Full Transcript for 18. Putting solid tumours under the T-knife: how a next-gen TCR aims to crack them

that are highly expressed on the surface of a cancer cell. 00:02:12.800 --> 00:02:21.360 And there are some reasonable options in hematological cancers, especially B cell cancers, where the CD19 antigen has proven particularly attractive. 00:02:21.599 --> 00:02:26.159 But there are few, if any, antigens that are uniquely expressed by solid tumors. 00:02:26.319 --> 00:02:44.879 And we've had seven CART TA approvals over the past eight years, all of them in hematological cancers, but we've only had one TCR cell therapy approved, and that that was only last year, Adapt Immunes Tessellera that gained FDA approval last year for treating synovial sarcoma. 00:02:45.039 --> 00:02:48.560 Why have there been so few TCR cell therapy approvals, do you think? 00:02:49.280 --> 00:02:50.479 Yeah, really good question. 00:02:50.639 --> 00:02:54.879 Well, I think let's start with conceptually CAR T and TCRT are not that different. 00:02:54.960 --> 00:03:06.319 Yeah, I think in both approaches, you really modify a T cell, typically an autologous T cell from a patient with a novel receptor that is specific for tumor antigen, and then redirects these T cells to kill tumors. 00:03:06.560 --> 00:03:12.479 And the major difference really between TCR and CART lies in the type of antigen and that these receptors can target. 00:03:12.719 --> 00:03:28.800 TCRs are really unique in the sense that they can detect very small fragments of intracellular antigens that are then presented on the cell surface and so-called MHC molecules, while, as you said, cars are really limited to bind only cell surface molecules like CD19 or like BCMA. 00:03:29.120 --> 00:03:34.639 And now, you know, white blood cells, the origin of liquid tumors, have very distinct surface markers. 00:03:34.719 --> 00:03:40.000 And car developers have, you know, leverage set for targeting heme tumors, which are derived from these white blood cells. 00:03:40.159 --> 00:03:49.039 And what we've seen, obviously, as a side effect is that the healthy blood cells like B cells that are also CD9U positive get eliminated as a side effect of the CAR T therapies. 00:03:49.360 --> 00:03:54.159 But that essentially is not a problem because you know, humans can live without B cells with some restrictions. 00:03:54.400 --> 00:04:03.120 I think while CARS have been really perfectly suited to treat heme tumors, TCR developers from the beginning on have really uh retreated more to focus on solid tumors. 00:04:03.199 --> 00:04:14.240 Yeah, where solid tumors, the antigen landscape is very different because it's very hard to find solid tumor targets that are distinct from healthy tissue in contrast to B cells. 00:04:14.400 --> 00:04:19.439 And most of these antigens are, you know, shared with vital organs like heart or like the brain. 00:04:19.680 --> 00:04:28.160 So, you know, using TCR really allows you to target a much broader range of intracellular antigens and it broadens the space for actually safe solid tumor targets. 00:04:28.639 --> 00:04:36.160 And what I would add is that unfortunately with solid tumors, very different to heme tumors, we have a lot of additional barriers to the immune system. 00:04:36.720 --> 00:04:52.399 It took a lot of time for the field to really learn which solid tumor indications respond to T cell therapy, which targets are safe enough, and also how to actually further engineer T cells beyond just the car or TCR to protect the T cells from the immunosuppressive tumor mic environment. 00:04:52.639 --> 00:04:58.800 So, what I would say is we not just had one TCR approval versus seven CAR T approvals. 00:04:59.040 --> 00:05:05.680 I would say what we've seen is then there was an earlier success in heme, and that was mostly driven by cars, being best suited for that. 00:05:05.839 --> 00:05:10.639 And what we now start to see is the first success in solid tumors, which is more challenging. 00:05:10.879 --> 00:05:15.199 And I would say remarkably, that first approved product in solid tumors is actually a TCRT. 00:05:16.079 --> 00:05:18.000 Okay, yeah, that's a fair way of putting it. 00:05:18.160 --> 00:05:23.199 And yourselves now, I mean, your lead investigational therapy is about to start a first and human trial. 