Multi-Omic Approach to Precision Diagnostics
One such macro trend is precision medicine, which we will be examining today.
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And with that, I’m pleased to bring you this week’s deep dive topic on multiomics approach to precision diagnostics.
So just gonna go over the agenda real quick.
Some of the topics We’re gonna go into just briefly major tectonic shifts.
So we’re gonna address that. And then we’re gonna go into a little bit of background And then we’re gonna take a look at the market where we think that this is relatively heading. And then we’re gonna go into the who and what of the market, the venture capital, the investment space and some of the large scale players and then the emerging startup community.
And then finally, we’ll cap it off with an analysis and a discussion.
So what’s been happening lately?
Let’s understand that there have been some very significant advances in technology specifically regarding medical science, large scale data acquisition, advanced analytics, machine learning and artificial intelligence.
Next generation sequencing.
Also, we need to understand that there’s a shift going on right now between B2B, more towards B2C.
We’ve seen that with Apple and with Google as they’ve made investments into this space.
Then we’re gonna notice that These technological advancements have really led us to the ability to analyze the entire biological system. So not as one specific part or one specific function, but how all these parts come together to create this entire system.
And then there’s been some significant capital allocations into healthcare information technology.
This has been going on for about the last decade or so. So that’s not so much new, but the text and the data integration aspect that falls under the information technology.
Is a major shift.
So let’s go into a little bit of background.
So a couple weeks ago, I gave a presentation on multiomics approach to precision medicine with regards to cancer. Some of this information is going to be a little bit redundant.
So we’re gonna try to move through it relatively quickly. But please note any questions you may have.
So some of the current problems, one of the major issues is diagnostic errors. Let’s take a look at the big picture.
So the quote on top, diagnosis is the foundation of meta And without the right diagnosis, patients don’t receive the right treatment.
Dr. Hardib Singh. He’s from the Bayer College of Medicine, and he works together on a report with Dr. Mark l Graber, who’s from the RTI International.
So using rigorous methods, we found that the diagnostic errors affect twelve million United States adults per year. Or one in twenty adults per year.
And it’s the common diseases that get missed.
Among those misses are infections, heart disease, and cancer.
So let’s look at medical misdiagnosis.
So whether it’s inaccurate late or delayed, This is an ongoing and and pressing issue.
John Hopkins recently had a study where they analyzed six thousand cases from all across the country, from various medical institutions.
And they found that one out of every seventy one cancer cases was misdiagnosed.
But to expand on that a little bit more, they also found that one out of every five cancers were misclassified.
These errors result in essentially delayed or inappropriate treatment or misdiagnosis.
So what’s the human cost and the financial impact of of some of these errors?
Most Americans will experience a diagnostic error at least once in their lifetime.
The patient deaths due to this range between forty thousand and eighty thousand per year. And these diagnostic errors and other efficiencies. This is not just solely misdiagnosis.
But this is upwards of seven fifty billion dollars each year. This is money that could be obviously given back through other various social programs or what policy government really seeks to to spend that. So kind of taking a little bit of a closer look.
Let’s look at rare diseases. So these are classified as less than two hundred thousand reported cases, but this affects three fifty million people globally.
There are currently between seven hundred or seven thousand and eight thousand rare diseases that have been identified.
What’s important to understand is that number is increasing and has been for quite some time now. Eighty percent of these have been identified as related to genetic and inheritance.
So kinda taking a little bit of a closer look at this. More than fifty percent of these rare diseases affect children. And unfortunately thirty percent of these patients passed away before the age of five. This is just of note, this is a major issue that could very well be discussed in a later deep dive.
So the question is really where do we go from here?
We’ve identified the problem. Is there a solution?
Fortunately, because of the technological advances that have taken place over the past several years, there’s been a very strong focus and very strong push for looking at OMIC So other names for it are Panomics, multiomics, omics based, which will all be used throughout this presentation.
But it’s all relatively the the same meaning.
So today, we’re primed for innovation.
Alright. So except in rare cases, there’s really no single technology that can capture the complexity of molecular events that lead to human disease. But there are different technologies that can be combined to diagnose disease and to really create a holistic picture of human phenotype and disease states. However, it should be noted that implementation of multiomics data introduces new informatics and interpretation challenges, which we will discuss here shortly.
But there are individuals that are working and teams that are working on novel approaches to analytical and statistical methods to improve this.
The real question is, can we use this information to create actionable tasks to really guide diagnostics, therapeutics, and clinical care.
So I won’t go to this too much, but this is just a recap from a couple weeks ago. So the main idea is to understand the flow of information that underlies disease. And we’re going to look at differences in DNA RNA proteins and other cellular molecules.
By conducting high throughput assays that produce tremendous amounts of data. And again, don’t think of this as a single approach, There’s no set standard. Think of it this think of this approach as like a package. So can we incorporate various omics into a certain package to generate a certain amount of data for a desired end state.
