Episode 6 - Genomic Futures: Health

Show notes

Speakers:

• Rob Scott, VP of Human Genetics at GSK;
• Soo Teo, Founding Chief Executive Officer and Chief Scientific Officer of Cancer Research Malaysia;
• Carl Anderson, Head of the Human Genetics Programme at the Wellcome Sanger Institute;
• Alejandra Medina Rivera, Principal investigator at the Regulatory Genomics and Bioinformatics laboratory in Mexico;
• Rachael Freathy, lecturer in Human Genetics at Exeter University;
• Gosia Trynka, Science Director at Open Targets;
• Alexandra Canet, science communicator and producer of the Genomics Futures podcasts;
• Zané Lombard, Academic Head of Human Genetics at the University of the Witwatersrand.

Episode description:

In the last installment of the Genomics Futures podcast, we interviewed the participants of the Understanding, Predicting and Altering Disease workshop. Featuring experts from academia and pharma, we delved into how genomics will underpin new discoveries that will directly affect patients.

We spoke with Professor Carl Anderson and with Professor Rachel Freathy. With Professor Soo Teo, we explored her views on how to implement genomics in healthcare systems in 2050. We then spoke with Dr Alejandra Medina Rivera and Professor Zane Lombard about universal tech and data access, data sovereignty and accessibility.

To close the podcast, we spoke with participants with expertise in pharma, Dr Rob Scott and Dr Gosia Trynka about how to shorten times for new treatments and what healthcare might look for patients in 2050.

Mentioned in the episode:

• Our Future Health – an ambitious collaboration between the public, charity and private sectors in the UK to build the UK’s largest health research programme
• IBD – Inflammatory Bowel Disease encompassing conditions such as Ulcerative Colitis and Crohn’s
• BRCA1/2 – BReast CAncer gene 1 and BReast CAncer gene 2, genes that when mutated significantly increase the risk of breast and ovarian cancers, among other cancers.
• Digital twin – a virtual replica of a biological entity or process. It can use real-time data to simulate, predict and optimise outcomes.
• Human Genome Project – a landmark 13-year international research effort (1990-2003) that successfully mapped and sequenced the entire human genetic blueprint.
• H3Africa – Human Heredity and Health in Africa, was formally launched in 2012 in Addis Ababa and has grown to include research projects across 32 countries, a pan-contintental bioinformatics network, and the first whole genome sequencing of many African ethnolinguistic groups.

Transcript

Gosia Trynka 00:00
I think that in 25 years, everybody that will be born will not only immediately have their genomes sequenced and available, but their healthcare will be monitored with different genomics tools.

Alexandra Canet 00:16
What will the future of genomics look like in 2050?

Zané Lombard 00:20
Well, I’m hoping it will move into a positive direction. I think we’re all hoping that genomics is going to have a positive impact on health and outcomes and really just empower our communities to have more choice in how they use this kind of genomic information. So I think, you know, if that was the outcome in 2050 that would be amazing.

Soo Teo 00:43
Currently in a low and medium resource setting, what is really a priority is to try and figure out how we might be able to use genomics more in preventing diseases, not just in dealing with crises when they occur.

Alexandra Canet 00:59
Welcome to the sixth Genomics Futures podcast. The main focus of this episode are the themes and visions that came out from the Genomics Future workshop titled ‘Understanding, Predicting and Altering Disease’. I’m Alexandra Canet, science communicator and producer of these podcasts, alongside my colleague, Olivia Allen, who is Head of Strategy at the Wellcome Sanger Institute. In this episode, we talked to the organisers of the workshop, Carl Anderson from the Wellcome Sanger Institute, and with Rachel Freathy from the University of Exeter. We also spoke about the different themes and visions that came up during the workshop, such as implementing genomics in healthcare systems. With Soo Teo from Cancer Research Malaysia. We also talked about shortening times to new treatments, with Rob Scott from GSK and Gosia Trynka from Open Targets, and about universal tech and data access, with Zané Lombard from the University of the Witwatersrand and Alejandra Medina Rivera from the National Autonomous University in Mexico. We start today with two members of the Organising Committee for this workshop, Professor Carl Anderson and Professor Rachel Freathy. What will genomics be able to achieve in 2050?