00:05:23.360 --> 00:05:32.639 That's TK6302, and that targets the prey mantigen, which is expressed, I understand, in a range of solid tumor indications. 00:05:32.800 --> 00:05:41.519 And maybe we can talk about that trial and the kind of patients that's going to enroll, the number of patients that will be involved, and the sort of main outcomes that you plan to measure. 00:05:42.000 --> 00:05:42.639 Yeah, sure. 00:05:42.800 --> 00:05:51.759 Our ATVA study is a first in human open label phase one clinical trial, and it will focus on patients with late-stage metastatic solid tumors. 00:05:51.839 --> 00:05:59.839 And namely these are ovarian cancer, uterine cancer, melanoma, squamous non-small cell lung cancer, and triple negative breast cancer. 00:06:00.160 --> 00:06:04.800 And all of these tumor types have in common that they really have a high unmet medical need. 00:06:05.040 --> 00:06:09.600 We've seen a lot of progress in the field, but still they are remaining patients with a high unmet medical need. 00:06:09.759 --> 00:06:19.600 And what we know is that additionally, these tumors have a very high prevalence of frame expression and you know are just a logical choice for investigating a prime-directed TCRT cell therapy. 00:06:19.839 --> 00:06:24.720 And this is with patients who have basically exhausted all available treatment options at that stage. 00:06:24.959 --> 00:06:33.519 For the ATLA study, we plan to enroll up to 24 patients in a dose escalation part of the study before we would then move on to the dose expansion phase. 00:06:33.759 --> 00:06:48.639 And what I think is quite normal is that for the dose escalation, we're using a very um monible approach for the dose escalation, which is an adaptive approach with a

Boeing design, which is much more precise than a classical 3 plus 3 design or identifying the optimal dose. 00:06:48.720 --> 00:06:49.279 Yeah. 00:06:49.600 --> 00:06:56.319 And from the outcomes, so the main outcome of the study will obviously be safety and also defining the optimal biological dose. 00:06:56.399 --> 00:07:02.560 Uh, but we, of course, are also very excited to look at the anti-tumor response and the set of biomarkers that we will measure. 00:07:02.879 --> 00:07:03.199 Yeah. 00:07:03.360 --> 00:07:09.680 I mean, from a safety point of view, obviously, in a first-end human trial, that's always going to be the kind of primary endpoint and concern. 00:07:10.160 --> 00:07:23.759 And both CART therapies and TCR cell therapies have, especially in the early days in the clinic, there were toxicities, some of them expected and some of them unexpected, that were arising. 00:07:24.000 --> 00:07:31.759 And from your point of view now, given where the field is at, what do you think are the potential toxicities that could arise during this trial and how do you control them? 00:07:33.120 --> 00:07:35.600 Yeah, so let's start with the clinical sites, right? 00:07:35.680 --> 00:07:42.079 So the sites that we're working with are really centers of excellence and very experienced and treating patients with cell therapy. 00:07:42.240 --> 00:07:45.040 They're really experienced in managing toxicities. 00:07:45.199 --> 00:07:51.279 And the classical side effects that we're seeing with uh with cell therapy are cytokin release syndrome, as an example, right? 00:07:51.360 --> 00:07:55.680 And I think the field has really moved on and understanding how we're managing cytokine release syndrome. 00:07:55.759 --> 00:07:59.199 And the physicians we're working with in particular have a lot of experience with that. 00:07:59.360 --> 00:08:01.519 And I mean, there's a classical path for that, right? 00:08:01.600 --> 00:08:08.639 You hospitalize the patients for the first 14 days, uh, you can monitor them closely and then obviously do an intervention if need be. 00:08:08.800 --> 00:08:18.160 But what we're also learning is that in general, TCRT cell therapies for solid tumors, in particular also for PRAM as a target, seem to be very well tolerated. 