#437: The clinical trial system is broken and here’s why with Dr Elsa Zekeng from SökerData

18:47Dr. Elsa Zekeng:final year of my phd i was like okay i should probably sit down and actually do the finish like let's actually do this thing that we started yeah and so off the back of that we then closed it as a company and then it became a student yes a student organization yeah and yeah part of the union exactly and it's yeah still growing still running I've been back I was back last you know maybe two years ago to celebrate the 10th anniversary day gave us this little plaque like pioneers I was like wow to think that it still exists so yeah wow that's that must be so

19:22James:satisfying again to like go back and actually see the thing still running absolutely and seeing all

19:27Dr. Elsa Zekeng:the students coming in and just how much innovation has moved on right and all the companies that are coming in and there's so much stuff around the physics aspect of it and like lasers using lasers to do so many cool things and I was like goodness me when I started that's not what we're thinking but these is what these are the companies that are in the area so yeah yeah so where did that entrepreneurial thread then take you where did it take me next well that was basically the pause at that moment from that and I think I picked back up with my scientific lens which was deploying to Guinea during the biggest Ebola outbreak in West Africa which again was virtue of the fact that my lab or my PI's lab was a respiratory lab and so when the Ebola outbreak hit in West Africa the WHO and Public Health England reached out to him to ensure that we well that the how he could support with all the research we had been doing and so I volunteered to deploy and those were probably some of the initial thoughts I started seeing around clinical trials I was seeing clinical trials on ground and yeah lots of things that I did not particularly agree with that's a whole other podcast in itself I can go into so then that was the next if I yeah probably milestone that put a dot the pin in my head around data clinical trials and all of this then I graduated in 2019 COVID hits 2020 and you would imagine with a PhD in infectious diseases everyone is asking your opinion around COVID I start working with the UK government and a couple of companies on COVID-19 vaccine uptake in black and brown communities or maybe the lack thereof and then I started asking the questions around clinical trial makeup and having an understanding what that looks like at the time I checked AstraZeneca and I think at the time it was like three percent makeup from black and brown communities and I remember thinking I am not going to be the one to engage and talk about this with communities because I was receiving so much backlash at least it felt like I was receiving a lot of backlash and so again put a pin in that and then I went into strategy consulting and I worked at LK Consulting, Guidehouse which is Navigant and through those and that was really where I started seeing the commercialization of what is now in soccer data right which again is understanding the pain point because obviously during COVID everyone's trying to understand what's happening.

22:02Dr. Elsa Zekeng:Pharma is developing these vaccines trying to get them to market and FDA were putting regulations around access and clinical trial makeup and that was the first time that I saw pharma companies sort of change the type of projects that I saw coming in um having an understanding of okay what is health equity how do we increase data sets from women and global majority populations and things like that and that's when I thought okay I've seen this problem from different lenses right so through my PhD I realized I didn't really delve too much in what I did with my PhD but through my PhD I saw that disparity in the data sets in communities when engaging communities I saw how that affected uptake and then of course from a pharma perspective I saw the commercialization opportunity and then I thought okay let's figure out what soccer data would be so yeah wow what a story

22:53James:um the clinical trials so your interest in clinical trials seems to have sparked uh on the ground when you're dealing with ebola which is interesting you you then also talked about well later you talk about then the backlash from black and brown communities and the poor uptake and you know lack of trust in those groups with health care services obviously playing a massive part in that um can you just talk me through i know you don't want to share too much on this and you said it's a whole nother podcast but what was it that you didn't agree with from a clinical trials perspective in the ebola crisis i didn't agree with the inclusion

23:40Dr. Elsa Zekeng:exclusion criteria right and how it was being decided and who was deciding um the inclusion exclusion criteria right essentially what was clear was that there were clinical trials that were being run on ground which you know fine um i say this as someone who on our team we were the first people to test out the minion i don't know if one anyone knows about the oxford nanopore technologies and the portable minion where you can actually sequence um genetic data on the go So we were one of the first teams to do that in an outbreak setting. So this is 2015. And have an understanding of what that would look like and getting all these long reads and directly translating that with public health experts to have an understanding of how you could trace back, right, and track where a specific virus was coming from and what tree that was coming from.