Rachael Freathy 02:14
So ideally, I think genomics is going to improve a number of things about our health. Thinking about 2050, I’m going to be 73 and may have grandchildren. And thinking about my parents, who are in their 70s at the moment, and kind of what’s important to them, having, kind of a healthy quality of life is important as you get older, various health difficulties come to impact your life more. So I think genomics, I would hope we would have a situation where things are more personalised. Everyone would have data on their genetic code which could be looked at in the context of what we know in terms of research and how lifestyle impacts health, so that we would have more targeted treatments and more targeted screening. So as well as being personalised, we may be able to predict a little bit more well, hopefully a lot more accurately, when, when diseases might start.

Carl Anderson 03:08
Well, I don’t what it says about human beings or us as people Rachael but I went to a similar space actually. I also thought about my age and how old I will be in 2050 so I’ll be 70. And actually, I was wondering about any potential grandchildren, the world that they would be coming into and actually expecting that during pregnancies, that actually the genetic testing that we have available for the unborn babies, the foetus to understand disease risk and allow people to make informed choices for their family, to help clinicians be better prepared for the birth of the children, and to make parents more aware of any issues and diseases disorders that might be affecting their their children, but then also the idea of having that genome sequence actually from birth, maybe even before birth. It’s the how much easier it will be for clinicians to predict and manage disease and ending these diagnostic odysseys that so many patients go on, the kind of trial and error approach to often finding which drugs are going to work for which person I think actually will be will be much better at doing that by 2050 and yeah, hopefully we’ll have generated the foundational data set to kind of underpin all that across a broad cross section of society and indeed, the globe, that the healthcare disparities that are perhaps in that we see today are greatly reduced in 25 years time.

Alexandra Canet 04:40
Fantastic, thank you. You were both part of the group that looked at the definition of disease in 2050. What are the main challenges about this theme?

Carl Anderson 04:50
Yeah, absolutely. So most diseases today are kind of defined based on a set of clinical symptoms, and there are multiple ways in which one thinks about aberrant underlying biology, underpinning those diseases. There are perhaps multiple different biological pathways that can be perturbed that ultimately lead to the same set of clinical symptoms. So for example, I work on inflammatory bowel disease. It’s a relatively common disease, affects about 1% of individuals in developed countries, and as the name suggests, the kind of clinical symptoms there are inflammation of the gastrointestinal tract. And there are two different types, Ulcerative Colitis and Crohn’s disease. And those two types are basically defined on the symptoms that one observes, the nature of the inflammation, the extent of the inflammation, the location of it. And there are different treatments, so some treatments work for both subtypes, other treatments just work for one. But those two diseases are basically defined on a set of visual cues that one can see during a colonoscopy. They’re not defined on the basis of the underlying molecular pathology. And so given that there are potentially many different biological pathways that will ultimately lead to the same disease presentation, that makes it a real problem when you come to diagnose a disease, because you don’t know when you come to treat it whether the person who’s in front of you has the disease because they’ve got aberrant biology and pathway one, not pathway two, or pathway three, or maybe a little bit of all of those. And so you can, you can give them a drug that might target one of those pathways, but actually, if the reason why they have that particular set of symptoms is because their disease is being driven by aberrant biology in a different pathway that drugs not going to work for them. So there is this kind of trial and error approach to finding out which drugs are going to work among which patients, because broadly, disease is defined by their phenotypic characteristics, the presentation of the patients. And what we want to do is move away from that, so that we start defining diseases based on the molecular pathology that underpins them.