So a lot of questions are sort of revolving around this. And you’ve noticed in the news with Apple and Google, Microsoft.
As well as some large scale venture investments and acquisitions in this space. Is this a revolution?
So we’ve seen a significant increase in the number of available genome wide association studies.
Advances in next generation sequencing and mass spectrometry have taken place in the past several years.
And this really leads kind of in parallel to the leaps and bounds in the bioinformatics and computational capacity.
That our computer systems are able to handle.
Additionally, there has been significant advances and really an understanding, a thorough understanding of systems biology.
Going back to that complex biological system.
And additionally, which we’re not going to get into a whole lot today, but advances in biomarker discovery.
So what are some of the foreseeable challenges from this?
Really, it’s the data integration.
So we have a significant amount of data how do we bring it together to provide the best type of research, the best source of outcome that we are currently seeking and that these researchers and scientists, doctors are seeking.
Again, that leads into really the next step, the interpretation, and that’s the tertiary analysis.
So that is drawing actionable insights.
And inferences from this data.
And again, this is highly tech this requires highly technical and qualified teams.
So various backgrounds of life sciences, computer sciences, bioinformatics, analysts, and so on and so forth.
I’m gonna back up real quick.
So with these highly technical and qualified teams, I was reading a recent report by I believe it was Dan Snyder.
He is out of Stanford, I believe, unfortunately slipping my mind.
But he said that there are teams out there that have access to this data.
Unfortunately, they’re all computational scientists, and they’re specializing in, you know, machine learning and artificial intelligence, and deep learning, and and so on and so forth.
But a lot of these teams are running into issues because the individual that is providing the oversight and the management does not have a background in life sciences, specifically into genomics. So what we are seeing is essentially one of the trends is just a waste of this information without proper guidance.
So again, this is from last couple of weeks ago, we discussed this. There’s a shift in treatment. Won’t go too far in-depth into this.
But, really, what we are seeing is major advances in precision medicine to really get rid of this one size fits all approach and and really personalize it, either on a small group or an individual level.
You’ll see that one of the major advancements on the right side is companion diagnostics and biomarkers.
Really, that’s biomarker discovery.
We are not, like I said, gonna get too far into that today. That will be for a later topic.
So kinda where is this all going? We need to really take a step back, and I had to do this, to be honest, several times, while I was doing this research.
And you kind of have to understand where this is all heading.
So with the Asia Internet, IoT, and things like that.
And in addition to significant advances in computing processing and data management and integration and interpretation.
Where is this really heading?
So if you kind of really take a look at it from ten years out, we’re seeing some pretty incredible advancements at this time.
So One of those things is the decreasing costs of really high throughput sequencing technologies. And then again, like I mentioned, advancements in mass spectrometry and genetic sequencing.
In addition to that, there’s imaging analysis that utilizing artificial intelligence and deep learning.
This is going to significantly improve the speed, diagnostics and monitoring.
There have been several reports and several studies that have concluded that Imaging analysis via artificial intelligence and deep learning has been more accurate than the analysis provided by a trained human being.
Again, these are just initial reports and studies.
So the third part, wearables and smart devices So if we take the other two into account and really all the things that we’ve been discussing earlier in this report, wearables and smart devices, this is something that’s incredibly fascinating to really Apple and Google.
Really, what this does is provide real time data collection and analysis.
And then this is really integrated into a low cost platform.
I put quotations around low cost because people’s personal financial backgrounds not going to discuss that here, but smartphones, obviously, these smartwashes, for example, are several hundred dollars, but people are still able to afford them.
But that’s that’s another discussion. But really what this leads to is the health diagnostics that are being collected So for example, we’ve seen advancements in heart rate monitoring, whether it’s garmin, Apple Watch, or Google’s Android platform.
But really looking into the v o two monitoring.
So this is a very large advancement that Apple is pursuing. And in addition to that, there is, as you see on the right side, irregular heart rhythm observed.
So these devices are able to monitor diagnostics real time and detect incredible irregularities that we would normally have to travel to a trained specialist professional, hospital, or clinic to be able to get access to.
Where this comes down to is the integrated platform. So all this information is collected, where is it going?
So Google has — I’m sorry, Google has fit, and Apple has their health application.
Looking at the future of precision diagnostics, we have improved patient care.
Then in addition to decreased healthcare costs.
And then these analytics I’m sorry, these diagnostics, the future of diagnostics are going to be very data heavy, data centric, and driven by these massive amounts of data.
And again, these high throughput technologies have really led to innovations in bioinformatics, systems biology, computational biology, neither of which I have any trained education and So just for transparency.
But really what we’re looking at as well as we talked about a couple weeks ago, these electronic health records. As I mentioned, last time. Apple has partnered with, I believe it was thirteen institutions around the country to get access to patient’s records. The numbers do not know at this point in time, but this is very This is highly qualified information from highly qualified, collected from through highly qualified individuals So where this is going is to provide these essentially real time capabilities.
