Inside Moderna’s Biggest mRNA Test Since COVID Transcript, AI Summary & Key Points

00:00 It's the first time there is a cancer vaccine working. The field has been doing that for 20 plus years, more than a thousand clinical trials that have all failed. >> What was different this time? What is it about mRNA technology that enables the immune system to learn in a way that other approaches were unable? >> We all have cancer cells all the time in our body.

00:19 Our immune system is very well trained. Basically notice those cancer cells very early and get rid of them. But if your cancer grows, then the question is how can you retach immune system? We're going to basically take a biopsy of your tumor. We're going to read all the letters of its DNA. And then we're going to do the same things with a healthy cell of your body.

00:36 And we're going to literally compare letter by letter, nucleotide by nucleotide. And then we're going to use an algorithm to identify which one of those mutations are the most relevant. And so when this is injected in your body, it teaches the immune system the signature of your cancer cell that it missed. >> What gets regulated here? Because every dose is different.

00:54 So obviously every dose doesn't get approved. >> Busy feet. >> Hi. Um, welcome to the A16Z podcast. Uh, I'm Horge Kai, a general partner on the A6Z bion health team. I am thrilled today to welcome back Madna CEO Stfan Bonsel. Um, for folks that have been longtime listeners may recall, uh, Stfan joined us on the A6Z podcast back in December of 2020. um when we were talking about all of the work Madna did to uh bring us the the mRNA COVID vaccine and at the time um if you can go back and listen to that episode you'll

01:36 hear um how quickly um Madna was able to react to the existence of the virus to analyze it uh to essentially print a vaccine um that would protect people against an uh against the the the co 19 uh vaccine and hence put us in a much better position uh with respect to the to the co pandemic. We titled that episode the the machine that made the vaccine and the reason why we're talking again today in August 2026 uh is because Madna has uh put out news uh on a really big advancement on what they can do with mRNA technology

02:18 when it comes to cancer. And so I want to hand it over to you uh Stefan uh again a very big welcome. Thank you for coming back. But maybe the place to start is let's uh let's lead with the news. Um you uh Madna and Merc uh announced uh earlier this month in August that uh you had uh uh conducted a phase three clinical trial in melanoma and have gotten uh very encouraging results.

02:46 So why don't I hand it over to you and tell us uh what have you announced? What have you seen in this phase three trial and then I do want to dig in into what Madna is doing in cancer. >> Wonderful. So Roy, thank you so much for having us back. We're very happy to be to be with you. So indeed uh last week now uh we shared the news with our colleagues at Merc that after working 10 years on a individualized uh treatment against cancer using MR&A technology that the phase 3 was positive it's a it's a first of many it's a

03:20 first time that there is an agent in melanoma that is better than kudra alone so it's a big deal for patient of course with melanoma it's the first time there is cancer the vaccine working. Uh the field as you know has been doing that for 20 plus years. I think more than a thousand clinical trials that have all failed. Um and um if you look at the phase two data because what we announced last week is that we met the primary endpoint of the study which was recurrence free survival i.e.

03:55 people having the cancer back or dying >> that we met and then to our own surprise because this was the first interim analysis. This is not the end of a study. It's a first interal of a study and what was a surprise even to us is we met the secondary endpoint which was distant metastasis free survival which is of course takes more time to mature because it means that you have distance metastasis from your primary tumor and we also made that uh endpoint which was great again unexpected from our side but again means it

04:28 is really good. Um we will share the data very soon at a big medical oncology conference which is as you know what the field does. But just to give you a sense maybe to orient kind of directionally we showed at ASCO the big you know oncology conference in the spring of 2026 a few months ago that our phase 2 study which was also randomized against ketra alone.

04:50 So the same type of study showed that around 50% of people had recurrence free survival versus people only getting kudra and this is 5 years after treatment and as you know incology five years is considered by doctor like a cure and so it's a big big deal and if you look at the data from organ that phase two you had around 80% of people that were disease free five years after their treatment and their surgery of melanoma And so we're very excited about what it means for the field.

05:23 Uh we are working very hard already with regulators to file so that the drug can be available to patient as soon as possible and we hope in 2027 and we're going to try to make it as fast as we can. Uh the factory is ready in Massachusetts but we have figured out how to make and scale a product for every human being at the at the time. >> Well, there's I mean there's so much to to unpack here.