Rachael Freathy 06:58
Yeah, I completely agree. Another thing I think genomics will do as we get more data and more people who are perhaps underrepresented in research, is actually begin to understand diseases that we just really don’t understand very well at the moment at all. So for a really nice example from recent years is nausea and vomiting in pregnancy, which is really common, and for some women, they have a very, very severe form called Hyperemesis Gravidarum, where they can often be hospitalised. It’s really quite a dangerous condition. This has been known about for hundreds of years, but only recently, due to genetic studies, have we begun to understand the molecular path that might be responsible for this? And we know now that there’s a protein produced by a hormone produced by the placenta in a pregnancy, so produced by the baby, that affects the mother, and it’s how sensitive the mother is to this that can influence how much nausea and vomiting there is in the pregnancy. So I really think that genomics over the next 25 years is going to help us understand that those are the diseases. And I guess the other point I was thinking about a colleague of mine who and is studying diabetes in Cameroon, and he’s describing a type of diabetes, which if a clinician in this country, saw patients who have a relatively common type of diabetes from Cameroon, they would say these people fit the type one diabetes profile that we that we know about. But actually, my colleagues find finding that this isn’t type one diabetes, it’s not an autoimmune condition. It’s actually different diabetes from anything we really know about, and it kind of just really illustrates how we’re only scratching the surface on in some populations of the diseases that are really important. And I think that genomics, as long as we can get good representation, is going to really help understanding things we just don’t understand yet.<

Carl Anderson 08:56
I think that’s such a good point. Rachel, we see something similar actually. IBD is classically thought of as a disease of Westernisation, so it’s much more prevalent in Europe and North America. But actually, as some of the world’s most kind of populous nations begin to “Westernise”, we find that there’s this tsunami of inflammatory bowel disease, which is on the way because as countries Westernise, they are in places that actually that they never previously saw inflammatory bowel disease. We now see it to a great extent. And there are even differences between in countries like China, for example, profound differences in the incidence and prevalence of IBD in the cities versus out in the country. And so actually, I think there’s a real opportunity to understand the biological basis of these diseases by looking at countries where they’re perhaps encountering these changes for the first time, and when we think a lot about gene environment interactions, because there is a genetic and genomic component to them, but there are also environmental components, and we’ve done quite well at understanding and getting a better resolution on the genetic underpinnings of these diseases, but how those genetic variants interact with the environment to go about having their effects and the separate, independent effects that the environment has. We’re really very poor at understanding and so actually, when we talk about understanding disease in 2050, one the things I’m more excited about, actually, is we’ll have so much better data on the environment.

Alexandra Canet 10:36
I’ve got a lot of questions of everything you’ve just said, which is brilliant. As a patient, I will come into the clinic, I will tell you about how I’m feeling. But how will this all look like?

Rachael Freathy 10:46
So I guess the situation will probably be, you know, similar to, you know, we have blood tests to measure for things now. We have scans and tests that you can undergo at the doctor or at the GP or at the hospital. And I guess that that kind of thing will still exist. But my guess is that there will be more detailed things that they can test from a blood test, there will be more detailed information that you can get from a scan image, especially with technologies like AI, which will help us to really analyse those images better. But I guess what I think is probably one of the major difference is that the information that you have at a particular consultation will be added to a kind of a live record of your data, including perhaps your genome sequence and your previous tests and information about your history and maybe information that you might feed in from apps on your lifestyle. I struggle to kind of imagine something really different, but I think I would really hope that the data integration and our understanding of that for somebody would be better.

Carl Anderson 11:59
So actually, it might be more preemptive than that. It might not be people going to the doctors when they experience symptoms. It might be people being sent to the doctors when phones detect, when technology detects, via the samples that we’ve contributed for genome sequencing and other assays, when they start to detect a pathway that they’re concerned about that’s what that gets you to the doctors, and it’s probably a separate conversation altogether about whether we’re still going to the doctors.

Alexandra Canet 12:30
Dr Soo Teo from Cancer Research Malaysia, was invited to the workshop. She was part of the group that looked at how to implement genomics in healthcare systems in 2050, not a small feat. This is what she had to say.