And then this slide – and I know this is pretty ugly.
I’m not a whiz with these graphs or charts.
But this is really to show that there are so many different variables that are really driving the future of precision diagnostics.
So just to name a couple, we’ve discussed imaging analysis, the high throughput computing, mixed with the wearables, and then additional environmental and lifestyle data.
But So some of those things are external.
If we look at the internal drivers of precision diagnostics, we really start to take a dive into this multi omec approach.
So again, ten years out, how are we going to be analyzing our health.
How are we going to understand our health?
Are we going to need to seek training professionals at several of these, whether it’s an institution, a healthcare facility, or or clinic, or is this all real time information that we can say a rest of essentially real time.
So that’s it for background. Please, again, note if you have any questions.
We’ll be able to discuss at the end.
Now we’re gonna go into talking about the market a little bit.
But before we go into this, it’s really important to understand that there are lots of tests out there.
There’s lots of scientific tests, lots of scientific studies that are trying to pull out and extract this data.
There are various markets for this. So we could talk about supporting markets, like high throughput screening markets, we could talk about mass spectrometry markets, we could talk about liquid biopsy market.
But again, we’re just gonna stay focused on really two two markets here. The first one is the global next generation sequencing market.
So this was a forecast from twenty seventeen twenty twenty two.
So this market is expected to grow to twelve point four five billion dollars at CAGR of twenty point five percent Some of these growth factors, the drivers of this or technological advancements in the next generation sequencing platforms.
The increasing applications of next gen sequencing growing partnerships and collaborations, the increased adoption of next gen sequencing and then decline in the cost of sequencing. So at the bottom, it says accuracy and standardization concerns in diagnostics testing.
So we’ve touched on this a little bit, but again, these are some of the limiting factors that are going to be evident in the and and essentially holding back for the growth of this market, really limiting the growth.
And this is something that I pulled from a report. Just really kind of going into the disruptive innovations and growth opportunities as you can see the disruptive innovations on the left side.
Combined with the growth opportunities.
It’s really just this vision that we have for ten years out How do we get there? What are the innovations? What’s the technology that we have?
What are the capabilities that we currently have to be able to process and integrate this and analyze this information and really push precision diagnostics to a level that we have never seen before There’s a lot of words on this page.
So I don’t expect you all to read it.
This is for reference, and this will be posted for review as it’s being recorded at a later time.
But really, let’s look into the diverse clinical applications.
So We’ll talk about this in a few moments, but the end to end informatics solutions, so can we conduct this research and then conduct or can we aggregate this data, conduct this research and provide health care specialists with the pinpoint research or pinpoint insight that they need. Additionally, there’s growth in AI big data analytics and tools to support that.
That’s been a very hot topic of discussion. We’ll touch on that a little bit later.
But really, the high speed data analysis and and computing tools, this is a major growth opportunity.
With the advancements in technology that are coming online today. And then genetics testing and analysis, and then the support of the SAS business model.
Some of the key trends in the next gen sequencing market. So again, the significant growth opportunities that exist in these multi omic platforms accompanied by the high speed analysis platforms.
New companies can look to target healthcare, big data platforms and analytics tools. In the interpretation, so that tertiary analysis segment.
And then there is a statistic there that the data interpretation and reporting segment is primed to reach seven thirty five million dollars in revenue by twenty twenty one.
And again, partnerships with these large clinics, large diagnostic testing facilities, manufacturers are going to be key to the growth of this. And additionally, the growth in clinical applications, whether it’s pharmaceutical or molecular diagnostics, we’re not going to really get into molecular diagnostics.
Today, but those are very important factors as well.
Going to some of the drivers, This is from a Frost and Sullivan report that we have access to.
And some of the things to take note here is that there is a growing demand for these multiomics platforms. So they assess that there is a low impact for these multamics platforms in the next year or two.
But if we fast forward five years, they have assessed that there will be a very high impact And I believe that this is due to the advancements in the high throughput screening, high throughput sequencing, and then the ability to interpret large datasets.
Okay. So this is where it’s gonna get a little interesting.
So we’ve talked about bioinformatics a little bit, and And it’s kind of hard to put into perspective because there’s a lot of, I guess, the best word or term is cross pollination.
In several of these segments and really these technologies and capabilities.
But as we see here, there’s a definition for next gen sequencing informatics.
And really, what this comes down to is computing, analyzing, interpreting, and storing data generated from next gen sequencing.
So it’s nothing too crazy per se, but the lines start to get very blurred when you start looking at the informatics of different tests, different studies, different procedures and so on.
We’ll talk about this a little bit more here in a bit.
But key solutions, so it’s been this has been broken down into four segments. So we have the hardware and computing segment. We have the computing platforms and software segment, bioinformatics and consulting services segment.