24:37Dr. Elsa Zekeng:And of course, how do we isolate those populations? So it was like the best case in real life technology, how we're using technology and innovation to directly affect and stop an outbreak so there was a lot of that going on so I'm not going to say that you know technology cannot be deployed um in any in a pandemic setting but we're going to touch on it that's where regulatory comes in there are lots of things around regulatory and probably why my interest again lays in a lot of the work that happens in Africa is again that regulatory by it so there were clinical trials that were being run and what was clear was that the feeling on ground was that there was one specific clinical trial that was having a higher success rate yeah and then when i say success rate in terms of recovery and in terms of actually

25:23James:moving out yeah and the one that was not yeah and i still clearly remember my 22 years old self

25:28Dr. Elsa Zekeng:asking so why are we still enrolling patients and the one that we can see that is not working do we have the paperwork for it and I essentially was told to just go back to my to my day job and focus on that and so and I there were lots of things like this that was happening I still remember someone coming to our office and by the way to paint the picture when I say office picture a wooden tent that has been built up you know in the middle of a pandemic so it's not really an office it's everyone sort of knew i mean it was an office but it wasn't they think you know brick walls etc think middle of nowhere put in wood to be able to create spaces so um yeah we were testing for ebola and malaria in our lab so we i went under the european mobile laboratory with the who and so we were getting literally firsthand all the samples that were coming in um for either ebola or malaria and so we of course had to be as careful as you could possibly imagine and whether cl well ebola cl4 right but we were working on it in a created cl4 which was like a box i think i posted one of these pictures on linkedin um and so we're i remember still sitting in our phone room and testing away and someone coming in and asking about some samples and some clinical trials that were ongoing.

26:58Dr. Elsa Zekeng:And I still remember our team leader at the time going, yeah, no, we're not doing that and just shut it down. Right, exactly. So there were lots of things that were happening on the ground and those were the things that sort of started making me think, okay, hang on a minute, what is going on?

27:16James:Is there, would they have argued there was an ethical justification for what they were doing? Would they have argued that?

27:24Dr. Elsa Zekeng:probably I think what I did not do was actually go and find out which clinical trials or which because we could not even because at the end of the day was all blinded right so from a patient perspective everything was blinded no one knew what we're getting enrolled over but of course I was staff so there were questions that I could ask and again I was the only French speaking person on the team again the only African on the team but I only say that to say that what alluded or what that gave me access to was gaining a certain level of trust and understanding within the community that other people may not have had, right?

27:59Dr. Elsa Zekeng:And so there were certain questions that I could ask. And this is how I knew that there were certain trials that were ongoing, that were working versus not working. And I did not double click on some of them. So to date, I can't even backtrack and understand. So I'm sure from an endpoint perspective, and this is where we start going into data sets right so from an endpoint perspective when you look at inclusion exclusion criteria you could probably say yes technically they fulfilled um the criteria to enroll these patients because maybe if they were specifically passed a unable um a threshold um they were right to be enrolled into a specific clinical trial but then again as we all know these data sets start from somewhere so what were the data sets that they started with and why were they not working within some populations versus others these are questions that i i don't know i don't

28:55James:have the answers to it's funny because before we started recording we started talking about um how you know it's not data itself that ends up being the interesting part of the conversation it's actually that it's it's the consequences it's the consequences of how it's collected it's the consequences of how it's managed it's the consequences of the storytelling around it i think that there's no more obvious example than this of how the handling of that data has actually just sparked this incredible passion in you to right some wrongs essentially of how the world is working and i think it's not it's not unrelated is it that the next thing that you talked about was the mistrust from the black and brown community with healthcare data.

