clinical output and try to understand does this allow you to justify a wider specification versus a tighter specification as you go through and move this, move your control strategy and how you have those discussions with the agencies. So obviously when you look at kind of the developing of the integrated control strategy, this is builds, I'm gonna just leave it like this. Your integrated control strategy has to build upon a solid foundation of what your platform CQA profiles are gonna look like based on a QTPP. It has to start out with some of your process characterization and it builds. So as your manufacturing experience builds, as your preclinical and clinical experience builds, and your first in human pivotal and your commercial, your characterization studies will build, your comparability studies will build on your product and process understanding, which leads you up to validation and final commercialization. And so obviously it's an iterative process. So this is not something that you can simply say, we will have a commercial control strategy at a clinical stage. You've got to make sure that from a risk-based approach, you're taking phase appropriate control strategy decisions. You have obviously CQAs that are non-negotiable. You also have prospective CQAs that you just don't know early on in development what's the most important and really making sure that you have a risk-based approach. Obviously all of this information continues to build as you execute your product characterization studies. You have iterative comparability and then obviously you have this ability to look at correlative analysis. So you can have a preliminary correlative analysis based on your first in human or your phase two type studies. And then obviously you can have a final corallive analysis based on your final pivotal study as well. And what I mentioned here, and I'll talk through these in a minute. So it talks about the initial ICEM and PQRI. ICEM is the integrated control elements matrix. And I'll get into that in a minute, as well as the product quality risk assessment. And so these are just additional tools that can help you support and facilitate product development and design as well. So holistically, and this is, it's like a ray of sunshine right here, where you're looking at the total integrated control strategy has to be presented in such a way. And we've done this in the regulatory filings to present it in such a way that it's just not about specifications and it's just not about CPPs or KPPs. It's about everything altogether. And so that's where you have to make sure that as you look at this, the holistic approach and how to buy down risk to the patient, and you're presenting this to the regulatory agencies, that has to have foundational elements of your GMP processing, your raw materials controls, your procedure controls, you know, what controls do you have at the input material attributes for patient variability, what have you done to support characterization. And then obviously the routine controls for manufacturing, your process controls, set points, your IPCs, CPPs, KPPs, as well as your specifications for all of your different components, whether it be apheresis, vector, drug substance intermediate, or final drug product. And then obviously there's different elements of your periodic controls as it relates to stability, your process validation, your continuous process validation, CPV, and comparability. And all of that coming together really gives the viewer of your process a true understanding of what are we doing to buy down and mitigate the potential risk to patient safety and product quality and improve product quality so and that's why i wanted to get it obviously the the tools to support risk-based control strategy the cqa assessment is critical there's a separate chapter on that. But I really wanted to get into the integrated control elements matrix and just describe it a little bit, as well as the product quality risk assessment. Obviously, the purpose is to capture the CQA profiles and then looking at the overall risk to the CQA profiles to patient over time. And so a lot of this is based on the AMAP case study, and we've used this when I was at Amgen historically, and certainly elements of this can be applied to your control strategy for CAR-2 therapies. So your integrated control elements matrix really looks at your CQA profiles based on your process and control elements that you particularly have. So experience within the commercial process, your process knowledge, what do you have and understand that goes into that? And then the other piece is what kind of detection mechanism do you have, right? So are you doing raw material testing, release testing, IPC testing, validation? Is it periodic testing for PV or extended characterization or stability? And how do all of those come together? And so when you start looking at this holistically, this is where you can go into this product quality risk assessment. It looks at the residual risk to patient. And essentially, what you're looking at is you're defining a severity score, so the potential of impact of a CQA on the particular patient, the occurrence based on the capability of your process, what it's been characterized to, your input, your other controls, gives you this preliminary hazard assessment level. And then depending on your detectability, so depending on how, if you choose to establish routine testing or periodic testing or no testing, right, we'll give you a different profile in terms of your detectability. And the overall purpose is to look at your final residual risk. And so obviously your final residual risk, you want to make sure that all of that is essentially a low risk to patients. And obviously, if you look at, for example, in the example in attribute four, you have higher severity, higher occurrence. Therefore, you have a higher preliminary hazard level, but your