And then the sequencing service. So where we are noticing, and then we’ll discuss this in the analysis portion.
Is there is a noticeable gap in the capability to provide an end to end solution for these large scale companies and corporations.
So just real quick going over some of the key players in the value chain. On the left, we have laboratory information management systems.
Then we move into the sequencing technologies, data storage, secondary analysis, tool providers, and then the commercial biological interpretation and porting tools. Again, this was pulled from the report provided by Frost and Sullivan.
Okay. So we talked about this last time.
We really went in-depth into bioinformatics This is incredibly important.
There is reportedly significant growth in this industry.
The forecasts, for example, are expected to grow at over twenty percent through twenty twenty.
And then it’s really just kind of understanding the basic foundation of of what these capabilities, what these large data sets can do. But again, it’s really back to the processing.
And that’s a massive trend that we’re seeing here.
So that was the market.
Now we’re going to get really into the who and the what really of of this segment of healthcare industry.
So can I ask a few questions today? Mhmm.
Like Carter, when you get that device that’s measuring your small socks and that type of stuff, Is there a license agreement with Chris or something that says who owns that data that comes out of that?
When we we do that with you?
I have no idea.
So Or if I don’t think you’d be the owners of it have any idea, I think everyone’s just sort of like. So the data is just kind of being gathered out there and there’s not any like weird chains of, like, rice or custardies around it necessarily?
Yeah. I think and that’s a product of just ads information.
That nobody’s really — nobody’s really had the time to even get around with the discussion.
Because there’s always – if you look at fit this in the amount of information. Like, when when I if I buy a Now in that case, I’m sorry, in that case, I’m sure there is something.
Do you know?
How is that data segmented in in terms of ownership today?
So I’m not too up to speed on the wearables.
But obviously, in the past couple months, there’s been significant issues with data collection and sharing.
There are some companies out there that really try to integrate all these different approaches. So, like, the genetic sequencing, the omics, lifestyle diet and fitness and whatnot, and then the real time diagnostics through the wearables.
There are some startup companies that are doing that and that have received some pretty substantial funding to carry this out in very new companies.
On all of their websites, they have you have to sign an agreement that this information is freely shared.
So I think that there is some understanding that you have to disclose this?
I think we need to understand this.
Right? Because I don’t know what the answer is. I guess it’s in flux.
And my guess is we have two events we haven’t seen. We’ve not seen a judge green decision — Mhmm.
— of this industry. Mhmm.
And I don’t know what that’s gonna be. And we haven’t seen the telecom active.
My team Right.
Yeah. It seems like it’s what I’m hearing is it’s a lot, a lot less right now. There’s gonna be there’s gonna be a couple events here probably where the industry structure is gonna be altered by policy.
In a good way.
Both of those were positive sort of policy events, the shifting innovation we’ve seen we’ve seen Dubai say they’re gonna sequence everybody I met a guy the other day who’s running a project in Finland to take all patient data and put it into a single database and Finland’s changing their laws to do that.
There’s some conversation around the fact that HIPAA is explicitly not in China.
And China is leveraging that as much as possible to jump by basically taking in everybody’s data and not even asking for their permission.
Also, the Chinese culture doesn’t have a concept of privacy.
So it’s not even something that people are worried about. From a standpoint of, yeah, of course, been paid to belong to exciting.
So I think that somebody should put on the two list of what — Yeah.
— what’s the related structure because it may that may affect them.
And I’m not sure we understand how it affects us.
The other The other conversation I was just talking here is I’ve had some recent conversations with people that are close to bezos’ heel, and I wasn’t named that or not.
Texas Mac or sky. What’s human? Human?
So the general sort of consensus around that that crowd is is that TL Amazon, you know, those guys will figure out the data side.
And that that scale kind of thing, AWS will figure out the okay.
Now I have good data.
What do I do?
But the other thing I’ve heard pretty consistently is EHR data sucks universally.
From all the HR systems for the purpose of data analytics.
Mhmm. That’s so much noise. Yep.
I mean, it’s also It’s standardization.
Yeah. It’s — Right.
And they don’t even it’s just even if it’s standardized to the proposed standard, it still will suck.
So the notion that, oh, I can go to EHR and grab all the data that that the the practical application is the the the the the people doing the data analysis within those areas basically say it’s cracked.
So there’s gonna be some kind of event. And as you were talking about, does anybody know what’s happy Jonathan Bush?
Did he leave a thing?
Okay. Let’s find out where he is. Because he that was the main thing that he started with Athena. His data’s crap was printed off.
And if he’s been pushed out of Athena, that might be an interesting character to bring into this conversation. That’s that’s what I’m reading here about wearables too.
The industry standard is that the data is not consistent enough for anyone to really care about yet. So while it’s important to know who owns it, I think, ultimately down the line at this point.
So I wasn’t really dealing with it because of that, it’s just so inconsistent.