05:47 So I um this is an extraordinary uh advance uh for the the treatment of in this case melanoma and hopefully over time cancers more broadly. Uh so let's unpack some of this if we could Stefan. Uh the first one um uh uh talk to us uh for folks that are less familiar with cancer treatments. Uh why is Kuda what is Kruda and why is Kruda alone not sufficient?

06:10 Why was Madna necess or the the the this particular mRNA cancer vaccine necessary in conjunction with Kruda? >> Sure. So if you look at Kudra which is kind of one of the leading imunotherapy that people might have heard of is basically if I oversimplify for nonbiologist it's basically a molecule that basically open the gates for letting the dogs out that are going to go and attack your cancer from your immune system if you want.

06:38 Um the the thing about checkpoints is when they work they're fantastic because those people 5 years after treatment are in the cured in the sense they have no remaining disease. Uh but only 60% of people are disease free after five years if you look at the phase three published data of kudra. So again for those 60% of people it's amazing but it means that there's 40% of people where you go through the treatment you're fighting for cancer and the treatment doesn't really work for you and also what is difficult is those

07:14 treatments are wonderful but they a lot of time come with very serious side effects very different from chemotherapy or radiotherapy the side effect of imunotherapy are mostly immune disease >> so you see people if you look at the label or the clinical studies people that get checkpoints Whether it's Kudra or any other type of checkpoints from other companies, they end up having type 1 diabetes, lupus, chron disease, these type of things.

07:40 Of course, it's better to have those disease than of course being dead from your cancer, which is why those have become standard of care. But think about those 40% of people who don't respond to checkpoint. They get an autoimmune disease most of the time and they don't get the benefit of a medicine. And so what we try to do with Merc and is really a technology that emerged from Mona Labs back in 2015 2016 when we did a partnership with Merc as we're looking for one of the best company in imotherapy to partner and to

08:10 have a complement approach the idea that we have at the time using our infectious disease learning from our infectious disease vaccine. We learn a lot about the immune system and how mRNA interact with the immune system. We thought we could develop a mechanism of action that was totally orthogonal. So very different from imunotherapy because you will be able to start from a sequence of your tumor.

08:36 >> Mhm. >> So that we could design a product to basically teach if I go back to the dog analogy to teach those dogs what to look for very specifically. And that's really the the beauty of about our technology is if you think about K basically unleash the dogs but they go a bit randomly sometime it's your immune system whereas Mona product is able at the molecular level inside your immune system to very specifically teach your T- cell this is what you need to look for and that thing is actually on your cancer cell so

09:08 that those tea cells go and basically attack your cancer cells. >> Okay. Okay. So now that that's where the the personalized cancer fixing um uh technology will make a big impact on the treatment of melanoma. So that we'll come back to the personalized piece because I think that's just fascinating not only from a technological standpoint but just from an operational standpoint.

09:31 Uh so I do want to come back to that but let's focus on the word vaccine. So you know you know typically when you think of the word vaccine uh it is to prevent a disease. Um here in this context uh these patients already have cancer. So what in a sense you are preventing something. You're not preventing the cancer. What you're preventing is the return of the cancer.

09:50 Um which is in and of itself an extraordinary thing to think about from a from a therapeutic intervention perspective. Um so number one is that is that a fair characterization of how >> characterization and the reason the field has used the word vaccine. It was not us using it. who just follow the field as I mentioned there been a thousand plus clinical trial is because it's about teaching your immune system if you think about a COVID or flu shot you teach your immune system before you get the virus infecting your body

10:19 here you teach your immune system not about the virus but about basically the cancer signal that your immune system missed because what we know today in the field is that we all have cancer cells all the time in our body >> whether it's outside factors or Just as you have cell replication and you have mistakes that happen that create mutation of DNA that are cancer cells, our immune system is very well trained to basically notice those cancer cells very early and get rid of them.

10:49 But if your cancer grows then the question is how can you retune system? So I think that's why the field use the word vaccine for that approach as well. Even as you said it's a therapeutic treatment approach post cancer it's about the teaching of the immune system. Okay. And so as you point out, the field has tried this many times before. A thousand on the order of a thousand clinical trials um have have tested this theory.

