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Christine Yuan HUANG, Founder and CEO, Quantum Life Alex ZHAVORONKOV, Founder and CEO, Insilico Medicine Moderator: Jeremy KAHN, FORTUNE

Brainstorm AI Singapore 2024: The 1,000-Year-Old Human

Conferences

So the title of this panel is deliberately a bit provocative, but there are several ways in which A I you could think about potentially extending human human lifespans. And one is through better treatment of disease. That's one way of looking at it. Another might be well through wellness, you know, sort of helping us live healthier lives. Another might be to actually try to treat aging specifically. So taking some sort of compound that would reverse the aging, the aging process. And lastly, and probably most sort of science sci fi would be the idea that somehow we're going to through brain computer interfaces or some future technology kind of merge with, with an artificial intelligence. And while our, you know, we might be able to extend our consciousness, even if our physical bodies don't, don't make it. Um I wanna, I wanna ask our, our guests because they're um they are experts in this area where they think the most impact from A I will be Christine. Let's start with, with you and then, and then Alex. Thank you. Thank you so much. Thank you, Fortune. Thank you Jeremy for the wonderful panel. Um I'm Christine. I'm from quantum life. And the quantum life is actually doing quantum enhanced artificial intelligence for precision medicine and for integrating human beings longitudinal data to predict more and more accuracy plans for human um health span. And of course, maybe later on its lifespan. So uh we are so excited for this new technology merging up and we are we have already seen for human life. And for the past like 30 years, we have already had a lot of changes in um Medicare in healthcare in pharmaceutical air driven pharmaceutical development. But the question is for human beings, we always think about birth, aging, disease and death. We are in Singapore. Some people are talking in uh in Chinese, we call it Shen Laing, right? We never think about we previously, we are all focusing on disease which is a disease and death. But how about aging before disease? That is how we wanted to introduce A I to improve before disease. And we have already seen during the past 10 years. Liquid biopsy, which is highly um based on the bos metics being there, which is make great progress in cancer prediction. And for disease prevention, which means a shift from the disease care before had based on the autonomous diagnostic. Before this, we had Q prevention. And next question that is going to be how we develop and interventions through the process. That is how A I will be introduced and we are going to move forward So this is on the idea of prevention as a way to forestall disease and to improve lifespan. Alex. I think you may have a, well, you may agree with some of that, but I'm curious what your view is on this. I think you might have a slightly different perspective on where you think the biggest impact will ultimately be on longevity. Sure. Uh So I um I wear many hats and one hat is in Silicon medicine, CEO and another hat. Um I co-founded a conference called Aging Research uh for Drug Discovery. Uh It's an annual event in Copenhagen uh every August uh for five days. A lot of people come to this conference and uh we convene and talk about aging. So it's probably the largest event right now. Uh And when I started the conference, um most of the scientists were talking about diet exercise, sleep, behavioral medication, right? And then of course, you know, about 30% of the delegates at that conference are also going after very, very visionary things that are way beyond that. But at the end of the day, um it always goes back to diet, exercise, sleep, uh and the hero medications. Uh And uh what I'm afraid of the most is that in the next 10 years, I'm already 46. Uh So kind of for me, it's all like you, you, you, you look into the future. It's all down hill, right? Sorry for both males and females at that time. So, whatever I do, it has to have more impact than just diet, exercise, sleep. So I think at most conferences, we need to start thinking about, you know, diet exercise, sleep prevention, diagnostics should be the baseline, right. So let's do what your mother told you. Just let's stop talking about us, do it, right? Some people are still smoking, right. Um, so we need to have something much more substantial, a big breakthrough cha GP T moment in longevity in order to get people excited and get them, steer them away from, you know, diet, exercise, sleep, behavioral medications, do what your mother told you because we know that works. It will work to some extent. But billions of people live their lives of them have optimized for diet, exercise, sleep and they still aged, got diseases and died. So we need to go radically to the next level. So far from what I've seen, nothing works on humans, there is nothing clinically proven to. No drug is clinically proven to demonstrate age reversal in human beings. So we are still trying to prove that some of the drugs that are known for 3040 years may have some impact even though they work in mice really well. So like creamy and for example, 15 to 20% consistent life extension in mice. And now people are doing combos looking at different ways to different molecules to go after aging. But I think that we need to have a fundamental breakthrough in order to live longer. And of course, things like um you know, brain clear