So there’s a whole entire, like, infrastructure of rights and management of data that has to be set up and built around this stuff too.
And it seems like some of these people are – their business is ultimately going to be almost like a content aggregator for the for the Internet.
Well, but they’re also gonna be in it.
I am I am I don’t know how to explain this all to everybody yet.
But I was moved by Laura Aserman and what she’s doing with the ice by trial effort in terms of despite experience in past slides of technology transition in conjunction with data.
I think it’s gonna be verticalized curse around a thing. That’s — Yeah.
— either high end performed athletes or breast cancer or it’ll be birdified for Or genetic testing for industry, you know?
Well, it’s because it’s – there are ten different twenty, thirty different systems that sort of got to be amalgamated and it didn’t require two minute intervention that these are due along a theme than across abroad. Paratory. And so we got to figure out what’s first, Susan. So we got to figure out what’s first. Well, I think we just got to be aware that there’s gonna be somebody first and Yes. Not like, is there gonna be one, but there’s a co order. Mhmm.
And that they tend to be verticalized. And that if we sort of hang around them, then we’re going then we’re going to sort of see something.
We’re going to end up — we’re going to see a more thing going. That’s where you’re gonna feed your guy. They may not have the right answer, but they’re gonna be the first one to give signals as to how the architecture might align.
And what rules will be, and and there might be some early winners, like, if you sort of know that big data is gonna be owned by AWS primarily or somebody who’s got tail, then it’s about who owns the who owns the clinical assays.
Or that’s the part that needs to be figured out. And how do we — had we had a lot of clinical assays at scale? And I mean, someone who has to take blood. And and and normalize the data.
So that’s it’s And it’s not like they’re like that body some diagnostics.
It’s not like everybody doesn’t know the diagnostics isn’t like, hey, if we get the diagnostics dealt with we won’t have that cure cancer because we’ll solve ninety percent of the issue. So ninety percent of the reason why people die from cancer is late diagnostics or whatever. I don’t know if you had the number, but — so I want to sort of know that it’s the best value solution.
But the reason still why it’s not working.
That was great.
Got some good things to look into a little more of it now.
A lot and understanding how the networks are forming up and then pop monitoring the networks. Is I don’t know how to do that, but that’s I think we’re the that’s where you’re gonna get flow information flow, and we want to be close to maximum information flow.
Don’t we all I said, don’t don’t we all All bad, actually.
First and second derivative of information change. Yeah.
I was reading about information anxiety a little bit earlier this morning.
The information anxiety, there was a book.
Out there. It’s like what we think we know versus what we really should know. And what we think we should know is exponentially and greater than what we should actually know.
There’s a book about it.
It’s pretty interesting. We’re metaphysical.
Keep going. Yep. Whoops. The known unknowns.
So the unknown unknowns are So okay.
So these are some of the key players that are really driving this industry to push for these essentially next gen sequencing and then these other testing and extraction methods. So these are guys that are at the essentially top of the chain, the acquirers, the mergers, and so on and so forth.
I won’t go into each company. I’m sure you all had plenty of time to read into the past few minutes.
Again, these are some more packed bioagulant, thermo Fisher and QIAGEN.
You guys have a history behind for a moment?
They’re from Saint Louis. Aren’t they?
Now, some don’t worry about it now, but read the original history on the creational thermometer for very interesting business model. Or let’s say it’s a poor man’s version of the program.
Thermo, the parent.
So these are some of the early stage companies.
And again, I tried to not focus on one specific thing that they’re tackling, I try to get kind of a little bit of a broader approach to this. But there definitely are some trends So we have I’ll make x on the top left and then solve bio and Maverick Biomics on the bottom left.
There has been a push she put electronic health records and all of this personalized data and information onto blockchain technology.
DOC AI is pursuing that.
And they think that they have a solution to provide precision based medicine and diagnostics really.
On the blockchain.
And I know we don’t really look into ICOs, but they just had an ICO of ten million dollars back in fall of twenty seventeen.
So Prothera is interesting.
I have a conversation – I’m having a conversation with them on Friday.
What they do is they don’t actually conduct the testing, but what they do is they consolidate all the information to be able to essentially provide these new insights into various diseases and disease and really put together comprehensive profiles.
They’re focusing in cancer.
That is their channel.
Don’t know if they’re gonna be looking into other disease states. So bio export, kind of an interesting company. They are I can’t recall what accelerator they’re going through, but they’re currently going through or have just graduated from an accelerator program out in San Francisco, I believe.
So again, this is kind of just really as it says, the intersection of the technological advances to analyze large data sets.
Pillar on the bottom left.
Well, before you hit that mobile line, I believe we’re all familiar with this company here.
But they are exploring the same kind of down the same channel of this data aggregation and really driving the push to draw meaningful insights from all of this.
Piller is interesting.
I’m very intrigued by what this company is doing.
They launched earlier this year. So the fundraising right now is a little unknown.