11:16 They've all failed. Uh you and Madna and Merc have succeeded here. Um what was different this time? What is it about mRNA technology that enables the immune system to learn in a way that other approaches uh were unable uh uh to be successful? Yeah, I think there's there are two components. Uh, one is the amount technology and I think the other one is the individualization by designing a product for one human at a time.

11:45 So, let me go through those two. On the technology of MRNA side, what we have known and published uh actually with Kolinska as you know the institute in Sweden that give the novel brao medicine back in 2015 is that with all technology I cannot speak about other mRNA companies that have different mas different lipids and so on but with all technology when we inject our mRNA in a muscle whether it's a co shot or flu shot or this cancer treatment basically the mRNA goes down to your lymph node and enters the APCs the

12:19 antigen presenting cell which as you know are key component of your immune cells and the mRNA gets inside the APCs. This we've demonstrated and proven at the time of Kolinskar and then it basically translate the message contained in the mRNA inside the APCs inside your immune cells and presented from within. So we think it's a very important differentiation from most of the previous vaccine in cancer in the field that were made by protein or peptide that basically are made in reactors are injected in the in the in the

12:51 patient but I basically turn into the blood because as you know recombinant or protein when you inject them they just go into your blood and they turn around so your immune system sees them but not in the same manner from within as it done with the mRNA. So we think that's one very important component of uh immune presentation if that makes sense. The other component is really individualization.

13:14 In the past a lot of time people have tried with non mRNA I protein technology or peptide but also we tried a shared antigen whereas here what we said is because DNA is a disease sorry cancer is a disease of DNA. >> Mhm. What we're going to do here because the cost of sequencing have dropped so much in the last 20 years is we're going to basically take a biopsy of your tumor.

13:40 We're going to read all the letters of its DNA, the 3 gigabyte of of these genes. And then we're going to do the same things with the healthy cell of your body. And we're going to literally compare letter by letter nucleotide by nucleotide. And then we're going to use an algorithm to identify over your hundreds or thousands of mutation which one based on the current knowledge of a field of imunology and cancer which one of those mutations are the most relevant.

14:07 We select the 34 that we believe are the most relevant and we stitch them together into one big mRNA molecule which we make in 30ish days for you that is injected then in a hospital intramuscularly. And so when this is injected in your body, basically it teaches the immune system the signature of your cancer cell that it missed. Not the signature of every other patient with a shared antigen, but the very specific signature of your cancer cell.

14:35 And what we we showed at ASCO and we published from a phase 2 study, but we believe it's the same thing in the phase three because it's mechanistic, is that around 990% of the antigen are different patient to patient. Because when we started we had no idea because again the field came from shared antigen. So when we started like we have no idea we're going to get 2% 5% 90% of of same antigen across all the patient actually 90% of antigen are different from a human to another one.

15:05 >> Wow. So the only way this can work is through personalization individualiz. Exactly. >> You know assuming that that carries over. Um, and so in that regard, uh, uh, you know, if you're doing, let's say, this normal to tumor comparison of of of the genome, you find the differences. I'm curious how you arrive at 34 is up to 34 as the right number. Um, I'm sure there's a very good reason for that.

15:27 Um, but what is the algorithm that enables you to do that? Is this something that's proprietary to Madna? Is this something that is known within the field? Help us understand like help us look into that black box. >> Sure. So I want to share a little bit about the blackbox not too much because there's a lot of knowhow and and things are very confidential to us but basically we started with of course what is known in the field and so we basically use a lot of database and publication and a lot of scientists and doctors

15:56 kind of bestin-class in iminology and in oncology and then from that starting point we use a lot of internal data that we generated over time we also partner with some companies that because they are having the diagnostics space or they are let's say cell therapy and other space in ocology had access to a lot of data a lot of T- cell mapping and so on that were very useful for the learning the the thing that is interesting about the data we shot last week is this is what I consider in this man version 1.0 zero because

16:30 the algorithm from the phase three was the same from the phase two was the same from phase one but because we've been doing this for 10 years it's a 10y year old algorithm so as you look at the data it works pretty well right as we said about the phase two data 80% of people are disease free after 5 years it's amazing for those patients but there's still 20% of patients that don't respond and so one of the thing we're going to be doing now that we have access to a phase three patient data and samples is to go back and

16:58 mine that data to figure out why some patient responded and why some other patient did not respond because we access to all their blood sample the sequence everything and we're going to try to see can we improve the algorithm uh and we will go to CFDA if we find scientific reason why we should change the algorithm to go from let's say 1.0 to a 2.0 zero algorithm and then change it.