29:46James:And the backlash you're gonna receive, even from your coming from a position of everyone needs the vaccine here. But I imagine you also have a great deal of understanding as to why the lack of trust is there. Because we all, I say we all, people might not know the studies that have gone on previously. you know infecting black populations with disease on purpose to study certain things how blood has been frankly stolen and commercialized from black bodies that are able to fight off certain things and people don't might not often realize that those are the origins of that and so those things are indeed connected and I can completely understand why you have a huge interest in this and so soccer data can you explain what problem it is you're solving specifically and how you are

30:48Dr. Elsa Zekeng:doing it absolutely um thanks James so what I came down to was having an understanding of where the data sets sit so access to data sets and then the use of the data sets right for these set consequences and i say that because a lot of times when i've talked about um why the problem exists so we go back to my phd so we've talked about um the ebola epidemic in guinea we go back to my phd and we're saying that this hypothesis is we're due another pandemic and so we need to be able to find out specific biomarkers that could be used to triage a specific soft population great we get data sets and samples from great ormond street um aldehy hospital and i'm like great we have these samples we're going to run proteomics genomics so understanding of the proteins understanding of the genes and actually connect them to clinical data and start seeing what biomarker profiles are coming out i'm like great we're going to do this analysis and i then turn around to my um supervisor and my PI at a time and I went this is great but like would this work for someone like me and he was like oh good question so but we need data for to be able to add to this cohort to be able to understand if your profile will be the same or different where do we get that data set from and then again because I am the curious nerdy kid that I am the summer before Before I had volunteered at the Institut Pasteur in Dakar, Senegal, which is a huge site for flu monitoring in West Africa.

32:27James:Wow.

32:27Dr. Elsa Zekeng:I reached out to the previous supervisor and I was like, I need data sets from the geographical region, specifically from Senegal. And yes, people that look like me, can I have data? Can I have samples? Can you collaborate? And he was like, OK, what do we need to put in place? And so that was how I ended up including data sets specifically from a Senegalese population or from people within that area. Because again, Institute Basel-Dakar doesn't only do Senegal, it does lots of like West Africa. And then having an understanding. And when we ended up pulling those data sets together, there were 56 % from a Black ethnicity.

33:03Dr. Elsa Zekeng:And we found specific biomarkers that had not been found before when it comes to flu. When we analyzed them according to ethnicity and gender. Yes.

33:12James:and then i went i mean it's not surprising though is it like i'm looking surprised but it's not

33:17Dr. Elsa Zekeng:surprising it's not but then it's like you know something theoretically and then you see yeah

33:22James:this is yeah cognitive you know that should be the case why am i surprised yeah but then you're

33:27Dr. Elsa Zekeng:like oh yeah there's a real issue here if this is what we're finding so we found 11 proteins that was significantly different could be potential biomarkers i mean as far as phd's go that was the end of that. But to tie back into the problem we're solving, the problem then became, okay, lots of people are saying that they want access to these datasets and they want to include these cohorts, but they don't know where to access these datasets from. So I thought, okay, fine, we're going to aggregate these datasets, we're going to source these datasets, pull them under one house, but more so we're going to analyze them in context.

34:00Dr. Elsa Zekeng:And why is that important? Because what could be an anomaly within one dataset could be an absolute trend in another dataset. It's just depends on which data set that specific data point sits in. And so the problem is that CircaData is solving is one, the access to data, to the understanding of data, and then of course, the use of the data set. So what that just means is we are aggregating data sets specifically from focusing on women and global majority populations. And then we're looking at starting with breast cancer cardiovascular diseases um we're looking we're going to look at some metabolic diseases and in the future and we're really trying to access a range of both publicly available data sets but also proprietary data sets so that means lots of partnerships partnerships with hospitals partnerships at a national level partnerships with charities um and just being able to say okay the question of i don't know where to access that data we've got it here it is so there's no longer of the excuse or not just excuse the the rebuttal of I just don't have access to that data I can't include it it's like well here you go uh now if you don't include it that's a whole that's a choice that you've made rather than um I just could not find it so yeah that is fascinating so who then who exactly is the customer for you great question so when we started my icp in my head was a r &d teams which was r &d teams were developing specifically a drug who are about to go into clinical trial this is pre-phase one pre um going into humans and they're trying to have an understanding of will this drug work within the cell population or not right and right using these cohorts to have an understanding you know basically making risk um decisions go yes yes

35:53James:Yes, yes.