interfaces very promising. So there you are not only maybe reducing the rate of loss during aging, you might actually augment your function, like think about glasses, right? Very easy innovation, but it restored a useful function. Plus nowadays, with 3D glasses, um you might be able to with a reality, you might gain new functions. So I think we need to go and look at the next level. I think that the next level is going to be drugs that target aging and disease. At the same time, if you are just looking for using A I to discover drugs that only go after aging, I think you won't be able to develop it into a drug for many reasons. One is just basic economics, you won't be able to reimburse it. Most people understand how difficult and costly it is to develop a drug. And also you have to be committed for 10 years or longer, right? So I run in silica for 10 years. My most leading drug is in phase two clinical trials. So in, as they say, in the Middle East, uh if we are successful with phase two and we go and you know, target the disease uh and show the proof of concept in maybe like 34 years, we will be able to show that it works on aging. Hopefully because that's how we discovered that using aging research. Uh So you have to be very committed for a very long time to demonstrate even a little bit of, of, of efficacy. Alex. Can you talk a little bit about how A I is, is maybe speeding up some of that process? And I know you're getting this but um have some of the most number of A I discovered drugs currently in various phases of clinical trials including in phase two, a drug for idiopathic pulmonary fibrosis, which is a drug that's targeting both the disease but also has interesting implications for aging. So, can you talk a little bit about first of all, like how A I can accelerate that process that 10 year drug discovery process? But also this dual strategy of going after things that both target unknown disease but may also have implication for age reversal. Sure. So first I need to kind of tell you and that's probably useful for all of you. A story of how drugs are discovered and developed. So, drug discovery and development is split in two phases. Basically drug discovery where you discover the drugs and ensure that you complete all the preclinical experiments and on development, it's human clinical trials. Phase one safety, phase two efficacy, phase three, safety and efficacy. That part cannot be dramatically expedited because you have to move with the speed of traffic, it's very regulated. Uh but the part where you discover drugs can be substantially accelerated. Um And so far, we've demonstrated at least 50% increase for the slowest of our drugs. In some cases, we've demonstrated that we can shave off 70% of the time and maybe 90% of the cost of drug discovery using A I. Um And drug discovery is split into three phases. One is formulating the mechanism of disease. So you need to understand why disease happens. So usually for Alzheimer's, for example, we still don't know why it happens and what's driving it. Then after you formulated the mechanism of disease, you need to understand which proteins or other features in biology are driving those diseases, we call them protein targets. So you identify protein targets that may work in a disease. Then you design a small molecule, a drug or a biologic, an antibody that disables that kind of criminal that you've identified that is driving the disease and that's a completely different kind of type of work that you need to do. Usually that's very two different separated areas in the pharmaceutical company. And then once you've designed the molecule, you need to conduct safety studies. So you need to go into animals and demonstrate preferably in more than one animal that the drug. Well, first of all works, so it's effective and it's also safe. So you call it I and enabling studies, safety studies and can start humans. So in our case, we use kind of life models, the models that understands human biology from cradle to the grave and also understand diseases to pick those protein targets that are implicated on aging and disease at the same time. So there are few. And then we have another form of generative A I that designs small molecules with the desired properties. Instead of looking for a needle in a haystack, you generate a bunch of perfect needles and then you synthesized test, go through the clinic, preclinical trials, go into clinical trials and then we either take it ourselves or we sell those molecules to other parties. So we actually sold a couple of drugs very successfully. And you know, last year we did $1.5 billion in total deal value selling those drugs. So those A I generated drugs that are faster and cheaper to make are actually of higher quality. So people are willing to pay a lot of money for it, right? Christine, you were telling me as we were coming in that you're also sort of looking at this idea of creating a life model using A I but maybe also to pick up on indicators of disease earlier and then you can have earlier intervention is sort of your idea of how you could extend life. Can you talk a little bit more about, about that and what you're doing? And then I want to go to questions from the audience. So please think of your questions. Hopefully we'll get one or two in. Yeah, exactly. Thank you Jeremy and, and just as I have mentioned, um because for drug discovery, it's a very, very long journey. And currently we also have the drug discovery from the preclinical stage. And Ino did amazing work driving forward the A I drug discovery to the to the