17:21 Of course, we have to do that in a very controlled way to ensure we don't lose efficacy very obviously. But the way I think about it is like when we talk about AI, it's we always joke that the current version of AI is the worst we're going to see in our lifetime. Well, it's exactly the same for inaran which is the current version of inaran that moana is the worst version of inismaran you're going to see for the rest of medical history.

17:42 And so that gave me a lot of hope not only in melanoma for those 20% of patient that don't respond but also for potentially over tumors that have been really hard in a field like you know uh pancreas cancer and others where imotherapy doesn't work. We want to be able to learn a lot about the technology using also what the field has learned in the last 10 years because the field has learned a lot as you know this is even not in inspir 1.0 so that's why I'm so excited about what's coming next.

18:12 >> That's fantastic. So, um, looking back, uh, you and I have had, we've known each other for a very long time. Uh, I won't depress you or me by saying how long, >> but you were very young. You in kindergarten, [laughter] >> but so I've had the benefit of of of seeing the Madna from the earliest days. >> Yes. And one thing that is true that was true then is true today is um you are well first of all you are an engineer at heart and um you have from the very very beginning been obsessed with process with operations with

18:45 being efficient. Um, and those things need to be absolutely true if you're going to attempt to do what you're trying to do here with personalized cancer vaccines and make a a you know a medicine for each individual patient precisely because in 90% of the cases there's no overlap uh uh in terms of the antigens. Um, can you talk us uh talk walk us through a bit the operational lift that is required here that you've already had to do to even run the trial, but that you would have to do if you eventually commercialize um

19:18 this product. >> Sure. >> Um and and let me maybe maybe one way to frame it is I think a lot of people think about the other big personalized therapy that exists in cancer is cartis cell therapy, right? And in that case, the thing that and CARTT cell therapy for folks that may not be familiar is this idea that you take you take a patient's tumor um and then you take the patient's immune cells, you take them out of the body and you essentially reprogram the immune cells um and and re-engineer them to be reactive to the

19:48 tumor and put them back into the patient. I'm oversimplifying of course um but that's you know cartis cell therapy in a in a very sort of a simple nutshell. In this case, you're doing in some ways things that are very similar, right? You're you're taking a piece of the tumor and that you have in the form of of a biopsy presumably um and and and you're trying to teach the um you're trying to sequence the tumor to generate a vaccine that is very specific uh to that patient's uh tumor.

20:14 How do you think about um essentially the veinto vein tie? like what needs to be true from the moment you you know sort of see a patient and get access to the tumor to the moment that patient receives their personalized vaccine. >> Sure. So the time is around 42 days now needle to needle. So from taking the biopsy to getting the vaccine in hospital ready for you.

20:38 I think we're going to be able to improve that as we still have a lot of efficiencies to work on and automations and robotics. Uh the place where this is very different from carti is that we don't have to take your immune cells we program them xvivo in a reactor in a fac in our factory and send them back to your hospital. The only thing we need is the information as you and I talked about the beautiful thing of ma it's an information molecule and so basically what we get from the lab is the sequence of your healthy

21:10 cells and the sequence of your cancer cells. So we just get a file uh and then we use that information to basically make the DNA but now we don't make it with plasmid growing ecoli or whatever we make it all synthetic so it's all enzyatic in liquid in water then we make the the RNA from the the template then we put the lipid around it and because of that and that it's a synthetic process it's much more like small molecule than a large molecule you think about carti for me the analogy is the re combinant world >> where

21:46 you have no sales and big reactors and you have big volumes because as you know the the reason you have big volumes in biotech industry is if you compress the cells too much they die you have the same issue with carti so it's all everything is big whereas here because it's all in water and it's all enzyatic meaning it just it's very catalytic the reactors are very very tiny and so what we been doing since before the clinic because as you said we had to start developing the technology to individualize it one human at a

22:14 time to even do a phase one study, right? Is we shrunk everything down. So the first version of a machine that looks like a big American fridge was a bit big and cranky because we told the team make it good enough so you have good quality but we're not solving for efficiencies yet because if it doesn't work in the clinic, what's the point of wasting five years making a beautiful amazing optimized robot if science doesn't work, right?