detectability, meaning you're going to want to make sure that you're detecting and having a robust analytical method that's quantitative, that's capable, and that's able to understand and detect whether or not you have an issue with that CQA limit or not. You're going to want to have that routinely embedded there. And so that's how you combine down that risk. And so this whole PQRA tool can be used to develop and minimize risk to patients over time. You can leverage this as a tool early on in development to look at what's your initial risk to patient based on your current clinical controls or your current process understanding. And then obviously, as your process understanding, your process capability improves or your analytics improves, your occurrence score can change over time, your detectability score can change over time, and then certainly your severity, depending on the outcome of your clinical studies. If you had a prospective risk that was theoretically identified and shown that, hey, there's no history of failure, there's no history of this transduction issue or other thing. Therefore, you can, you know, buy down that risk over time through characterization. What this does is it allows you to go through and really understand what your specification should be. And so this is an example of an initial early phase autologous T cell therapy specification, of an initial early phase autologous T cell therapy specification, obviously looking at color clarity for appearance, identity, purity, you know, using flow cytometry techniques to look at cell viability, T cell purity, product-related impurities, as well as process-related impurities. A lot of what we've seen too is looking at strength based on viable T cells using flow cytometry as a surrogate to potency in early first in human type studies. And then certainly safety, you know, transduction controls VCN-RCL, as well as your standard endotoxin mycoinsynergy. And so obviously a lot of these are embedded based on ICH or agency expectations, right? There's some of these that can be evaluated based on real-time data, based on what you're seeing in the clinical space, and based on your experience holistically to where you can adjust and drive those improvements. So obviously, throughout the entire pathway from early to commercial control strategy, you're looking at the characterization studies, process and product, your core of analysis, and that really drives you to understand what your final commercial control strategy is going to be. And so this is an example where, you know, I think the key here is meaningful specifications established per clinical core of analysis. And I would add to that an appropriate statistical analysis, And I would add to that an appropriate statistical analysis, as well as process-related impurities, right, for process characterization and purity risk assessments, and then making sure that you have a meaningful clinical corollary analysis for potency transduction control. So it really is a tool that enables you to further clarify and adjust and shift and establish meaningful specifications. And so this is kind of the final slide. I kind of look at it, you know, we as an industry have to adapt and leverage the novel approaches to developing a phase-appropriate control strategy, looking at the industry tools, making sure that we utilize those throughout development, and then making sure that when we look at the integrated control strategy, it contains all aspects of process development, GMP development, raw material controls, procedure controls, routine versus periodic release testing elements, comparability, PPQ. All of those have to be embedded to support an integrated control strategy approach that's going to be meaningful to the agencies that review this so that there's a clear understanding that we are doing everything that we possibly can to protect patient safety and improve the likelihood of product quality to support a commercial process. And so, you know, in my mind, it's the most important thing that we can do to support our patients, making sure that they can get the cells that they need, get the treatment that they need in a very time and cost effective way. So that's, I think I've got about 12 minutes left, but thank you for listening. Thank you for coming. I appreciate that. And I think that's it. So thank you. It's my email, just in case if anybody needs it. So with that, I will stop sharing and I think we'll go into a Q&A. Yes, thank you so much, Nolan. So we have, yeah, we have 12 minutes for Q&A right now. We've started receiving some questions from the audience, so I'll get to it right away. So how do you best design or implement control strategy taking into account the patient variability and commercial spec setting philosophy from the agencies? So we can stay in control while also maximizing production success rate and minimizing unnecessary out of specification. So that's a great question. Right. And I think if you look at this, obviously, making sure that you've got enough that you don't cut corners on characterization. Right. You understand and develop a robust process. You've got to have a robust understanding of your analytics and making sure that you have enough replicates to support meaningful clinical development. So obviously it's going to be much harder if you have a clinical study with 20, you know, 20 people versus 200 or 600. So that's going to be critical for you, obviously, and making sure that you can establish that clinical correlation. And so I that that's going to be critical for you obviously and making sure that you can establish that clinical correlation and so i think that's critical um the one thing that i would take a look at and this is kind of the the uh the challenge right because as we were interacting with the agencies with the fda and others um on what would be the meaningful specification, right? From an industry