00:08:18.399 --> 00:08:21.519 So we're not particularly concerned when it comes to that. 00:08:21.759 --> 00:08:25.199 And I would also say what people sometimes forget, right? 00:08:25.279 --> 00:08:29.839 Uh what's so exciting about cellular therapies is that we're talking about this one and done treatment, right? 00:08:30.000 --> 00:08:32.720 Patients get one hospitalization, one treatment. 00:08:32.879 --> 00:08:36.480 They're having a transient period of potential side effects, and then they're done. 00:08:36.559 --> 00:08:41.200 And they can leave the hospital, they can go back to lead a normal life, they can potentially go back to work. 00:08:41.440 --> 00:08:49.360 Whereas in a lot of other treatment regimes, you

get like a continuous treatment, but you need to return to your position, you know, every three weeks for it to stay on course. 00:08:49.519 --> 00:08:52.480 And I think that that's really exciting about cell therapies still. 00:08:52.879 --> 00:08:53.039 Yeah. 00:08:53.200 --> 00:08:56.480 And PrayM, a term we've used quite a lot already. 00:08:56.720 --> 00:09:00.480 I mean, it's an acronym, and the full name is quite a mouthful. 00:09:00.720 --> 00:09:04.000 Preferentially expressed antigen and melanoma. 00:09:04.320 --> 00:09:08.399 You really do have to ask yourself who gets to actually uh coin these names? 00:09:08.639 --> 00:09:13.360 It's so often the case in biology that a protein is named after its first identified function. 00:09:13.519 --> 00:09:23.200 But obviously, we now know, and given the list of cancer indications that will be open on your trial, um, or included at least, it's not solely connected with melanoma. 00:09:23.360 --> 00:09:30.639 But from your point of view, though, why target prame and what makes it particularly attractive to a TCRS cell therapy developer? 00:09:31.360 --> 00:09:32.879 Yeah, happy to discuss this. 00:09:33.039 --> 00:09:36.399 So, Prame is what is the so-called cancer testis antigen. 00:09:36.480 --> 00:09:44.399 It's expressed in a limited number of healthy tissues, such as testis, as the name says, but it's frequently expressed across a large range of cancer types. 00:09:44.559 --> 00:09:48.480 And as you said, initially it was identified as being expressed by melanoma cells. 00:09:48.639 --> 00:09:54.639 And it also has been associated with poor prognosis across a couple of cancer types. 00:09:54.879 --> 00:09:58.320 From a function perspective, we don't know all about crame. 00:09:58.399 --> 00:10:05.600 It's known to be a pro-tumorogenic factor, but really what the molecular function is and in tumors is not yet really fully known. 00:10:05.679 --> 00:10:10.000 So we rather use it as a marker that is, you know, frequently present in tumor cells. 00:10:10.320 --> 00:10:17.200 But we, you know, at TNF, we've really done a lot of studies to understand crame expression uh very well because it's a late program. 00:10:17.279 --> 00:10:20.960 We really wanted to get a deep understanding of that protein and tumors. 00:10:21.200 --> 00:10:27.519 And everything that we've seen is really encouraging to position it as a really great target for TCRT therapy. 00:10:27.759 --> 00:10:37.919 We see it expressed across a very large range of tumors, and particularly there's a number of indications, most of them which are included in our etlas study now, where the prevalence of PRAME is also very high. 00:10:38.320 --> 00:10:42.480 To give you an example, in a variant cancer, almost all tumors are actually positive for PRAM. 00:10:42.559 --> 00:10:43.679 So it's really frequent. 00:10:43.840 --> 00:10:54.720 And what we are seeing, interestingly, by on a biology perspective, is that this prevalence also correlates