22:40 So we told the team, make it good so that we have no quality issue and we don't have a false experiment, a false negative in the clinic because it would be terrible for patients if you build a robot kind of working but not working. You run the study and it tells you the science doesn't work and you don't if it should have worked, right? That would be a disaster for humanity obviously.

22:59 And so the team did exactly that. They developed a robot that was good in term of quality but it was not very efficient. uh and when we got the phase two data that this was working the first inter phase two data at only two years now we have five years of data maturing beautifully for duration of efficacy we told the team okay now we're behind and knew this was going to happen in case of success which is a happy program and so we dedicate a lot of very smart engineers to think about okay now how do you make a very

23:30 efficient machine that you can compress even the volume of a machine because one of the important vector of course is time as you mentioned cycle time from needle to needle, but also cost. And so one way to reduce the cost is reduce the footprint on the floor because if you have a fixed envelope of a clean room facility and you can put let's 2x or 10x more machines in that surface area, of course you're going to get a bigger throughput and a much lower price on your fixed cost.

24:00 And so we are obsessed about cycle time because the more minimal more you can reduce cycle time the more you can get a turnaround let's say in a year on your assets and the second vector I'm obsessed about is square inches [laughter] and literally I'm always a pain but sorry when I go to the factory to look you know at all the space we can save how we can be creative how even we can put some compute out of the clean rooms just to shrink things as much as you can because a huge impact on cost of the product at the end

24:31 of the day >> and had um at scale um how many roughly how many for let's say let's just focus on melanoma how many doses would you need to produce in a given year >> so we've already got thousands of doses because of nine clinical studies that are ongoing >> uh the facility will be able to make the one in Marboro mass at tens of thousands of doses u and then as we keep improving the technology that number is going to go up in the same facility and then we might need to build several other facilities but if you look at

25:05 the incidence of melanoma if you tens of thousand of doses you're going to cover you're going to easily the melanoma market >> yeah I I can believe that um have you disclosed how you think about cogs and price or is that something that >> we have not disclosed yet uh we need first to disclose the data with our mer colleagues we need to engage with the payers once we can share the data with in term of what is the value being driven there and so on.

25:31 Um but this has not been discussed yet. >> So maybe one place to focus is uh on this concept of personalization. Uh I'm sure you've seen uh the story of the of the GitLab founder uh who went founder mode on his on his own uh osteocaroma. Um uh number one uh do the future SIDS of the world um come to Madna or is is this end of one phenomena something that you think will just happen and exist in parallel.

26:03 >> So I think they will come most of them to Mona because he will just going to be easier and safer because as you know uh making an injectable product always carry risk of contamination of a product. If you inject to somebody a product that has even one copy of bacteria, you might give the patient sepsis, then you always have a question of quality because when you have a multi-step process, a mistake can happen.

26:27 And of course, if it's industrialized and has been validated in term of good manufacturing practice kind of FDA standard, you have much less chance of this happening. So, it's a bit like every tools in life, which is, you know, do you make your first knife because there's no knife store and you are in a cave and you need to feed your family? Yes, of course you make your first knife because you have to feed your family, right?

26:48 But when you have a a store making high quality knives down the street, you're going to use your time to do something else. So I think it's a bit of the same phenomena which is like in any technology which is when you have industrial scale of high quality product, you use your time as a human to do something else with your time, right? >> And in that world um how does the how does the regulatory envir uh the regulatory apparatus function here?

27:12 So in other words, you you mentioned earlier uh you along with Merc will prepare um a regulatory filing soon. Um what gets regulated here? Because every dose is different. So obviously every dose doesn't get approved. Is it is it obviously the the the process for synthesizing mRNA? Is it the algorithm? Is it a combination of the the entire system? Help us uh understand that and build intuition around how we think that these kinds of personalized medicines will be regulated in the future.