Soo Teo 12:45
Well, today, genomics is something that in many parts of Asia and many parts of low and middle income countries is not a reality. It’s a pipe dream that we look at in high resource research laboratories and within the duration of a project, but not really reality for the lives of patients who need treatment. And they’re also not a reality in healthcare systems that need a more effective way to use technology and the latest medicines. And I think in our pipe dream of what we’ll like it to be in 2050 is really to have it integrated into our services, not just in terms of giving patients the ability to access the therapies that are effective rather than a trial and error method, but also healthcare systems to be able to budget it accordingly so it’s fully integrated into the service. And I would say that currently in a low and medium resource setting, what is really a priority is to try and figure out how we might be able to use genomics more in preventing diseases, not just in dealing with crises when they occur, but, you know, the BRCA1 and BRCA2, genes for which the Wellcome Trust had a major role in identifying, was discovered in the 1990s right? But in many parts of the world, particularly in low and middle income settings, just routine genetic testing and then routine clinical management to prevent disease among those individuals that have a high risk of developing breast and ovarian cancer simply still doesn’t happen.

Alexandra Canet 14:29
So I’m a patient with cancer. I come into the clinic. What might be different in 2050?

Soo Teo 14:35
Well, in our ideal world, there will be fewer patients to begin with, right? So in other words for the individuals that were identified, where it may be a single gene or a small number of genes that was causing disease, then we may be able to identify those individuals even before they get the disease, and make sure that they have the access to the preventative measures. Today, it might be prophylactic surgery. But in the future, hopefully it’s more targeted medicines that at the molecular level, hit specific genes in a way that enable us to prevent disease with more options for patients. But it also means that, hopefully, by our understanding of the lifestyle factors that drive the development of disease in these individuals, we have a better understanding of which interventions, either dietary or physical activity or medicines, may actually help to prevent the disease before it occurs. Today. That’s not yet a reality, but I believe that in the timeframe that we’re talking about, I would sincerely hope that that will become a reality by 2050.

I think the number one challenge that we all identified within the group was is that healthcare today is already unaffordable, even in high income settings. So even in the biggest economies in the world, they are already struggling with how to integrate genomics into healthcare. I think this is where England and the UK have really led the charge with efforts such as Genomics England, Our Future Health and so on, being large scale studies that are not just research studies, but actually projects that have focused on integration of genomics into the healthcare system. But by far, I think this is the exception rather than the rule. So today, the challenge is already significant, because you don’t just require funding, you actually also need expertise. You need investment in infrastructure. You need a change in how we approach medicine. And I think my worry is that it takes a very courageous leader within the political system to look at long term healthcare transformation that changes how it is that we not only interact with the population, but also how we interact with industry. And industry is really important in order to be able to bring the funding in place to create new ways in which we can provide better health care for the population.

I think it’s, I think what struck me a lot about the workshop was we were divided into two groups, one group that was discussing the utopia situation. And the second is, what would the dystopia look like? And what struck me a lot was that the difference between the utopia and the dystopia was actually an incredibly fine line that we found very difficult to separate. So when it came to wearables, for example, do you want to have that data monitored? Or do you will having that data monitored really make your life absolutely miserable? And it seemed to us that it was really hard to see that difference. You know, if you were collecting the data, we felt that, yeah, sure, some central way in which you could collect that data and improve health will be fantastic, but if you had it implemented in a Big Brother fashion, so that you lost autonomy, that would be such a terrible situation. So I think what struck the group a lot is that there are, I think the gap between what is possible and what is ideal is going to be one that we will need to confront very carefully. But one thing I wanted to point out, is that today, genomics data is primarily coming from the European population, right, or individuals of European ethnicity, either from Europe or from North America, and there’s much less data coming out of Africa or Asia. And even in Asia, you know, the majority of data is really coming out from East Asia, which is China, North China, East China, Japan and South Korea. But it’s not really coming from Southeast Asia. And if you think about Southeast Asia, it’s a very, very diverse population. The population of Southeast Asia has got approximately 2000 recognised ethnicities, right? So the reality is that because so little is known about the genomics of these populations, there many people feel that, Oh, it’ll be ideal to have genomics made publicly available so that we can make medicines for all of these individuals, but there’s a lot of scepticism that any data that’s made available is going to benefit the high income countries that have greater access to technology, and that technological divide is going to be one in which it further disadvantages the individuals that have not been part of the genomics agenda thus far.