35:54Dr. Elsa Zekeng:So I say that that was what it was at the beginning because as with businesses, the commercialization of that, I found that slightly more lengthy slash the problem was still hypothetical because you're still future planning a problem, right? And of course, from a commercial perspective, you're looking at someone who has a problem today that needs to be solved right now, right? And so when I did some more commercialization conversations, I realized that those who actually had the biggest problem were pharma companies who had a drug that was already past clinical trial phase three, for example. I was maybe about to come to market, but they did not.

36:31Dr. Elsa Zekeng:They had missed out on some data sets that could really, they needed it either for regulatory purposes or they needed those data sets to be able to have an understanding of how to market and position the drug. there we started seeing a huge uptake and that was all the impact by the way not because they

36:47James:want the drug to be better but in order to get over a hurdle to commercialize it so i'm just going to draw attention to that for a second but um yeah we can we can complain about the moral stuff maybe a bit later it's in vain isn't it like anyway the key is obviously matching the incentives to the morality if we do that then at least which they are here i guess is the positive

37:09Dr. Elsa Zekeng:thing to say exactly and that's what we're trying to do at the moment just matching doing that matching process but i mean i say trying to do we've been for how old we are we've been pretty successful in being able to engage the right pharma companies and um who are willing to go through the processes to include these specific data sets and so we're basically at the real world evidence teams is where i would put us in so real world evidence teams and then of course now with the uptake of ai you know we're not seeing med tech coming in having interest and trying to understand okay how do we even train our ais how do we train that but the conversation around ai today was not where it was two years ago at least for me right so we're obviously evolving with with that conversation and making sure that we can continuously serve these um icps how long have been running soccer data goodness me um in may will be three years nice yeah that's amazing

38:02James:three years three years that's awesome um and why soccer data i've tried googling soccer or circa as it looks like if you yeah if you're the umalauts from german i guess over the o but well that's over you isn't it german but anyway yeah can't seem to fight can't seem to find the answer because i was thinking oh is there is there a historical figure called this

38:24Dr. Elsa Zekeng:that or something but i yeah i don't know no so the inspiration behind that was just i was trying to find and something that meant to seek because what we're doing is seeking data right and so i found i looked for the names in like latin and greek you know all like some of these other names and there were two things that were driving my decision making one i didn't want like a dot tech slash dot ai type of name and it was like no and then the other point was i wanted to ensure that yes today would be to be if we ever went b to c it was something that could still take right that could still be used in like everyday language and so my previous flatmate is um Norwegian and so she was like oh have you checked in Norwegian I was like of course I have not like it's not the language that I would start with uh but I did and then it was available and so soccer is Swedish to actually mean so Norwegian and Swedish are quite similar from what I understand um means to seek so it means the soccer data means to seek data lovely lovely um can you talk me

39:34James:through an example of maybe an insight or a or a biomarker or something along those lines that's something that has been some some practical what has reached clinic here like how do those insights actually how do those insights actually reach the patients and make meaningful change have you got an example of how your product i guess has been used in that way oh good question um i wouldn't

40:04Dr. Elsa Zekeng:say we have an example of how something has reached um in clinic right because again you know the clinical trial process in itself is like 10 years long so even if anyone's three years old exactly we're only three years old so even if anyone wants to use our data sets you know last year i wouldn't find out so probably about 10 years in but what we're directly seeing at the moment is how is directly supporting patient engagement how is directly elevating um patient experience how is directly allowing pharma companies to understand what and where some pain points exist specifically for subpopulations so a case of current client that we're working with they're working in the stroke space and they're specifically wanting to use our data sets for women and ethnic minorities so the we're really focusing on the stroke outcomes from women and latinx and african-american because we're the study specifically focused in the u.s and this again is to have an understanding of what exactly what is their patient preference for specific medications are the adverse events that have happened or occurred and previous drugs I haven't taken, you know, and things like that.