with very high expression level and with homogeneity of expression in our focus indications. 00:10:54.799 --> 00:10:55.039 Yeah. 00:10:55.200 --> 00:11:00.000 And so these homogeneity and expression level have both been associated with better therapeutic outcomes. 00:11:00.080 --> 00:11:02.799 So that's really important for a target antigen. 00:11:03.120 --> 00:11:10.159 And another interesting factor for design is that so Prame is reactivated in tumors by DNA methylation events. 00:11:10.240 --> 00:11:16.639 So in some time during the tumor development, the gene region where prame is encoded is getting demethylated. 00:11:16.720 --> 00:11:19.519 And then it's that's when praying extraction basically starts. 00:11:19.679 --> 00:11:23.759 And the interesting piece is that this seems to be a mostly irreversible process. 00:11:23.919 --> 00:11:28.320 So it also means that once PRAME is turned on in a tumor, it doesn't go away. 00:11:28.399 --> 00:11:28.559 Yeah. 00:11:28.720 --> 00:11:42.799 And so what we can see in our data is that it's very stable across disease progression when we follow patients during different lines of treatment or, you know, diagnosis to later stages, um, they remain positive and frame frequency remains very stable. 00:11:43.039 --> 00:11:48.879 And we see the same stability of BRAM expression also between primary tumors and metastasis. 00:11:49.360 --> 00:12:06.879 So, what it means for us as a developer is that, you know, once we have a brain positive biopsy from a patient, there's a high likelihood that the T cells that enter that patient therapeutically will find brain positive cells across the tumor and in the different uh in the different metastasis and you know, at the time point when the patient gets treated. 00:12:07.039 --> 00:12:11.120 So, yeah, I think from a lot of different angles, it's a very interesting target for tumors. 00:12:11.279 --> 00:12:12.879 And at the same time, it's a very safe target. 00:12:12.960 --> 00:12:20.720 So all of our studies say it's very lowly expressed only in a very limited set of healthy tissues where we are not concerned around on target toxes. 00:12:21.360 --> 00:12:23.440 All of which is absolutely fascinating, actually. 00:12:23.600 --> 00:12:31.039 I hadn't actually realized that level of detail was understood about its actual role itself, potentially in tumor genesis as well. 00:12:31.120 --> 00:12:32.320 It's really, really interesting. 00:12:32.480 --> 00:12:35.360 And you're obviously not alone either in in targeting Prame. 00:12:35.679 --> 00:12:38.240 Several other therapy developers are also in the space. 00:12:38.320 --> 00:12:48.240 I mean, Imatics is already in a phase three study of melanoma and has shown additional clinical data in earlier indications with a second generation product. 00:12:48.480 --> 00:12:52.080 So where does that leave you from a kind of a competitive point of view? 00:12:52.559 --> 00:12:58.080 Yeah, I would say that we actually have the good fortune that Prame as a target is already heavily derised in patients. 00:12:58.159 --> 00:13:04.879 Yeah, in particular TCRT as a modality, in contrast to much of the earlier targets, which actually led to on target toxicity in patients. 00:13:05.039 --> 00:13:10.960 Yeah, the data we've seen from the field so far really show that it can be safely targeted by praying TCRT. 00:13:11.120 --> 00:13:16.559 And importantly, praying TCRT has shown really astonishing results and late-stage constipations already. 00:13:16.639 --> 00:13:20.080 And as you mentioned, particularly in melanoma, but also in synobal sarcoma. 00:13:20.320 --> 00:13:37.840 And um, just to give you one example about these data, so uh patients with uveal melanoma, which is a form of melanoma of the eye, uh, which is a much less common subtype with melanoma and typically considered a cold tumor, where a lot of standard melanoma treatments do not have the desired effect. 00:13:38.000 --> 00:13:50.559 In this indication, um, there was an initial phase one for an TCRT, and that showed a 67% overall response rate in this patient population and a median progression-free survival of 8.5 months. 00:13:50.720 --> 00:13:54.480 And these data actually cause a lot of excitement around this year's asthmo conference. 00:13:54.639 --> 00:14:00.080 And if they can be confirmed actually in a larger cohort, uh, I think this is a real game changer for these patients. 00:14:00.320 --> 00:14:18.480 But what we're also seeing is that the field has really faced the challenge that how we can translate these great data and initial indications to more broader indications that are maybe more harder to treat opposed to melanoma, so that are not niche indications like synovial sarcoma, which don't represent a commercially enough attractive market. 