clinical stage. But the question is we still need to facing the clinical stage, right? We still need to take like 10 years to get through all the regulatory approval. I have to say for bringing in new medicine or bringing in effective drugs or cures or intervention to our life. It's not only about pharmaceutical, it's not only about drugs, it's also about the one regulatory. Secondly, what is a personalized red drug for ourselves? So during the past 30 years and during the medical, if we leave our Western medicine is true, like to the past 200 years, we will figure it out. Ok. So actually the medicine drug, the drug development, the system is all because of based on one fundamental for medicine, which is, which is autonomy and that is a fundamental curriculum in medical school. Like I'm training as a medical doctor. So that is the the first year of curriculum. But during the past 10 years, we already shift our diagnostic window before the traditional autonomy because you think about it, how we diagnose the disease, we do autonomy and we have our brain, our liver, our stomach, right? And now what has happened during the past 30 years, the next generation sequencing has already shown up and a lot of molecular information has already been discovered. And together with the BOS Matic, which is the A I for the past 10 years, we have already developed several pre diagnostic tools like Guri developed the cancer prevention. OK. So the next error going to be, think about it, we have already shift a little bit the cancer diagnostic stage from the late stage like two and 32, the four stage zero. And what is the cure for the stage zero? And we need to develop a cure for the stage zero. And that is how quantum life wanted to discover from stage zero. And we compare that is not only disease we compare which is younger, healthy group and older group. And we discover what is the red intervention, what is the red target before any disease happening? And we develop a cure for the stage zero. So that is how we can make jobs for extending the life span and health span. Then think about it if we have this kind of interventions already, maybe drugs, maybe some other treatment like medical device can already be a treatment to the C zero. And what is the next step? The next step? That is how we can compare. Where is the right? Ah If we are talking about the precision Medicine, where is the database, how we can have the database, how we can make the individual personalize the medicine for ourselves. So we also need to have the database for ourselves. That is, you have to, you have to record a lot of data about each individual person in order to deliver the precision medicine because the data you have on the whole population may not be actually be relevant. That's interesting. Let's take some questions from the audience, at least let's get one in while we have time. Who has a question? Please raise your hand. We'll get a mic to you. There's I see there's a woman at the table over here. If we can get a mic to her, please, there's someone running over to you and if you could please stand up and state your name, that would be great. And where are you from? My name is Mia. So I'm Global Cio for it from G LP. So I have a question around. So the A I, the role A I plays in medical and where do you see the obstacles of further utilization of A I? Either in um this is not just precision medicine but diagnosis of a disease but also in a longer term health care really care about people. Is that a compliance issue or government regulatory or other obstacles? So I guess what, how you know, how do you make sure that A I is actually helping us bring out better treatment, maybe assisting doctors in a better way and what are some of the hurdles for that, whether they're regulatory or not? I know Alex was talking about a number of regulatory hurdles on that he's facing in terms of just getting, getting treatments out. But I'm curious on this wider question, do you have a view on, you know how a I might assist doctors in picking the best treatments for individuals and then maybe Christine can answer. So sure, actually, I will give the time to Christine on that because she is working on that area more than I do. Um So on the population level, how do you um use A I for health care? Uh That is um the dream, it's actually the actually the dream for human beings and for the current pub public health system, that is to say that is how we wanted to bringing Medicare beneficial to human beings and through medical doctor system as soon as possible of. So the, so the first of all A I for diagnostics, we can exactly enhance the diagnostic errors of the make medical doctor, radiologist to, to, to, to have a better, have a better diagnostics. And also for me, doctors can select a better drug and bringing the drug repro because some of the interventions may not need a new drug. We need to figure out how is the right drug for the right person? Maybe that the drug is for traditional drugs like Metformin, Metformin is very cheap and Metformin. Currently, we also see the um effect on extending human lifespan. Basically, it has been, but it's very cheap. So uh we are considering if we can use it and bring it to the healthcare system, I have a question on this, which is sort of, I feel like there's a lot of these medical techniques that are sort of have have been invented. They, they there have been a number of breakthroughs yet, the distribution of that is a problem. So we have this big inequality issue depending on where you live or even which doctor you happen