Alexandra Canet 19:50
Dr Alejandra Medina Rivera from the Autonomous University of Mexico, and Zané Lombard from the University of the Witwatersrand were also participants at the workshop. The theme they were working on was universal tech and data access. What are their views for the future, and what’s the role of representation and data sovereignty in this conversation?<

Zané Lombard 20:15
Well, I’m hoping it will move into a positive direction. I think we’re all hoping that genomics is going to have a positive impact on health and outcomes, empower our communities to have more choice in how they use genomic information and how they act on that, and how they use that to better their own health and outcomes. So I think you know, if that was the outcome in 2050 that would be amazing. I think, as far as kind of our jobs and what we’d be doing, I’m hoping that we would be very busy educating the next generation of scientists and clinicians that will be working in genomics. That there’d be lots of new work opportunities, especially in our community, where currently there is really a scarcity of opportunities and training for genomic scientists and genomic clinicians.

Alejandra Medina Rivera 21:09
There is a promise no like, and I think it has been, it has been going on longer than than just the next 25 years, but like this impact on health and personalised medicine, that treatments are going to be the best while not making you spending money on things that will work, while also avoiding secondary effects and and that’s a promise that hasn’t been complied. But we hope that in 25 years, we will be able to see this actually coming, and ideally that would be accessible as well in the Global South, which is sometimes difference is not like the technology doesn’t exist. It’s there. It’s just sometimes accessibility is the main issue. So my hope would be that then the accessibility is not a challenge anymore, that people can actually benefit from these technologies, and also the population can be the people that are doing the analysis, not just providing data for others to be used, for others to profit, not only in the economical side, but also to profit on the knowledge that can derive and how that that will probably be more applicable to their own populations.

Alexandra Canet 22:25
Fantastic. And you were both part of the group that was looking at universal technology and data access. I think this is a massive and really broad theme, but what are the main challenges that you see here?

Alejandra Medina Rivera 22:37
So, again, accessibility, I think that’s one of the main challenges. And then there are a lot of things to sort out, like governmental rulings and like even local taxes, like, it’s that something that could be a structural wall for some of that accessibility. And regarding data access, take this, produce and there’s always this nuance, data is as useful as it can be accessed to no so if people can access it, they can analyse it, and if they can analyse it, they can deliver knowledge and technology, and they can move us forward into this future that we are hoping to see one day. Nevertheless, that accessibility is also a risk, like this access to data can also be a risk of privacy and costs as well, like the insurance, like adding costs because you have a genetic marker or something for a disease. That’s something that is forbidden in some countries. That’s something that is not regulated. Like, I still live in a country that charges more women than male for insurance just because we live in a less woman friendly world, just because everything is designed for men. So and that’s the thing, like, if that’s not something this even regulated here, like the genetic part is not regulated either. So then access to that data can be a risk for our own populations. So there’s always this balance that can be easily broken, and then this hopeful future then looks with this topic.

Zané Lombard 24:21
Yeah, I’m going to pick up on that point, because I think it’s so important that Alejandra raised about so it’s not just about access. It’s about also not always playing catch up in the global south. So it’s also about making the tools and the resources available that enable you and empower you to use those things. So I think there’s one space where there’s an accessibility issue, but there’s also a space where there’s a human resource and maybe other resource scarceness as well that makes it difficult to keep up with trends etc. So I think that is some a big challenge that we need to think about is, how do you not only give access, but make sure that what is accessible is actually useful and can be used, and that the progress is almost equal as well, so that we’re not forever playing catch up in the Global South. We spoke a lot about whether there is a world where we have, like, an international global access policy, where fairness and equity is kind of at the front of everybody’s minds, and that’s the way that people share data with each other and use data. So I think that would be an amazing future to think about.