00:14:18.720 --> 00:14:21.039 So we're looking at lung or ovarian cancer. 00:14:21.440 --> 00:14:22.639 How about treating these? 00:14:22.720 --> 00:14:23.120 Yeah. 00:14:23.360 --> 00:14:26.480 And we believe that the field here is probably really at a breakthrough, right? 00:14:26.559 --> 00:14:35.519 We are now seeing these next generation TCRTs entering the clinic where the T cells are getting engineered with additional molecules beyond just the T cell receptor. 00:14:35.679 --> 00:14:37.679 And these will further boost the immune response. 00:14:37.759 --> 00:14:45.679 And what we've now seen is really, you know, a first phase one data coming out from such a second generation bank TCRT in broader solitude human medications. 00:14:45.840 --> 00:14:50.000 And these data just presented at ESMO.io actually look extremely promising. 00:14:50.080 --> 00:14:56.639 And we'll be, you know, really exciting to see how uh much more uh in that field, the next generation uh TCRTs. 00:14:57.360 --> 00:15:02.799 Yeah, and I guess that's where TK6302, the T9 therapeutics therapy, comes in. 00:15:03.039 --> 00:15:06.720 Now you are obviously a couple of years behind some of

your competitors. 00:15:06.879 --> 00:15:09.039 What gives you confidence that you can be competitive? 00:15:09.120 --> 00:15:11.679 What are its key differentiating features? 00:15:12.159 --> 00:15:15.039 Yeah, let's let's talk a little about the TK6 for your tool, right? 00:15:15.120 --> 00:15:29.600 So um at T9, what we're doing is we're calling that supercharging T cells, which essentially means engineering new functions into T cells that were particularly designed through synthetic biology to overcome specific challenges that T cells face in the tumor micro environment. 00:15:29.759 --> 00:15:32.960 And particularly in the tumor mic environment of how to treat tumors. 00:15:33.200 --> 00:15:43.200 Our current lead product, TK6302, what we're doing here is we're combining one high affinity, brain TCR, that comes from our own proprietary Mikey platform. 00:15:43.360 --> 00:15:48.960 And we're combining it with a co-stimulatory CD8 co-receptor and a so-called switch receptor. 00:15:49.200 --> 00:15:51.440 And so these components are really interesting. 00:15:51.519 --> 00:16:01.360 So the synthetic co-stimulatory CD8 co-receptor, a really hard to pronounce word, it allows us to integrate also CD4 T helper cells in the anti frame response. 00:16:01.600 --> 00:16:06.000 And these C D4 cells in turn are really thought to really orchestrate a more global immune response, right? 00:16:06.080 --> 00:16:10.720 So they are really important to attract other immune cells and really get the immune response against the tumor rolling. 00:16:10.960 --> 00:16:16.960 And at the same time, we've engineered in here co-stimulatory domains, which are activating the T cells more optimally in the tumor. 00:16:17.039 --> 00:16:20.480 So that's a very novel function that has not yet been tested clinically. 00:16:21.039 --> 00:16:26.080 And that second component, which is really a major differentiator, also is what we're calling a switch receptor. 00:16:26.399 --> 00:16:31.440 This is designed to block inhibitory signal that's coming from the solid tumor micro environment. 00:16:31.759 --> 00:16:35.279 Namely, that inhibitory signal that we're blocking here is fast-ligand. 00:16:35.679 --> 00:16:39.840 We're flipping that inhibitory signal into an activation signal for the T subset. 00:16:40.000 --> 00:16:44.000 Yeah, so leveraging it in a positive way instead of letting it act in a negative way. 00:16:44.240 --> 00:16:46.799 And so, yeah, this product is really extremely novel. 00:16:46.960 --> 00:16:50.399 It's very differentiated, and that's how we hope to be a competitive. 00:16:51.279 --> 00:16:51.919 Yeah, okay. 00:16:52.080 --> 00:16:58.480 And obviously the data will be watched with interest, and uh obviously that will take time for the data to mature. 00:16:58.960 --> 00:17:03.279 But so far, how is it performed in a preclinical kind of settings? 00:17:03.679 --> 00:17:09.440 Yeah, so preclinically we definitely see a highly differentiated profile of these uh