41:18Dr. Elsa Zekeng:And so that's directly informing if a drug can be prescribed or should be prescribed to a specific subset of the population or not, or allowing them to think through that whole process. So that's sort of where we're seeing that at the moment in terms of direct patient output. Then the flip side of that is that we're currently running a study in Nigeria. and what we're seeing the interesting part is from a clinician's perspective so we're seeing clinicians actually say when the that study specifically focused on breast cancer so when they're going from a diagnostic perspective they're seeing differences in let's say biomarkers versus treatment responses so let's say treatment pathways perhaps if a patient tests positive for a BRCA1 biomarker for example there's a specific treatment pathway that they have to follow and what they're seeing from using our data sets is that there's some patients that are yes following that treatment pathway and that response and that outcome and there's some patients that are completely deviating from that and so exactly so from that perspective the question i just said exactly because of james is um not anyone who's listening i'm not watching and so what that will be interesting to see is that if we were to further subtype those patients that are not following that treatment pathway are there further mutations there that could probably and suggest that they should be on a different treatment pathway and then actually not potentially just BRCA1 right what is this the further subtype that we can see from there so at the moment that's sort of where we're seeing that in that direct application um and i think lastly is just having from a patient perspective just being able to have an understanding of adverse events a question that i get all the time is is this typical in people that look like me like what are other people saying around that like if someone took this treatment is this should i be experiencing this and so yeah it helps build trust doesn't it i

43:22James:I think that's the other thing. That's sort of the business that you're in almost. It's funny, I speak to Malone quite a lot who wrote the Mind the Gap book, the dermatology book. And I still honestly find it wild that it took until, what was it, 2021 for that book to be written. It's frankly unbelievable. And just back to this thing about, you know, the lack of trust between those populations and the healthcare system. There's a long way to go. is a really really long way to go and it is incredibly important work that you're doing on many levels i think yes there is the commercial viability of the model and thankfully yes those incentives are aligned um you know if you want something to change change the change the exam change the measurements and then people end up learning it or doing it right so it's always that That's the thing we need to focus on.

44:20James:And that's why policy is important, by the way. It's why like lobbying is important for change at that kind of level. Because if you change the tick boxes, different boxes have to get ticked. It doesn't matter why. I don't care about the morality. If at the end of the day, you still have to tick the box. Let's just get the right boxes on the page that need to be ticked. And that's why I think that policy work is still incredibly important, if slow moving, but just important to change. So it's unbelievably important work. And because you're such an expert in this i'm wondering what you think about where we're at in the uk and how far we have to go on that because obviously there's gonna there's a long journey here and this isn't going to be changed overnight this data gap is talked about in women's health a lot it's talked about in minority groups a lot that there there is such a long way to go where do you think we are as a community as a country maybe in how we're set up to address that gap and what if anything do you think needs to be done further from a cultural perspective we clearly understand

45:36Dr. Elsa Zekeng:the problem and we know the problem and i say this actually i should probably say this cautiously because again i know that my news feed on my linkedin is obviously very clearly biased because of the work that i do and so even a lot of the events that i go to still understand that lots of people in there are still early adopters right even within the whole uk ecosystem so but okay if we say generally i think we know or understand the problem now where in my opinion we're falling short is very much on the implementation of it so i think we're talking earlier on we give a couple of examples one is access to data right we take just access to data sets within the uk as it stands today um the uk is known as a science super house and it's like yep great we have access we have data sets we have access to it and lots of people talk about genomics england the uk biobank our future health and cprd and quite a few other um sources to have access to data sets and on the

46:37James:face of it by the way fantastic news coming out of those places fantastic news coming out of those places we've reported it on here we've reported it in health tech pigeon just saying you know all this data exists uk is going to be this amazing hub for data like yeah i'm with i'm

46:52Dr. Elsa Zekeng:with you so far exactly on the face of it now you double click or you dig further access to data sets specifically from the likes of genomics england probably takes about two to six months that's number one and that is from an application process to actually having access to data sets and again, they're all within security data environments. When you look at some of these institutions that I have mentioned, there is none that you can have access to, let's say next week, right? Like the availability of these data sets are not as instant as the headlines may make it appear. So you're looking at 12, at least 12 weeks up until six, heck, probably 18 months, right?