Alexandra Canet 25:42
So I think this, this is a really interesting topic, but just for our listeners, what would that look like in practice?

Zané Lombard 25:49
So I mean, I always think about the guidelines that are in place now that if you’re doing an exome or a genome sequence for a patient, that there’s a specific list of actionable variants that you’re obliged to report back to a patient so that they can use that information to make health decisions. And the reality in many of our setting is even if you feed that information back to the patient, they wouldn’t have the resources available to action those so are they really globally actionable variants then? if, if the person who receives the data can’t actually do anything about the information that they’ve received, where, if they were in a different healthcare setting, they might have options, surgical, medication, etc. So I think that is kind of what we want to work towards is making sure that not only do you have access because access to technology and to sequencing technology especially is becoming more and more available. So you know, I think that is not really the barrier that we’re struggling with. The barrier is whether you can interpret the data appropriately for the population that you’re working in, and the ethnic background of the community, and whether that information then is actually actionable based on what healthcare systems are in place.

Alexandra Canet 27:11
Okay, so what needs to happen today for those utopian visions to become a reality in 2050?

Alejandra Medina Rivera 27:18
I think investment and this has to be global, and that’s important. I have heard from people in decision making positions that maybe it’s not necessary to sample like that many people just like, really a sample just like to generate enough data. But I don’t think that’s correct, because it’s not like the global north will stop generating the amount of data that is being generated. So I do think that investment should occur and should be something, and this is a topic now. This is the idea of thinking that the different governments can come together and agree that this is investment that needs to occur in the Global South, where we need to generate this data, where we need to train the people to analyse the data, so that we keep some sovereignty on the data, and hopefully that will happen at some point.

Zané Lombard 28:17
Yes, I think with data sovereignty, I think that the issue here is, again, this, it’s not just about the data, it’s about access and resources to use the data. Because again, I think in a lot of low resource communities and settings and low resource settings that sometimes, you know, gathering the data, gathering those samples, doing the data analysis, generating data, and then analysing the data for kind of that first instance of what the purpose was, or what the funding was for, is a really hard thing to do in a resource constraint environment and then in a space where you have to share your data almost immediately upon generating it with the rest of the scientific community. Means that other people with better resources can do much more with that data than the ones who actually generated it in the first place. And I think that’s what data sovereignty talks to me about, it’s kind of, again, that unequalness of ability to use data to make discoveries and to build resources and build capacity in these low resource settings. So I think that’s important from a scientific point of view. But then I think the ethical point of view of making sure that communities are empowered to give informed consent, to really understand what they’re engaging in, and being culturally sensitive to differences in that kind of what you need to think about in research processes, process that the H3Africa consortium put in place. They’re data sharing access policy is based on a bit of a moratorium time to make sure that the communities that are engaged with this have first access to data. And a nice follow up to that data access is once the data is released into like a global database, there’s an access committee, and one of the first things that the committee looks at when there’s a request to use the data is if that group that it’s requesting data actually have an African research partner to make sure that that partnership is ongoing, and that kind of capacity building is an ongoing theme. I completely understand where the sense that maybe it is better for the patient and at the end of the day to make sure that the discovery is happening as fast as it could. But I think the other question that comes to mind for me is, which patients are going to benefit from this, and is it really a global benefit that we’re keeping in mind here when we take that fastest first approach.

Alexandra Canet 31:09
Another theme that came up during the workshop looked at how to shorten times for new treatments. Within that group, we could find Dr Rob Scott from pharmaceutical company GSK, and Dr Gosia Trynka from the public private partnership, Open Targets.