supercharged prime T cells. 00:17:09.759 --> 00:17:17.759 We see, for example, that they're very activated, that they maintain a more sustained NHUMA functions, that the T cells do not exhaust over time. 00:17:18.000 --> 00:17:23.359 And we've even developed a very sophisticated preclinical model, which is a 3D tumor spheroid model. 00:17:23.680 --> 00:17:29.440 And that model really represents more naturally the actual challenges in the tumor microenvironment for T cells. 00:17:29.599 --> 00:17:38.799 And in this particular model, and these are data that we just recently also showed at SITSI, we see that uh TK6302 really outperforms other brain targeting approaches. 00:17:38.880 --> 00:17:41.279 Yeah, when we benchmark that side by side. 00:17:41.519 --> 00:17:55.440 So, yeah, I we really hope, you know, that this preclinical data, which are very clear, will also not translate into deep and durable responses in these brain medications, in particular outside of melanoma, which have so far been just harder to treat. 00:17:56.160 --> 00:17:57.200 Yeah, fair enough. 00:17:57.359 --> 00:18:03.920 And then manufacturing, obviously, is so important with all therapeutics, but particularly this complex category of therapeutics. 00:18:04.079 --> 00:18:10.240 And it's been stated time and time again that cell therapies of any kind that the process is the product. 00:18:10.400 --> 00:18:19.440 A clinical batch will need to meet a very complex set of quality requirements, which will hopefully correlate with its expected performance before it can be released to a patient. 00:18:19.599 --> 00:18:23.680 And what are the key steps in your production process for TK6302? 00:18:23.920 --> 00:18:27.200 And where do you stand in the process development kind of piece? 00:18:28.400 --> 00:18:39.279 Yeah, for manufacturing, we're using a very novel approach, which is non-viral gene editing, which gives us actually a lot of advantages over traditional viral vector approaches for XVO cell therapy. 00:18:39.440 --> 00:18:48.960 And the advantages are one, that we can actually use a much larger DNA cargo, which we need because we need to, in addition to the TCR, integrate that co-receptor and the switch receptor that we talked about. 00:18:49.200 --> 00:18:56.000 The second advantage really is that while we're knocking in the new genes, we also simultaneously knock out the endogenous TCR. 00:18:56.079 --> 00:18:59.680 So that's a thing you can only do with gene editing and not with viral vectors. 00:18:59.839 --> 00:19:05.839 Knocking out of the endogenous TCR really allows you to boost much more the expression of the TCR, increases the safety. 00:19:06.000 --> 00:19:10.079 So there's a lot of benefits for TCRT in particular to be able to do that. 00:19:10.960 --> 00:19:13.759 And by the way, too, the what what is the non-viral approach? 00:19:14.000 --> 00:19:16.480 How are you actually transfecting the cells with the cargo? 00:19:16.799 --> 00:19:16.960 Yeah. 00:19:17.039

--> 00:19:22.160 So we are using a CRISPR-based approach and we're we're getting it into the cells through an elect operation. 00:19:22.640 --> 00:19:23.599 Ah, yeah, yeah, yeah. 00:19:23.839 --> 00:19:30.000 And then in terms of getting to dose levels, et cetera, et cetera, where are you at with scaling to the level that you need to scale? 00:19:30.400 --> 00:19:31.599 Yeah, really, really important. 00:19:31.759 --> 00:19:33.200 So we've locked the process. 00:19:33.279 --> 00:19:35.200 So the process development has actually finalized. 00:19:35.279 --> 00:19:44.400 Uh, we've locked it, and we could really demonstrate that we have very consistent batches and being able to generate the highest uh target cell dose actually that we're planning to use in the Atlas study. 00:19:44.720 --> 00:19:47.359 We've also demonstrated, and I think that's a that's a key point. 00:19:47.519 --> 00:19:56.160 We've demonstrated that this process also works for patient material because you usually do the development only on healthy donor material, which you know the fitness TT cells, just very different. 00:19:56.400 --> 00:20:05.440 Yeah, I totally get that point because obviously the patients with Typically be immunos compromised or in some way have they've been through multiple rounds of therapy. 