47:33Dr. Elsa Zekeng:To actually have access to some of these data sets. so what does that do for anyone who's innovating in the uk landscape that is a massive blocker to be able to have access to it i was at a women's in health care event last night and someone and some women said that they had to go to the us for example to some all of us research data to be able to have access to data sets to train some of their models for two reasons one the the speed and efficiency to be able to have access to these data sets which look they were looking at two to four weeks turnaround time to have access to these data sets in comparison to six to nine like that is huge that's a difference between a business surviving um startup definitely received funding exactly i mean and then um two was the makeup of this data set and i think that's where we go the second part of the uk challenge yes we have access to these data sets but who is represented in these data sets and i think that that's something that absolutely everyone needs to ask um data sets within genomics england within the uk biobank are still very much i think less than 10 percent from black and ethnic minority populations and we even look at it from a gender perspective still significantly lower so yes you may have access to these data sets but what are those data sets what are the data sets you're actually having access to but then you look at it even further down the line what can you do with these data sets right which we're talking about which the consequence of these data sets, a lot of them, the IP still is probably co-owned with these institutions, open source, having to report these insights back within with these institutions, which I know others may argue that, yes, you have to keep maintain that open source ability and have everyone access to it.

49:20Dr. Elsa Zekeng:Great. But then from a commercial perspective, how do we then actually use that to commercialize actual innovation that can be that can stand within the global market and I think that there's some specific key points where we're currently failing as a country

49:39James:What is the answer to that? How can you then use it to commercialize?

49:42Dr. Elsa Zekeng:I don't know that you I don't know that you can use it to commercialize but I think you can use it to start training a model from a research perspective and then build on top of that so perhaps you can look at it as a foundational model so you can use it look at it as a start um to allow you to develop something that can hopefully commercial you can add on other data sets but i think what it keeps coming back to is that that data set is not going to be enough for you to commercialize it will be enough probably for you to build that first iteration but you will need to find other data sets to be able to commercialize and what would

50:21James:need to change so so is this a mindset thing but so i'm thinking now about how you know you see all this stuff that's that's saying oh the nhs has got so much data in it like once we get to the stage where we can sandbox all this data and everyone can use it i'm going to build so much stuff and so is what you're saying that there's actually just a fundamental approach to the way that we deal with data currently that actually that isn't as true as we would like it to be because i'll be honest but when when something sounds too good to be true it often is and so when people are like oh don't worry every single bit of nhs data is just going to be able to be in this central place and secure data environment everyone's going to go in there and be able to deal with it and train models and i should it is is that is there just a philosophy that we have on data currently that

51:11Dr. Elsa Zekeng:would need to change i think it's beyond the philosophy of data i think it's just how generally the uk mindset operates on innovation because if you start at the beginning which is what is the end goal right what are we solving for yes we're solving for hopefully commercialization because that's the only way we maintain some sort of economic you know power but also yes we're solving for research and advancing research if we stay at just advancing research which yes we are excellent ad within the uk we can't spit on that then okay fine as we are then we're great to keep on going but until we can flip that mindset and take it a step further which i think is a general uk problem um within the entrepreneurial ecosystem and think no actually we're not just solving for research we're solving for commercialization to own a position and to really take that position in terms of scientific innovation until we can switch that mindset then we then start trickling down to okay so what does that mean for the rules and regulations we're putting out around um access to data around policies around what people can do around data sets right so as we were saying fda at the moment has changed a lot of their laws where you can a lot of things are some ai can actually end up in the market with little or minimal um review processes yeah you know we have have lots of thoughts around that but i think i think there are some positives there are some negatives sometimes maybe good sometimes i did sure right but i think that what that signal that clearly sends is that we are here for innovation first yeah i i hear you wrongly we're here for innovation first rightly or wrongly yeah and we will fix whatever problems that land on the market later on or it will end up in being someone else's problem right and again that's the u.s stance at least like that's the stance they've taken and they're clearly reflect that's clearly reflected in their policies so is that what is maintaining them in being the house that you know the power innovation house that context we can argue whether the u.s still is the economic powerhouse that it thinks it is versus not but at least it is the stance that they're currently taking and i think that from a uk perspective um we keep dancing around the same issue and i don't think that that's just a uk perspective i think that's a europe perspective and a european mindset um i was at um festival of politics in november festival of politics is run by the european commission they do it every year um bringing together european top leaders politicians um heads of states and the conversation last year because it holds in november every year was you had the one of the co-founders of mistral can't remember his name on the top of of my mind mistral ai and he was specifically talking about we can say what we want to say about the us but it's pushing europe to think about owning our own um ai infrastructure owning our own um defense security things like that a lot of the data sets that we're using currently to understand and track Ukraine, for example, was coming from US powerhouses and the conversation around the tables.