But what they’re trying to do is utilize the latest next gen sequencing technology.
To really put together a personalized report, that I believe would be able to, if they’re successful at this, be acquired and consolidated with some of these large scale companies.
These large scale tech companies that are making this push towards personalized medicine.
And then just again, some of the leading investors broken down by some of the early stage investors.
Institutional strategics, and then the others as well.
By others, like grants, for example, Just a couple of clippings from some recent news articles. So Purion Diagnostics, They just raised three million dollars just recently in February, I believe.
And then on the top right, Again, we touched on this a couple weeks ago.
Microsoft and Google are really driving the innovation towards precision medicine. Perian here.
They spun out of wash u.
Yeah. Yeah. Back in twenty fourteen.
What was that? Perian? Perian diagnostics? It’s running or was running out, but we still is not.
Are they still alive?
I had I believe they got acquired.
Which is current. What happened at Tedstone?
That is Tedstone.
That was the company Jim Howard was working for, that Ted was running So if they get both, I don’t know the answer to that. I haven’t talked to Ted about it in a while, so I have no idea what he’s up to.
Again, some of the brightest minds in the country and in the world have gotten together to discuss big data and big biology.
And then venture investing has soared to and continues to soar into healthcare IT.
We’ll take a look at that here in a moment. Some of the mergers and acquisitions, some of the more recent activity within this sort of next generation sequencing informatics market?
Unfortunately, I think on human longevity, but the the one that sort of keep comps trading on COCs.
I think they’re mounted down to ten thousand dollars for a full There were off twenty five thousand dollars and and it’s one of smart skies and genetics. Yeah. Kicked out or yeah. Keep out.
You got kicked out with? A human longevity about three weeks, maybe a month ago. Okay. That means that they’re probably gonna take off even more.
You got kicked out. I think so. There’s a lot of smart guys on with the planet on the subject. So There was a there’s been multiple of what our call is that there have been multiple turnovers at the top and at advantage of the vendor.
Right? Quick vendor? Yeah.
You gotta kick that with human longevity?
I think so. That’s fair to say. The guy to watch. Yeah.
We have the human genome sequence in a much cheaper because of him.
Has got more – on the – on the – on fulfilled ideas that is set than anybody on the Facebook plan.
What was his name again?
Right. Mentor. May twenty five, you create vendor retiring for human longevity. Over time.
Well He is like eighty.
He’s like eighty.
The articles are a little more.
Okay. Retiring might be a nice way to Generate.
Anyways, If you notice go through these companies and look at trends of what these buyers slash acquirers are looking to really bring into their companies.
A lot of data management, information management and analytical solutions and platforms.
Going to move into a little bit of the financing landscape. This is both from the CBN sites report.
So this is looking at – this report was published early two thousand eighteen, I believe.
Really just kinda taking a look at just general healthcare financing trends over the past six years.
And then getting a little bit more in-depth breaking down the last eight quarters into various stages for investments.
So seed early expansion late and then the last one is other.
Let me make sure I understand this. Okay.
The top level is our seed deal.
The bottom the bottom The bottom up.
Bottom bottom up.
And then other, which I’ll be honest, not quite quite too sure what other would constitute? What’s your source for this to CB Insights?
Yes, the CB Insights.
I’m not sure what that tells us. Yeah.
It’s just where these investments, like, what’s what stages?
I mean, twenty percent in Q3, twenty seventeen, thirteen percent in Q4.
It’s a little it probably would be a little bit better to take a look at.
A larger timeline, but it seems relatively constant.
You know, One thing that I wrote down as a presenter earlier is I black skilled personnel.
Yep. I think an early economic opportunity, I That’s where it was going to be a lot of pressure.
And so that might be one of the harbinger sources for convergence.
So it’s simply the fact that there aren’t enough people, and we gotta increase productivity is gonna cause rules to change quickly and economics to change quickly simply because because — Mhmm. — because the the force of the bag will force them.
And then the other thing is is we’re seeing a tremendous bifurcation and longevity data between the Mavigate cohort versus the the upper decile.
So you’re seeing the upper death file. I don’t know what the numbers are, but upper death file has got ten years, the longevity of the Medicaid cohort. Medicaid Medicare cohort.
And I I don’t know what that means. So we talk about the fact that things like Fitbit are all for wealthy people.
Do you stress, hey, the people actually did this kind of analysis probably.
The Medicaid cohort when you look at overall costs. Mhmm.
But it might be that they’re gonna be managed in some kind of there’s a there’s gonna be a what the hell is that thing called in?
In Africa for pain.
It’s a text message in Africa for pain.
There may be a low – it’s one of the reasons why pharmac is sort of interesting is that Somebody might come in and and come in with a not a fitbit, but some other kind of solution into that cohort.
That improves care, but it’s more commercial. So whether that’s like Sugar Clinic, down to Mexico or somebody who sort of perfects a different service delivery model under that area to catch people early.