perspective, in many instances, we had OOS type material that says, okay, we've got one or two patients that had OOS material and it turned out that it was a clinical benefit for them. I think the agency's perspective is coming and saying, well, we want to make sure there's process consistency. So where do you set that line? Right. And in some cases, there's a negotiation, obviously. And so you may have to set up, okay, based on an evaluation of process consistency, we may have to tighten it and establish that as a commercial control strategy. and establish that as a commercial control strategy. But I think the critical piece here too, is to make sure that there's an embedded understanding and alignment to say, okay, if and when we get a certain number of OOS type results. So inevitably you're gonna have some level of patients that will be OOS during routine release. You can do an exception release and they can still get the therapy and you can still monitor from a clinical perspective the patient outcome. But having an understanding of how many of those out-of-spec patient examples is good enough. And I think that's, I think as an industry, I think that's the hardest part for us to understand. How many is good enough, right? What's the statistically significant number of patients to where you could, you know, absolutely identify, hey, we believe that we can go through and do this and widen this specification. So it's going to come with experience and it's going to come with the number of OSs. So for example, if you have a robust process and you have 600 patients and they're all really tight and you have one patient that's an OS, it's going to be much more difficult to justify that extended specification range compared to if you had, you know, 600 patients and you had 50 of those that were OS and it was a wide variability process, you know, or just based on the patient or population or the disease state. So hopefully that helps clarify some things. It may, it may be come down to numbers. It may come down to the absolute numbers of healthy donors to support characterization as well. Great. Thank you. And maybe as a follow-up to that question, I wonder if the audience asked what your experiences are, your success leveraging the correlative analysis, similar to the examples that you shared in the presentation. And I would say my experience has been, we always started off with the most aggressive approach, right? So obviously within the industry, you're trying to balance, you know, progressive approach, right? So obviously within the industry, you're trying to balance, you know, commercialization as well as patient access, knowing what you know. And essentially, you know, some of our standpoint was we knew that this OS and, you know, cell viability was down at 40 or 50%, for example, and our spec was at 60 or 70. And we knew that it made benefit, it had a benefit for that particular patient, right? And so we were trying to push for that as much as we can. We didn't always win, but it's a negotiation and it's a discussion point. And what it allows you to do is go through and say, okay, you know, based on different statistical analysis approach. So in the biopharm space, you may be looking at a stats analysis of a 95% confidence, 95% coverage versus 95% confidence and 99.9% coverage, right? So depending on your stats, you can adjust how aggressive you are with your specs. And essentially what we had to do is we've adjusted and shifted those. And obviously the FDA wanted us to come back a little bit. But we didn't come back to the point where you would see in a typical biologic. So there was some level of understanding that we did have a clear understanding. We knew and understood what the impact was. And then certainly for an autologous serotherapy, what does that mean to patients, right? So if you have one patient that's OOS and they can't go and get enrolled in a single patient clinical study or it's delayed, what does that do to that patient? It either means that they don't get treated or it means that their treatment is delayed. And from my perspective, when you start looking at where some of these cell therapies are at, whether it be a third or fourth or fifth line therapy, these patients are at the end of the rope and they don't have a whole lot of time. And so I think that's the big thing that you're trying to balance and have that negotiation with the agency. Great. Yeah. Thank you, Nolan. How does the organizational implementation of the integrated control strategy look like? How would an organization need to operate to maximize this approach? How would you? I think it all comes together to make sure that the, I kind of look at it with the experience that I've had in the past, and it was a matter of make sure that the, I kind of look at it with the experience that I've had in the past. And it was a matter of making sure that there was a strategic partnership and healthy understanding of risk, of GMP quality systems, of development, as well as manufacturability from like an MSAP perspective, right? And making sure that all of those are embedded on the same line, ability from like an msap perspective right and making sure that all of those are embedded on the same line right and there were there were time and instances where historically you know based on a biopharma understanding your control strategy was built solely on your characterization and solely based on your cpp kpps and your ipcs and that was that and i think making sure that you have a holistic understanding of what that looks like, right? And all these additional control elements that come in from a quality GMP perspective, manufacturing perspective, the technical perspective, and the clinical perspective. I think all of those have to come together to build that framework. And so if you look at what we did, this was at Genotherapeutics, there was a lot of discussion, well, what does