00:20:05.599 --> 00:20:07.599 A healthy donor isn't the same thing at all. 00:20:07.759 --> 00:20:08.799 It's a very interesting point. 00:20:08.960 --> 00:20:09.119 Okay. 00:20:09.279 --> 00:20:13.440 And I presume you're working with the CDMO then to actually do the actual process work. 00:20:13.519 --> 00:20:13.839 Is that right? 00:20:13.920 --> 00:20:15.039 In the in the live setting. 00:20:15.440 --> 00:20:15.759 Correct. 00:20:15.839 --> 00:20:26.799 Yes, we've now successfully transferred the process to a CDMO that that will do the clinical batch manufacturing and they can reproduce the data that we've generated in-house with regards to you know batch manufacturing success. 00:20:26.960 --> 00:20:32.079 And uh yeah, we're really happy to now uh move that forward into the into the clinic. 00:20:32.400 --> 00:20:33.920 So it's really all systems go. 00:20:34.160 --> 00:20:37.599 And then on the commercial side, what's the strategy for 6302? 00:20:37.759 --> 00:20:41.119 Do you want to get a partner for this or do you want to maybe commercialize it yourselves? 00:20:41.200 --> 00:20:45.119 Or if you're seeking a deal, what would be the ideal timing for a deal? 00:20:46.400 --> 00:20:50.480 Yeah, it's early, but it's not, you know, it's not uh early enough to actually ask the question, right? 00:20:50.559 --> 00:21:01.039 So yeah, I think in the near term we're really focused on demonstrating clinical proof of concept for TK602, which uh which means that you know it's there needs to be a real added benefit for patients, right? 00:21:01.119 --> 00:21:03.359 Uh and a really differentiated profile for it. 00:21:03.519 --> 00:21:08.799 And yeah, demonstrating that supercharging T cells can really crack the code for how to treat solid humors. 00:21:09.039 --> 00:21:10.720 That's really what we're uh aiming to do. 00:21:10.880 --> 00:21:14.240 Um that being said, I think we, yes, for sure, right? 00:21:14.319 --> 00:21:15.599 We've looked at long-term plans. 00:21:15.680 --> 00:21:22.880 We we have set up as you can see a development plan that brings down costs over time and really think about how the commercial journey could look like, right? 00:21:22.960 --> 00:21:23.759 Um over time. 00:21:23.920 --> 00:21:26.480 So that's definitely already strategies in place for that. 00:21:26.720 --> 00:21:32.799 And with all that work, I would say we feel really good about the business model for not follow this uh cell therapy for targeting brain. 00:21:33.440 --> 00:21:34.880 And yeah, you asked about partner. 00:21:35.200 --> 00:21:41.519 I uh I think the right partner can be a real benefit for the program, both with regards to upscaling, patient access. 00:21:41.680 --> 00:21:45.440 So I think you can just leverage a different set of resources there. 00:21:45.680 --> 00:21:51.839 So for sure, we're open to partnering and we also continuously build relationships with larger companies that are active in the space. 00:21:52.000 --> 00:21:57.359 But I would say right now, first and foremost, uh for us it's about showing what we can make a difference for patients. 00:21:57.599 --> 00:22:00.319 Yeah, and when you do that, everything else follows really. 00:22:00.400 --> 00:22:01.599 Yeah, yeah, I would take that point. 00:22:01.839 --> 00:22:04.480 So I wish you and your colleagues all the best in these endeavors. 00:22:04.559 --> 00:22:06.319 And thanks for joining us this week, Elisa. 00:22:06.880 --> 00:22:08.000 Yeah, thanks for having me. 00:22:08.079 --> 00:22:11.039 And as our team would say, better stay supercharged. 00:22:12.640 --> 00:22:16.480 And thanks everyone for listening to this episode of BioBizBuzz. 00:22:16.720 --> 00:22:20.720 We welcome your feedback as usual, comments or thoughts on this or other episodes. 00:22:20.880 --> 00:22:28.240 So if you want to get in touch, please log on to biobizbuzz.com or comment on LinkedIn, Blue Sky, X, or Facebook. 00:22:28.319 --> 00:22:34.960 And please continue listening on Apple Podcasts, Spotify, YouTube, or wherever else you may find your podcasts. 00:22:35.119 --> 00:22:37.200 In the meantime, goodbye for now.