54:30Dr. Elsa Zekeng:Okay, what if the current US president or some of his counterparts decided to just turn the switch off? What would that look like for Europe? And from what I am seeing, some of the comments coming out of Davos is still similar to that, that it's pushing us to own our own infrastructure, our own policies around AI. And I think that that is as true for the UK as it is for Europe, right? Until we're able to own this data set and think about how we commercialize it, we will just end up being a great research powerhouse that lots of entrepreneurs leave at some point.

55:06James:Yes, because we have to get, we had Harvinder on the HealthTip Vision podcast talking about this, that we have to consider if the UK, what part of this conversation we actually want to be in, because we're not going to win on the processing power. like we're just not we're not gonna nvidia's not a uk company like i mean open ai is not and google's not and like all these we're just not we're just not going to compete there and so actually where can we compete particularly in healthcare of course we we do have the nhs and we can create data there so can can we be the data provider to a lot of this and actually is that is is that where we fit question mark then open ai comes out and just says ah just just just put your data in here voluntarily uk guys like although it's not available in the uk's i think i vpn'd it to get on the way waiting list from the us wow just just to get on the just to get on the list and i've looked at it but um yeah it's it's it's super super interesting i mean do you you clearly feel uh because there's a lot there's a lot in here about you clearly feeling this viscerally like it's all it's almost like you're you're caught you're being held back like almost you can set you can sense it in in the frustration in the way that you're speaking i guess um when you when you can move to china like when's that when's that when's that coming

56:26Dr. Elsa Zekeng:that's a that's a thought that's a question i was having a conversation i was having in the first year and i know that i'll move to china but the question very much is how do we bring that in I think the frustration right comes from a lot of places a couple of them being one I've been in this industry for the past easily since I was 22 it literally is and that is over 10 years now and then almost 10 years now and again within being in the yes being in science even longer than that right I started learning about precision medicine over 10 years ago so it's having seen the potential of it and I think that that's always a frustration and then seeing not seeing it being realized and I think that those are the key parts to play here and I've always sat at the intersection of entrepreneurship science and policy making um because I again I think they all affect each other and we have to continuously at least I have to continuously switch hats because one policy could literally tank my business and or could make my business you know it's constantly thinking about that and being being up to date with that so it's i think

57:41James:the other thing the other thing is also is that we talk about the consequences as well the consequences of all that is is a level of injustice that is quite difficult to swallow as well i think that for me is why this grinds my gears is that i cut like that i struggle i historically whenever i see injustice i massively overreact and create 10 times the amount of problems myself um and not even injustice towards myself it's generally injustice towards others and i just get involved in things i shouldn't and so actually this is it's one of those things for me personally that i think i see here is that the consequence of this inertia let's call it is a level of injustice that doesn't justify keep maintaining that inertia um i think what's interesting is that there is open still the call for evidence on the regulation of ai from the mhra i don't know if you've have you been involved in this are you submitting to this um what's your relationship with this call for