So it may not come from Fitbit, but it may come from a different service delivery model. Isn’t that what a lot of telemedicine is trying to do?
Well, so we’re on that.
I was on this panel with singularity recently, and and there was a crowd in the room that said, we need free gigabit ethernet to everybody, and it’s a civil right. Because they were found on the north side here.
And a guy who runs the virtual health virtual health mercy was there.
And and everyone’s like, you know, AT and T needs to give us all day to baby some extra free or we’re gonna, like, burn for the brand.
Sort of that kind of conversation.
And I asked the Mercy guy, and we didn’t have a centene guy. It’s like, but why is it not and economic interest of centene and Percy from a service delivery model to provide, to be an augmentor of that capital cost so that they can cut down the cost of their service delivery model. So for every hunt live they take care of, they make whatever, call it twelve hundred bucks a year. Why shouldn’t they pay ten bucks a month to be able to get better remote access to that client. So I think there’s gonna have to be something in the business model as we deal with the Medicaid patients.
You gotta have to have some we might see different business model work that’s more like sugar plank in Mexico than, hey, we’re going to give them a Fitbit. But maybe it’s Fitbit. I don’t know. Maybe you give them a Fitbit. I don’t know whether Fitbit’s good.
You know, if you think about it once that coworker’s problem, it’s diabetes, it’s overweight and high blood pressure, which then just is a precursor to cancer.
So how do you get you know, you probably got to deliver empathy So I just think that business model might be different.
So I don’t know if we’re looking I don’t know what that means, but it’s a different it’s a different channel. And that in the emerging market, it’s a different channel.
High blood pressure, you know, we’re still dealing with the issue that you’re dealing with high diabetes, high blood pressure, or or being overweight, those can be icons right now.
By staring at somebody.
You don’t need to do any genomics data.
The genomics data is good to tell you in that cohort that ten percent of the cohort might die a month or later because of somebody. Because of the genetic data.
But you can’t even get those people to you’ve got challenge in getting those people into a situation where they are either even just compliant with meds?
Well, I might challenge that a little bit. I think the whole process of early detection And then in the analysis, we’ll just go into it real quick.
But there’s a group out there that’s pushing to really emphasize and promote neonatal and prenatal screening to be able to identify.
I think right now it’s like twenty five diseases.
They want to increase that profile to But that disease class, what is that disease class total cost of care?
Yeah. I’m not sure.
And when you pre – you just take high blood pressure.
When whole foods when Whole Foods did their whole HSA thing before ACX. So Whole Foods came in, said we think HSA performed better.
They were an early leader in fast. And they said to their they said to their to their cohort, look, if you’re got diabetes, you’re overweight, high blood pressure, you’re not dealing with it, you’re gonna pay more for health care. But if you resolve it, we will back pay difference in your healthcare to yours. So we’ll cover your we’ll incentivize you.
Right. That’s really terrible. But here’s the thing with interesting. So this is a reasonably well care for a cohort. They’re under doctor’s care.
They had health insurance.
They were changing the program in which they specifically needed to diagnose.
They need to set the baseline who’s weighing the difference. I needed to understand diabetes overweight, smoker, and high blood pressure.
And thirty percent of their population was under doctor treatment and was not aware that they had high blood pressure. So the clinician had not even diagnosed high blood pressure. High blood pressure is a stupid simple thing to diagnose. Right. But thirty percent of that population under a private care plan had not been diagnosed for high blood pressure by the clinician. Yeah.
That’s that’s So it’s not like low end Medicaid doctor.
Right. I mean, it’s so there’s And that high blood pressure. Right?
I mean, not like looking into rare diseases to did a little bit of research into that for this presentation, that could be another topic.
I mean, rare diseases like rhinovitis supergroup. That’d be great.
An overwhelming majority of the people that do have a rare disease.
Don’t get properly diagnosed until five years. After they walk in to see the first specialist.
And and it’s so bad, but in the grand scheme of things, the cost or market opportunity, I’m just sort of wondering is Most people are gonna have in fact, can’t even be diagnosed with high blood pressure and that’s — Right.
— sixty percent healthcare costs.
It’s I just don’t understand the two cohorts well. There’s something else we’re missing.
And that now could be — Yeah.
— it could be — I mean, that’s the intent of this.
Well, so it could be that it’s a sampling error. You walk into your doctor, your doctor is busy, he’s seen you for fifteen minutes, he’s seen a whole bunch of people, He wishes all of them would be with their high blood pressure.
He’s worn out.
So it’s just like people can’t look at imagery data, and AI is better. So it could be another thing to look at is it could be that longitudinal data is a better way to identify things like high blood pressure.
And if we one of the biggest challenges that always felt is what do we need for innovation to occur? Because then we started meeting.
Customers. Okay. And they need early adopter customers.
So if you go to every market that’s succeeded, big.