27:43 Yes. So the good news is there are precedents as you mentioned Karti. Karti was also approved in the same way as we believe Intaran will be which is as a process BLA uh not a product BLA. So as you know Mon has five product approved. So that's really product approval on this one. the whole process since we started in the clinic the IND was a process IND >> because we had to ask the FDA can we go to the clinic do you think it's safe and do you think we have a good control of a process so we can do safely a phase one

28:15 study so we already had that discussion just to go into the clinic years ago and then before we started every phase three we need to have end of phase two meeting and agree the design of a study this the manufacturing protocol with the FDA so those discussions have happened for years and So it's not like we have not talked to FD for the last 10 years I'm going to show up at their you know front door you know in a week or two and tell them this is a new product and they're going like what is this there's been a lot of

28:43 discussion a lot of engagement several time we've had technical question on the manufacturing front where we basically requested additional meetings to ask the guidance to also educate them on the technology what we've learned and so on. So there's already been a lot of discussions and there's a very clear regulatory pathway in ter of approving the entire process.

29:05 Basically, what the FDA wants to know, which is very legitimate, which I would want for my own family sake, obviously, which is if you give if you get the same sample at the beginning, the tumor and the blood, do you get the same product make at the end of a big black box? And that's what we have to demonstrate first to ourself and then with the data to the FDA so that we have really robustness overall process.

29:28 So if we have a same input, we're going to get the same output going to patient as an individualized medicine. >> How do we think about uh moving beyond or how do you all think about moving beyond melanoma? Uh is this an approach that's going to be uh applicable to a broad range of cancers? Are there cancers that are much more likely where this is going to be a viable option versus others?

29:52 uh and sort of what's your u I'll use the word what's the ambition here for where uh cancer vaccines can can happen >> the ambition is pretty big because we believe we have demonstrated at least to ourselves and we hope to the world there's always will be skeptics but that's that's always true that we are able to create an educo education of T cell and this we showed it even at this year we took the blood before treatment after treatment of cancer patient melanoma we technology and we showed that don't think we have an

30:26 expansion of a tea cells but we have denovo new tea cells being created that recognize what we code in the mRNA that was not in the patient's body before the treatment so we really in my book have proven to ourselves and to the clinical community that moderniz technology can develop teach the immune system to develop new T cell to go attack your cancer So based on that there are basically I would say three different vectors we're going after in term of expansion from melanoma.

31:00 So this study to remind people was a cancer patient in stage two, stage three and stage four that were enrolled in that phase three study. So what we're doing is we're going first everywhere where kudra works because as I told you we believe the mechanism of action of a PD1 and modern scientist man are totally orthogonal. So we think these allow to have synergistic element and performance or efficacy for the patients.

31:26 And so we are in phase three for lung. Uh we're in phase two for kidney cancer, bladder cancer. So we have a world slew of studies ongoing where the world knows that kudra works because keta has been approved there. And we believe you're going to see a material improvement versus ketra alone. Before you run the clinical experiment, it's impossible to know are you going to get 50% like we saw in the phase two of melanoma or you going to get 30% or 40% or another number.

31:54 We have to run the study. So these are a lot ongoing. The second vector is to go early in disease where checkpoint work and the best example is we announced in the spring of 2026 starting a phase three study for patient with stage one lung cancer. But as in so modernized product as a monotherapy without checkpoint. >> Wow. >> And we are doing that because we believe when you go early in disease checkpoints are not used because of a side effect that they bring.

32:27 >> Mhm. Because if you have stage one cancer, the medical field thinks it's worth monitoring your cancer versus giving you a checkpoint because not everybody's going to respond. But everybody's going to get pretty serious lifelong side effects like autoimmune disease. But what if you could have an MRNA made for cancer patient that has lung disease stage one, which you can find easily with X-ray.

32:51 Let's say in former smokers is the easiest target population. Just screen regularly with X-ray your former smokers and if you do it regularly you're going to go from not seeing the cancer to seeing the cancer and then the idea is do you do a surgery which is to the side of care and you give in monotherapy which the side effect is similar to a vaccine you know you might feel tired for a day but that's it.