to see your doctor might know about this breakthrough treatment, might be able to prescribe. It might do genetic testing, might not do genetic testing. I feel like there's this great disparity in the health care we receive just on some of these very arbitrary factors about where you live and what doctor you happen to have. I don't know if you can say anything about that Christine and maybe A I can help sort of bring the general standard of care up to a certain level. Exactly. Exactly. So, yeah, I think that A I is the ultimate um uh democratizer, enabler, and unifier, and equalizer. So now with C GP T, everyone here in the room can uh you know, can have more access to knowledge than some of the best doctors just five years ago. So you can take a picture of uh you know, your wound for example, and let it annotate it. And I hope it doesn't hallucinate an answer. Well, yeah, it has a problem, right. But it has a very good demo of what is possible because you give it a little bit more reinforcement learning. And if you don't need a snappy response, you can actually, you know, have a multi agent system that will try to solve your question in maybe a little bit more time than it doesn't hallucinate where you eliminate some of the areas where it could hallucinate. Um And it can answer very accurately. So it shows you that with technology, we actually not only uh equalize some of the professions, but we also make them very accessible to everybody democratize and we gain superpowers. And I think that we need to use this technology right now as much as possible to identify interventions that can target aging at a core and make this technology available to everybody. That's why we are very focused on small molecule drugs because small molecules, unlike biologics, they are super cheap to make. Once they go off patent, they are available to everybody on the planet for peanuts and they are also very easy to store to transport and people are very used to pills, right? Rather than injecting expensive biological. There's lots of potential for A I to democratize access to health and access to hopefully these new medicines, we're out of time everybody. But thank you very much for listening. And thank you to our panelists, Alex and Christine. Thank you so much. Thank you, Singapore. Thank you. Thank you so much.

Yin Ye, Chief Executive Officer And Executive Director Of Bgi Group  Alex Zhavoronkov, Founder And Ceo, Insilico Medicine  Moderated By Yvonne Xie, New Media Executive Editor, Fortune China

Innovation Forum 2024: Shaping The Next Era Of Healthcare

I, I'm thrilled to have Alex and Y with me today, the work you're doing is really shaping the future of healthcare. And please note missing, we'll be speaking in Chinese and headphones at your seat for you. Um As my, we just noted the fight against COVID-19 is really a worldwide collaborative effort. And now as we are entering the post Pande era, um which area of collaboration in the healthcare sector are most critical Alex. So I think that it's extremely important to collaborate in drug discovery. Uh That is the most critical area for all of us because we can live without a cell phone even. Well. Nowadays, it's difficult but we can live without a better car, we can live without uh you know, flying business class, but we cannot live without cancer, drugs or drugs targeting Alzheimer's or Parkinson's or infectious diseases as we've just learned from the pandemic. So I think the global collaboration in drug discovery is fundamental and of course biology is so complex that we need to work together to solve it. Um So Mr Ying, what, what's your view? Uh I will continue on that and talking more about data actually, we have mentioned that we are now in the era of artificial intelligence. So we will have a lot of data. If we not, we if we don't share the information from like China American and European, then we will enter the silo effect which will cause waste. Actually, when we first identify the genome of human being, scientists have mentioned that we need to share this kind of information uh when we protect the personal privacy. So I believe that such words are right for the present, whether it is for the anti aging research, we really look forward to that it's really critical and in which area can the results of collaboration can be seen most immediately? Alex. So again, I think that uh we need to collaborate on drug discovery, that's where you would be able to see the results of collaboration most immediately. Uh because patients cannot wait and when it comes to aging, all of us are patients and we should be very impatient patients. Uh So uh we probably will see the result of collaboration uh immediately in personalized medicine because uh that's where we can use already existing drugs to repurpose them towards specific diseases that might have not been targeted by those drugs immediately. Uh So that's one of the areas where we need to collaborate. And by the way, China is the world's largest developed uh uh market with 1.4 billion people. More than 1.4 billion people. So having a very good understanding of how different drugs work and different diseases. And so many people can uncover huge potential for us to live longer and better life and very rapidly because we are looking at already existing drugs. And the second wave of course, of course, innovative novel drug discovery. Uh they are China over the past 20 years, has developed the world's largest and the most efficient infrastructure for chemical synthesis, biological uh manufacturing and very rapid testing. So if