Gosia Trynka 31:27
25 years is a long and short period of time, right? It’s looking back at how much genetics and genomics have advanced over the 25 the past 25 years, you know, the advancements have been tremendous. I don’t think we ever anticipated that we would be here 25 years from now. So it is a hard question to think where we will be in 25 years. At the same time, it feels like it can be quite a short period of time, because when you think how much the technology has advanced and how much of it is implemented, for example, into a healthcare system, then I would say it’s been rather short period of time, because I would have loved to see a lot more of it implemented in the healthcare system. But having said that, I think that in the 25 years, the technologies will really accelerate unimaginably, and they will become routine in diagnostics and understanding molecular mechanisms of disease. The costs of genomic assays and tools will drop down very significantly, and it will become really affordable to implement it for everybody in a routine healthcare system. I think that in 25 years, everybody that will be born will not only immediately have the genomes sequenced and available, but they’re healthcare will be monitored with different genomics tools, and that can include very detailed, very high content measurements of the, for example, of the blood, and that will enable us to have a baseline understanding of individual’s health status, and enable us to monitor someone’s health and very rapidly pick up any kind of deviations and then very quickly respond to that.

Rob Scott 33:40
I spend my time working in the genetics and genomics of disease and how those insights can be applied to drug discovery and development, and I’m utterly convinced of the value that we can deliver from genetics and genomics to accelerate and improve our productivity in drug discovery and development. A bit like Gosia, as I reflect on this, 2050 feels like a long way away. Yet, 2003 and the completion of the Human Genome Project feels, even though it was almost 25 years ago, feels like yesterday in some ways, where at the time there was the promise that we were going to revolutionise the diagnosis, the prevention, the treatment of most, if not all, diseases. And I think we’d probably all agree that, well, whilst we’ve made huge advances, we’re not quite there yet. So I think through what are the changes I expect to see in 25 years? And one of the things that’s been highlighted is the inevitable explosion and scale of data. The areas where I see particular impacts coming through that ability to sequence at scale and link to detailed disease phenotypes that gives us inevitable improvements in how we think about selecting targets and how we might develop those target hypotheses into mechanisms, into understanding, as has been described with the explosion of genomic data, single cell spatial, transcriptomic data and disease, in understanding the mechanisms through which those potential targets impact disease. And then finally, when we’ve identified those targets and mechanisms, the patients and the diseases that might be most likely to benefit from those medicines, I’m full of optimism for where we’ll be in 2050.

Alexandra Canet 35:32
So you were both part of the group that looked at shortening the times to new treatment in 2050 and I recall a couple of challenges coming up about this theme. Can you tell us which they were? And some thoughts.

Rob Scott 35:46
Yeah, so good questions. So one of the biggest challenges we face in drug discovery and development is not only that it takes us a long time, but most of the things that we take into clinical development are likely to fail. That means that, because we know there’s a high risk of failure, we often spend a long time on preclinical preparation, validation of those hypotheses. So one of the things that I think improvement in scale and quality of genetic and genomic data offer is building our confidence in those targets that we think are most likely to represent attractive medicines for patients. Where we have really clear insight into the causality of a particular target, the directionality of the effect of that target, the magnitude of effect that it might offer, the mechanism through which it operates, we are then able to move at real pace to progress through preclinical validation and move with confidence into the clinic with the medicines that we’ve designed.

Gosia Trynka 36:45
I think we’re getting a lot better at target selection, where I think the challenge is, is the next step, what to do with that information. So I think we’re even getting a lot better at prioritising which genes in this, genomic loci are likely to be driving the phenotype, but the next step of what biological context is important to act on, I think that is one of the biggest challenges. And that is, I guess, coming back to Rob, what you said, and I think in the workshop, you refer to it as a confidence in causality. So building our confidence in causality and building therapeutic hypothesis, understanding how a gene modulated in one cell type, how does it affect the phenotype, and then when it’s modulated in a different cell type, how that affects the phenotype? And I think that building that picture of how a regulation of a gene across cell types, across organs, ultimately leads to disrupted function, and therefore gives us a hypothesis how to reverse that back. That is a big challenge, and it’s also an opportunity, because the tools that we’re getting at our hands are increasingly scalable and capable of helping us systematically build pictures of what genes do across cell types, across different contexts, how perturbing them in one direction or the other? How is that impacting cellular functions, and therefore enables us to think about therapeutic hypothesis and drug development and improve the time from discovery to bringing drugs to patients to the clinic.