33:16 So in cancer it's a pretty cool type of side effect right uh and that's another approach we have in term of clinical studies uh where checkpoints are not available today the third vector is where checkpoint don't work so of course it's where you have a highest risk >> but because the mechanism of action is different from a checkpoint we and M believe that there's a very good scientific rational to go try so two places we're trying right now is pancreas cancer and also gastric cancer.

33:47 Those two cancer type checkpoints and ketra do not work. The clinical studies have been run in the past and they were negative. But we think because again the mechanism of action is different from checkpoint and we know now that we have a proof that we can create denovo T cell. We think it's an experiment worth running. If we have good signal we will think about combination.

34:10 And as you know literally yesterday you know revolution medicine had a wonderful new medicine approved for pancras cancer using the kas mutation. What if you could combine that medicine and in this man those are very orthogonal mechanism of action. For me it makes no scientific sense that if it is work and we should know soon in in pediatric sorry in in pancreas cancer by itself and of course the revolution medicine uh does great improvement of survival in pancreas cancer.

34:43 If you combine those two things we believe it would have benefit again you need to run the clinical experiment to see how much but that's the type of things we're going to want to do. So if you think about in tisan the modern medicine is going to be used with kidra it's going to be used early in diseases where kidra and it might be used in places where kidra doesn't work but with over agents >> well that that gives a lot of uh reason for I think uh cancer patients and their families to have a lot of hope >> for the

35:13 future of of of novel therapies in this field. >> Yes and and on top of of what we just said remember this is inan 1.0 zero. So what I saw is very powerful and I'm really pushing our team uh to think really outside the box and to do a lot of analysis and to use AI to look at that gigantic set of data that we have which is what are the things we can learn from the clinical studies to understand about the people that did not respond because I think you always learn more from things that don't work and things that work.

35:43 So I want to obsess about the 20% of patient that do not respond 5 years out so we can understand why they did not respond and can we to trick anything in the algorithm or in the technology to be able to help them. Wow, that's remarkable. And I just to just to wrap, I think, uh it's remarkable to see how you were able to take a technology platform that originally wasn't built for a uh you know, a pandemic, uh pointed at a pandemic, create a vaccine for millions and millions and millions of people, and essentially point

36:18 it back towards uh treating some of the diseases that you had originally intended uh 10 years later as as you described. It's very remarkable and the piece that's going to be exciting or maybe to close is before the end of the year we should have our pivotal study so late stage study for rare genetic disease for kids that have rare genetic disease of a liver.

36:38 So it's another vertical which we pointing the technology the phase one two have shown kids three years on drugs doing fantastic. So we'll see when we get that data and in June we had our annual science day and we announced that the next mountain where we are appointing mRNA platform is autoimmune disease because if you think about it we've learned a lot for infectious disease which are know mediated by the immune system cancer we just been talking a lot about immune system.

37:05 So we learned so much about the immune system that we think we have some very novel approach on how to treat the root cause of autoimmune disease not the symptoms which is what the farmer industry has been doing. It's of course very helpful to patient to treat the symptoms so we can have higher quality of life but it doesn't treat the root cause and we think we might have find ways to use the immune system to treat the root cause of autoimmune disease.

37:29 So there's still a lot of ways to point the platform. So we're quite exciting about what's what's to come. Would the theory there be that you'd have personalized autoimmune modulators or would this be more a product or more process? >> So, we we're doing both. So, what we presented in the in the spring was a product that would be the same for everybody.

37:48 But what I'm the most excited about which is in the lab still is the ability to do individualized autoimmune treatment where you target directly to the immune cells that are attacking your body as self when you have an autoimmune disease and to have basically part of your immune system going attacking those immune cells that are out of order so that you able to take the symptom of immune disease out.

38:12 Again, it's still early days but that's what I'm excited about today. Well, going from infectious disease to cancer to eventually autoimmune disease, we would love to have you back on the podcast to film episode 3 and complete the trilogy when you're ready. >> Wonderful. >> Stefan, thank you so much for joining us on the A6 and Z podcast. It's always, as always, it's great to see you >> and congratulations