you are designing anywhere in the world, you might want to come to China and do the testing and uh synthesis and uh you don't need to rebuild this infrastructure. Of course, it's a good idea to have that infrastructure in every country. But I think that right now we need to focus on efficiency and delivering really fast um uh very effective solutions to patients in need who are dying. Mhm um So Mr Yy, what would be your ideas? I think that's still about the uh data, for example, about those rare disease, it seems rare by the name. But if we um take the humanity, all the human beings as a whole, we have, for example, like we have 8 billion people in uh every year, we will have 90 million newborns every year. And the um the incidence rates of the birth defect about 4 4%. So if we can share all this data altogether, we can have uh like more insights for the precision medicine. So we will be a community of shared future. Alice. You mentioned that before the A I develop, develop drugs can really hit the market in silico. Your company still has a long way to go and you are still facing a lot of obstacles. What are these obstacles you're facing? So sure there are several obstacles that everybody is facing. And one of these obstacles is that biology is very complex. Biology is perhaps the most complex area that humans need to be focusing on collaborating and making a lot of bets that are going to fail. So in drug discovery, currently, if you start with a novel mechanism of disease, so you never touched that target before the protein that is driving the disease and you want to take it to the market. The probability of taking it to the market is less than 1%. So it's not about the time or the cost is just the sheer probability of success is low. That's why I like to call it the molecular casino. Uh And in this molecular casino, the wheel is is spinning for 12 years and you actually, every time it touches some number there is a probability of success of failure. So 90% of the drugs that are in human clinical trials that enter human clinical trials fail. That is why uh people who invest in this field are extremely sophisticated and they understand the risks and benefits and the payouts if you've got the drug approved are gigantic because a lot of people start using the drug and the health care system reimburses uh your research and development expenses. Uh So just complexity of human biology and the probability of failure, that's our greatest threat. The second greatest threat is actually currently that something that I don't understand is geopolitical tensions. So previously, I thought that that would be off limits, right? Because we all agree that life is the fundamental human, right? After that, there are no rights after you're dead. So we need to figure out how to collaborate internationally, at least in this area. Um So uh so for Mr Yin, you have already um led the team to uh to, to serve for those rare diseases like a Os, including th uh he is a famous patient of a Os. So when you are helping these people, what are the biggest obstacles ahead of you? Can you introduction of Mr Tale? Maybe uh today, not many people know him in this event. Uh Actually Tale is really famous and when we and actually Tale is a patient of a Os and you may, when we talk about a Os, you may think of ice bucket challenge or Stephen Hawking. So during the past several cents centuries, we do not have a cure, direct cure for L OS. And at the beginning of this year, Tale had a conversation with Ban Ki Moon and as a patient, he has, he he he really advocates a big um research over this rare disease. So I think for rare diseases, the most important thing is that we need to acknowledge it, we need to know it, we need to under it. So for a rare disease, if there is only for example, like 1000 patients in one country, there are so many countries in the world, the total population of this rare disease will be really big. And also there is a cognitive misunderstanding thinking that there are no rare diseases in their ancestors for eight generations and they will not get them. It's a misunderstanding because no one is perfect on genome. For example, I personally carry seven serious defect genes. The important thing is that my wife cannot carry the same position. Otherwise our offspring will have high probability of getting sick. So the rare disease are actually born by normal people and the normal people is in quotation marks. And currently the cost of treatment is too, is very expensive. And now the gene therapy drugs is really is very, very costly, about 2 million to million. So we have a long way to go to conquer rare diseases. But anyway, we need to start from understanding these rare diseases. Yes, indeed. So the first step is to make all the rare diseases being seen. Thank you, Tyler. Thank you, Stephen. Um There is the first step and always the last mile and what's the last mile in application of A I developed drugs? What what factors influences um its scalability of these drugs. So A I discovered drugs in my opinion, are actually better than the drugs discovered using scientific serendipity. Because intelligence design allows you for multi parameter optimization. Where instead of looking for a needle in a haystack, you create multiple perfect needles and you test those perfect needles in a test setting that is geared towards a very specific disease or a broader spectrum of diseases. So we've demonstrated several times already that we can design molecules with surgical, with nano perfection that are like to become good drugs. So they are