this really mean, right? And how do we look at this what, how do we present this in the findings? We actually built an integrated control strategy section in S2-6 that really just kind of described it all. And so therefore you could, you could combine the elements of your technical considerations, your characterization, in addition with your risk-based approaches, your GMP approaches. And essentially you say, we're looking at everything holistically, and this is how we're buying down that risk to the patients. And so I think, you know, how do you implement it at the company level? I'd say making sure that everybody has a clear understanding of what that holistic strategy looks like, getting all of them embedded and all of them aligned to truly understand, hey, this is only here to help, and we have to all do this together. Great. Thank you. Let's see. Can you speak to the timing of small-scale model qualification and process characterization? Is it required to qualify the small-scale model prior to starting process characterization? Is it required to qualify the small scale model prior to starting process characterization? It's a great question. And I'd say, you know, my personal philosophy is having a small scale model that's qualified is only gonna help, right? And I think there's unique instances where if you start looking at car teeth therapies, in many cases, the commercial scale is not much bigger than what you're doing on the bench scale, to be quite honest. There are instances where it's not fully qualified for all particular attributes, but it allows you from a comparability and a characterization to truly understand how do you manage this? What's practically different? And do a lot of those studies as you look at, you know, just due to the patient variability, you've got to offset that with number of replicates, right? Which may mean you have to do more. So having a small scale model early on in your process, it may not be necessarily needed for first in human, but certainly as you go through commercialization and having that process will only help. So hopefully that helps. Hopefully that answers the question. Great. Yeah. Thank you. I think we have time for maybe two more questions. So part of a DOE, what type of raw material inputs do you see being altered to make sure you have a robust process? So I would look at a number of lots, right? Obviously, if would look at number of lots, right? Obviously, if you look at some of the raw materials that we use in the CAR T therapy space, some are human derived, some are animal derived. Making sure that you have specific lot to lot variability embedded. The last thing you want to do is have a process that you've established. You have one raw material that works. It works great. Everything's fine. And then suddenly later on in your 10th or 50th or 100th batch, you suddenly run out of that particular raw material in that batch. Now you have a different, you know, whether it be impurity profile, whether it be metal impurities or any other biological type of impurity, metal impurities or any other biological type of impurity, you've got to make sure that you're building in a robust number of unique lots for that particular process. And I would also say, if you start looking at, you know, raw materials from an apheresis perspective, making sure that how you're handling, whether it be patient material or certainly from a characterization perspective, healthy donor material, how you're handling that. If you're going to say, we're going to make sure that we have a wide variety and make it very flexible, you've got to make sure and test very thoroughly that how you handle, store, and ship that material will not impact adversely your product. And so I think those are different aspects that could be leveraged to support just from a raw material perspective. So hopefully that helps. Great. Thank you. Okay. I'll wrap up with one more question. So how do you apply, how do you assign values to the risk assessment or FMEA in early stages when you process knowledge? I think that, I mean, there's a whole decision trees. There's different things out there and we can certainly provide some examples, but essentially with higher uncertainty, your risk goes higher. And so you're, in many cases early on, you may not know and understand what your occurrence is, you know, what's your likelihood of your process to, you know, contain a certain CQA profile or certain impurity. So you may have to go through and simply err on the side of caution and say, okay, based on this, it's going to be a bigger risk. What that does is it drives your RPN type number or your pre-QRI assessment to say you have a higher risk there. And what you're going to do is identify and support that during commercialization and characterization. We're going to go figure that out. Is this truly a big risk or do we have, you know, knowledge and understanding that it's really not a big deal? Great. Well, so we are at time. It is noon right now. So with that, I'd like to wrap up the webinar and I wanna thank you again, Nolan, for lending your time and your expertise for this topic. We really appreciate it and I'm sure the audience learned a lot from it. And as a follow-up, if we didn't get to your questions, we'll try to get to the questions and post them online. And with that, again, thank you again, Nolan. And have a great rest of your day, everyone. Thank you.
A-Cell: Control Strategy
This webinar will discuss the development of a robust integrated control strategy for a CAR-T cell therapy product in the setting of current regulatory framework and guidelines. The presenter will cover various concepts within risk-based approach to control strategy generation, including various process characterization tools and correlative analysis between product CQAs to clinical outcome.

Speaker:
Nolan Polson, Ph.D., Vice President, Strategic Product Quality, GSK