58:53Dr. Elsa Zekeng:evidence i've not been involved in the creation of it but definitely putting together um a submission and evidence for it i mean the deadline is the 2nd of feb so it's on my to-do list before the end of the week um and the points that i'm essentially trying to pull through are of course one specific access to data but again specifically our speed to to market and efficiency i was just trying to find specific numbers just why we again you may have seen as you're listening you wouldn't know i was scrolling um but one of the key a point that i well an article that i read um two days ago probably was around clinical trials within the uk right so it takes on average 186 days to initiate clinical trial in the uk 186 days to initiate to initiate to initiate a clinical trial in the uk yeah now compare that to some of our neighbors germany it takes 92 days half half italy france okay 120 141 140 days right okay so if you compare us to italy one could say okay we're not so far off you compare us to germany we're literally double the time now what does that mean what that means is that for a pharmaceutical company that actually wants to initiate a study that could actually potentially benefit trials and populations in the uk yeah would they want to spend 186 days trying to negotiate six months trying to negotiate launching a trial here than going to germany that's going to take them 90 days to figure that out so when we're talking about the cost of this as we were talking about the inertia and the lack of and the speed to market it literally is directly affecting patients um lack of access to specific uh potential life-saving medications and treatment options so that's definitely one of the things that i would like to bring in on into that call for evidence in terms of if we don't we're seeing exactly the same thing that has played out in clinical trials it's just going to play out again in this you know in this ai race right but what is even more what was also interesting within this specific article was looking at which again i can send it to you you can link it if anyone wants to read it is um one of the reports was that research is still viewed as a nice to have in the nhs and so if it's a nice to have when budgets go and we all know the cost cuts and everything that is happening of course it's just not going to continuously move forward and the feeling was that yes everything could be running fine but then once you start trying to engage different nhs trust time just balloons so that's specifically happening in england what i find what was interesting was that Scotland and Wales actually have standardized costings that are currently working so did have no site level variations so it's one contract for all standardized costings so following the successful models that obviously happened in Scotland and Wales having speed as a KPI specifically looking at how can we actually making sure that from that beginning initiation to actual actualization um is as efficient as possible and it's not a side project right and so i think that these are some of the things that are key in when we're thinking about how we're actually bringing these things to market yeah otherwise we'll just keep losing to our

1:02:29James:neighbors yeah i know we've only got a couple of minutes left but if you were to if you were to i don't say call out but like if you who's got the most responsibility here do you think i know there's a lot of shared responsibility between government and pharma and and there's a there's a lot of different places where you could land responsibility here but who who holds the cards who holds an ability to actually make change here is it policymakers is it like who where where do you think the the the most gain for the least effort is where do you who holds the ring here

1:03:03Dr. Elsa Zekeng:i think you said it already which is when you put specific boxes i have to yeah yeah eventually somehow they then get ticked and so if that's what it's going to take then that is what it should take so whether that is from a policymaker's perspective whether that is from an mhra perspective i don't know if it's mhra that holds the cards to be able to put that as a metric to potentially even examine NHS trusts, right? Yes, you might have, I don't know what KPIs NHS trusts are, so if anyone listening, it'd be great to learn. But potentially engagement in clinical trials and speed to actualisation could become a key metric so that it incentivises everybody to be able to do that.

1:03:49Dr. Elsa Zekeng:But then again, of course, the question with that comes with budgets, right?

1:03:52James:Of course.

1:03:53Dr. Elsa Zekeng:Because who is going to actually do, whose job is that?

1:03:55James:We don't have to do, which they do, yeah. exactly more targets awesome yeah um and what are you most excited about at the minute with everything that you've got going on and everything that's going on in the company um let's end this on a positive what's something that you're positively excited about

1:04:13Dr. Elsa Zekeng:honestly that there are actually quite a few early adopters and there are people who are interested in the topic area women's health is not as niche as people make it a thing a surprise surprise and global majority populations are actually global majority globally so we need data sets so i am quite excited by the fact that um there seems to be not just talkers do us right so that's based on the pharma perspective even the fact that the call is currently open of course we've had this great conversation and one could call it critique but at least it's there we get to give feedback at least the conversation is happening i think moving away from the constant like we have to be politically correct and agree with everything that's just it's like change and movement has to happen and that's by us having these conversations and by these um calls being open and people being early adopters so yeah that i'm excited about like there's lots happening uh we just keep we can go faster i'm always like guys we can go faster let's go let's go

1:05:14James:also it's been absolutely a pleasure thanks for coming on and talking about this and thank you for your honesty and your candor and frankly for the education i think it's been one that i've definitely learned a lot from if people want to uh get in touch with you to learn a bit more they want to learn about soccer data um or they want to ask you a question what's the best way

1:05:32Dr. Elsa Zekeng:for them to reach you absolutely i am dr elsa zekang on linkedin probably the best way to reach me and yeah my link is open awesome it's been a pleasure thank you thanks james hey everyone

1:05:46James:thanks for listening and making it all the way to the end of this episode. Remember to subscribe, rate us and leave a review. And you can head to the description of this episode to follow me on all of my social media so you don't miss out on any of the latest health tech content.