yet to be brought to the market. But we sold one for example, for $80 million upfront. So demonstrating that it could become a very solid business model. So it's not just blah, blah, you create a drug using A I uh in like a year, you sell it to a company that is specializing in drug development and you make $80 million upfront. Plus you take milestones and royalties. So we actually think that A I discovered drugs are likely to be better than the drugs that you can find using high throughput screening or from molecular libraries. So and the biggest bottleneck right now for us is actually testing. So very unlike images or that is A I generated where humans can very quickly look at the output and say, oh it's correct. I like it. In drug discovery. You actually need to perform the experiment and first you perform the experiment on animals, then you go humans, you can go more humans, you can go different humans, bigger human population. So that is the bottleneck and that is a very well regulated area. You can, you can basically like Tesla, you can accelerate from 0 to 60 then you move with the speed of traffic because it's very regulated. So we would of course like to figure out ways to go around that. And on that point, if I do have a little bit of time, I'd like to expand on als. So one really cool example that I can bring of our global collaboration. Recently 2022 in March, I go to a conference at Johns Hopkins called Answer A LS Consortium. It's the head of Neuroscience Chair of Neuroscience at Harvard MG eight JH Merit. If Merit is listening, uh the B and Jeffrey Rothstein for counterpart at Hopkins, they created a massive data set of A LS patient data openly available. You can apply for it and take the data. So we applied for that right after the conference, took this data for um cells collected from IPs for LS patient and reprogrammed into human neurons. So it's a very, very good data source. We combine it with many other sources of data, run it on our A I platform identified new targets and possible drugs to repurpose, tested it in Mayo clinic's model of flies, fly neurons in real flies show that some of those targets worked. And then uh we met Dr Lu Bai Bai Lu, very famous Chinese American neuroscientist at Tsinghua University who is pioneering multiple neurodegenerative diseases including A LS. He found one of those targets to be very promising to one of those already existing drugs and started a phase two clinical trials in China. Um Principal investigator in this, the target was to enroll, I think 60 patients in the US. It would take you years to enroll so many in China A S is not a rare disease, they are fully enrolled right now waiting for the outcome. So even if it fails, just the sheer speed of that exercise and global collaboration can take this process of trial and error that fails most of the time anyway to the next level. That's why global collaboration is extremely important and that's what we need to do more and more. So collaboration is definitely our keyword for this discussion and the keyword for this whole summit is A I. So uh Mr Yin has BG I already deployed generative artificial intelligence. What role can I play? Actually, we're doing it. We are actually helping doctors and even public quickly understand the language of life. Uh language like languages including English and Chinese or languages used by human beings. Um And English is a um unilateral, it is actually a one dimensional language. And for Chinese, I think it's a two dimensional language because we have characters. But for DNA, it's a three dimensional language and it has a or it is a four dimensional one because it has a space. So I think we can use N LP or A IGC to do development and we can also use uh and also for A I, there are three pillars uh algorithms commuting power and data. And now I think we still do not have enough data to uh to support the development. And for the, for the first, for the first human genome, we spent $3.8 billion to produce the first human genome. But now it will only cost us $99 and we can have a multi uh we, we can have a multigene, we can combine all of those different materials altogether. And nowadays, um nowadays, we have the evidence based medicine, they only accept inductions. That's so, so even if we can use A I to predict things, we, we should still prove that in the real world. So we need to have a digital twin here. So we will be very time costly. You need to have a lot of time, spend a lot of time to prove that. Um Alex I noticed you recently published a study in nature biotechnology. You called it the most important paper of your life. What's the big deal of this paper? Can you explain. So sure we publish a paper every 10 days in in good journals. But that one was very, very special because it combines uh the story of drug discovery from the very, very early stages where you try to formulate the disease, identify protein targets using A I uh then generate novel chemistry, novel chemical matter using A I. Then we performed many, many, many experiments in vitro in a Petri dish, done many, many, many experiments in multiple different animal models in mice and rats and dog showing that the drug is effective in lung fibrosis and multiple other fibrotic diseases. So we perturbed that type of biology. We also did something really special called hallmarks of aging assessment for the first time. So we looked for protein targets that are implicated on aging and disease at the same time because my philosophy is that if the drug is powerful enough to work in some hallmarks of aging, it's