Rob Scott 38:43
One of the things that’s often cited in our community is the fact that targets with human genetic evidence are about twice as likely to succeed in clinical development as those without evidence. Fact, increasingly, data are showing that highest confidence genetic evidence gives a two, three, often, four fold increase in the likelihood of success. One of the things that’s under-appreciated is another factor that increases the value of having genetic evidence is the similarity of the genetically associated phenotype and how that relates to the particular disease for which a medication is approved. And it’s really clear that the higher the similarity of a genetic association studies to the diseases that we seek to treat in patients, the more value that genetic evidence has. So I think it’s really clear that many of the studies we’ve done at large scale are limited by the depth and the quality of the phenotypes that are studied, and a huge push in the field has to be to increase the depth of precision and the quality of a disease phenotyping be that in detailed clinical records from primary or secondary care, and the challenges that we’ve seen in linking those to our genetic studies, be that in longitudinal understanding, not only of individuals who are susceptible to disease and disease onset or disease presence, yes versus no, but in studying disease prognosis and understanding amongst individuals diagnosed with disease who are those who are more likely to progress versus those who are more likely to remain stable. That’s a model that more closely relates to a therapeutic paradigm. So I think seeing improvements in the scale, the quality of our phenotyping is going to bring yet more value to how we apply genomics to drug discovery and development.

Alexandra Canet 40:33
Brilliant. During the workshop, some thoughts around disease and diagnosis, looked at potentially simplifying clinical trials by testing mechanisms rather than conditions. Can you tell us what it means and what it would look like?

Rob Scott 40:48
So thinking about changing clinical trials to study mechanisms rather than diseases, ultimately, we are going to need to demonstrate impact of a medicine on how a patient feels, functions or survives. But it’s really important during that drug discovery and development process to understand where we can invest our resources most aggressively or with most conviction onto the targets, the medicines that we think are most likely to deliver value for patients. And one of the areas that I see as a real opportunity is to think about what are the early experimental medicine studies we might do to test the impact of a medicine on a specific mechanism. So you might think of that as a particular proteomic profile, a particular transcriptomic profile that we might be able to measure, such that we can look very early and very quickly at whether or not our particular medicine is shifting that particular mechanistic profile that we’re measuring from proteins, transcripts or another tool. We can do that earlier with smaller numbers of patients and get quicker readouts, such that we can then move with more conviction to those later stage, larger studies looking at the impact and how a patient feels, functions or survives.

Gosia Trynka 42:10
So one maybe I’ll add here, one thing that comes to mind in the workshop we were thinking about how to get there quicker, and one of the proposals that we came up with was, or we envisioned that could happen, would be this kind of hybrid digital twin, so everybody would have they twin that’s digital. There’s some model that captures our health and our genetics and has built into that model all the biomarkers that we can monitor longitudinally, and therefore, with something goes wrong, and there’s a need for potential intervention. This digital twin, we can use it to model the responses and the effects of treatment and the digital twin could also be coupled with some of our cellular material that one could use in order to then test the hypothesis more quickly, in a more bespoke way to develop and apply the best therapies. So all of that then becomes rather than it becomes informed in biology and in mechanisms, and I think that was kind of a conceptual shift on how we were thinking about how this could change in The next 25 years.

Alexandra Canet 43:41
Thank you for listening to the Genomics Futures podcasts. If you have followed the series in order, this is the last of the episodes. If you haven’t listened to them all or are still missing one, do find us wherever you listen to your podcasts under Genomics Futures. It’s been such an exciting journey to get here and an absolute privilege to produce this set of podcasts. Thank you to everyone who has been a part of it and that have given their time so generously to reflect on the future of genomics. If you want to get in contact, please do you might agree, disagree or have your own thoughts. We’d love to hear them. You’ll get in touch with us at genomicsfutures@sanger.ac.uk. Thank you for listening.