likely to work in multiple age related diseases. And then we took in of phase zero human clinical trials just to see if it's nontoxic for, for humans, did that check mark and then we did two phase one human clinical trials demonstrating the safety. Now, this drug is in phase two. And in this paper, we are outlined this entire journey, the probability of success of that of having this uh go go to phase two is less than 1%. And I bet my life on it so it can still fail. But that milestone now published in Nature Biotic, we also created a resource even for peer reviews. When people were reviewing the paper, we provided a form of charge GP T where you can talk to the paper and ask it questions and many other in silo papers and get the response because it's such a multidisciplinary paper. When medicinal chemists look at it, they ask medicinal chemists questions but they overlook the A I part and the clinical part and the biology part. So it had many, many reviewers with many, many questions. Nowadays, if you go to five papers in silico gpt.com, you can actually talk to this paper and ask it questions today. It was accessed more than 42,000 times in the first two weeks were very popular paper. So do look at it and the gene is called tick that we've targeted. I think it is going to work in many diseases and hopefully aging recently, the University of Copenhagen showed that mice that lack those genes are they when they are put on high fat, high sugar diet. So they basically live their live on cheese cake for their life. They don't get fat and they actually muscle. That's what you want, right? So serendipity since you've mentioned about agent several times, I know that the ultimate goal of your company is to extend human life lifespan. How so sure, I think it should be the goal for everybody here because it's the most altruistic and egoistic goal. At the same time, I think that all of us are going to age and die at some point in time, it would be great to die younger later. And the way we are approaching it is in a very traditional way within the pharmaceutical, traditional paradigm where we discover drugs, put them on the market and sell and then use the proceeds to reinvest and research to understand the fundamental human biology. And actually, I think it's a shared goal even with BG because from what I know one of the founders, Wang Jian, I made a statement that he wants every BG I employee to live to 100 or 100 and 20 right? That he was criticized for making that claim, but it's not a bad claim to make, right? And it's not a bad goal to set for yourself. Because one thing that I want all of you together from this conference, from my talk at least is that the longer you expect live, the younger you are going to behave and by extension, the longer you're going to live. So just psychologically, you should already prepare for a much longer lifespans. And if you think that technology is going to stand still for the next 20 years, you don't understand exponentials because what we have done on the paper is pretty revolutionary. But look at some of our other papers for many other drugs that we are developing. Uh And uh they have like really, really good fundamental understanding of aging um come to our conference in Copenhagen. Uh And um I think that uh you should already set your longevity expectations way beyond what you have planned, what you are planning, what you uh parents live to or what the country average is. It's a very good thing and it's also a very noble goal. So if we can agree on one thing, uh you know, in the US and China is that we need to live longer and healthier. Maybe the next generation of us can be immortal sometimes. Um Anyway, uh so uh my race for Mr Yin is that for extending human life, how uh gene editing can play a role? Actually, back in 1990 the average life is VT is 30 years old. However, today it's already at 73 years old and in 1949 the life expectancy in China is 39 years old and now it's 81 years. So, and I have a good news for the women here. In any era, almost every female will have four or five years longer of life expectancy compared that with men. So I think extending life expectancy is very important and it relies on the nutrition, better medical conditions and the development of some drugs. All of that extend our life expectancy if we can intervene with tumor earlier, if we can treat the new degradation earlier, if we can treat Alzheimer's, I believe that all of you can extend your life expectancy to 90 or 100 years old. Uh However, you need to have saved enough money. Right? Because the last 10 years of human life is quite expensive. And actually we have established a uh we have a, we, we, we think, we think that now we can frozen the gene. Uh We don't talk about the ethics for now. I think we don't see any limits, maybe live for like 200 years old. That's possible. And actually, personally, I, I I'm very clear that I personally oppose personal immortality because if there is no death, every birth is a tragedy, right? It seems that the resources is quite limited. And second, I also talked about the immortality. I think that I also make a joke that the first immortal person might be physical, be killed by the second immortal person. So to some extent, we should pursue a kind of spiritual immortality, a kind of cultural immortality rather than the immortality of individuals. So that we can, we can say that we can save some resources for the next generation. Thank you for your discussion. Thank you. Thank you. Thank you.

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