There’s a there’s a there’s a mechanism of early doctor behavior.
So the question is, what is the early doctor of behavioral health care? And to some degree, early doctor of behavioral health care is shunted by regulatory environment.
If you if I’m using some guy just sent me a device — Mhmm. — eKG — Mhmm.
— pulse ox, temperature, and blood pressure on one device for twenty bucks.
There are about ten diagnosis diagnostics that can add to that it would give me longitudinal data to enable the device, efficient broadband.
You just sent it to me, but, yeah, I think you’re cool here’s one. So and it measures it off. There are like ten things he can put on top of it. They give me other information.
Watson needs to have a bunch of other things, but he’s not allowed to provide because the FDA will not let me have the data.
Really? Now I can take the data out, go process it myself, and still get there. But the FDA won’t gives us no clinician involved.
So that goes back to my point about the accuracy of the data.
If you get if you make the sensor you know, more specific and more sensitive, would would that convince the FDA?
Is it a is it is it a sensor issue?
I don’t know. Or is it just pure regulatory?
It’s it’s a combination of things. It’s like when we when we went to an episode that you may not have known, but like in the late nineties, there was this whole episode where everybody’s getting full body stands.
The goddamn thing would pick up, like, fifty benign things. And all of a sudden, the patient’s like, I’ve got this thing.
What should I do? And it just triggered this whole cascading set of just like we’re going through this genetic thing. At that point, it was full body cap cap. And all these doctors like pushback on the rich people said, no.
You don’t need to do it, but it was just like the venture is doing for full genomic stuff.
They did this sort of full body a virtual colon scan and the whole crap load of stuff. It was I think it was MRI and CAT scan. You know, around fifteen hundred bucks.
And it just triggered this whole cascading set of the elite bugging their doctors with over data.
And it sort of died down again.
So Sometimes, so one of them is that like the FDA’s concern that you’re going to just sort of screw up your SKira people.
You know, if you really knew what was inside your bowel movement, you just wouldn’t wanna know.
So I’m not sure I wanna tell a new hero. But I’m I mean, I’m buzzed by with the same problem as food. When we talk about agriculture, we’re like, oh, we’re gonna have this bespoke, cool food.
It’s all gonna be great. They’re gonna have organic and all those kind of things. But still, there’s thirty four percent of the population at the bottom of the pyramid, they just need calories for one point nine five.
And GMO warn, it’s gonna do that.
And if GMO and round up are bad, maybe it will cut the person’s life short by a year But the fact that they have calories means they’re gonna look at seventy five.
Right. From sixty four. So yeah. So you’re sort of, like, that’s a shame, but but let’s not lose sight with the fact that we got to deliver calories cheap.
In a similar way in healthcare, I don’t know where we want be on that conversation and where the leaves could be.
From above, that so many practitioners can’t even why can’t those practitioners diagnose high blood charge. Yeah.
It’d be interesting.
And who is the early adopter network?
And the early adopter network may be Finland.
There’s nothing we should think about. It doesn’t have to be conus.
Maybe we go listen to tweets from those areas to get perspective. I don’t know.
But I I think we got to know where the early doctor network is.
On the high end, you’ll get early doctor network from people who’ll pay off their doctor to make this happen and everyone will get in this sort a frenzy that that’s cool.
So you’ve got, like, the elite will do their kind of thing. But then at the low end, you’ve got nurses in Kenya Mully, Mully’s son lost their child in childbirth for something that Mully said that had been in the U.
S. Basic clinic would not have happened, an almost welcome mother.
And here we are in Kenya in a modern world, and I’m wealthy and I can’t even get my baby delivered effectively.
So if you go and talk to nurses that do work here or do it like in Kenya, like, I love Kenya. Because I can actually do my work, and I can serve a whole bunch more people because I don’t have all this other crap around.
So I’m not sure I’m helping you.
But No. There’s there’s definitely a lot of things to unpack from that But unfortunately we’re — I think we got to think hard.
I think we got to think hard. I think we it’s a good combination.
And I think to turn our analysis into reality, we need to get closer to some of the cohorts that are struggling with elements of it. So Laura Esterman, or somebody who’s working sugar clinic in Mexico City on diabetes or or if somebody I don’t know if anybody’s been working. It’s so boring, but significant is, you have high blood pressure.
Which just stick statins and and put pressure into the floor like we’re right. Right.
I mean, just to kinda How do I go to polyfill?
Does that remember that? You’re supposed to do there was a polyfill that they’re getting great.
It’d be a baby aspirin, a statin, and a high blood pressure, all in one pillages take once a day.
What what happened to the smart toilet?
The hardest. This is cheaper than the department. And and he met and and he’s part of the the world with something like that.
Mike is So it’ll it’ll trigger a conspiracy You can you can eat grapefruit and exercise, and you can eliminate the deeper step and boom.
No need to to be tied to the drug.
How many people you bring?
You’re gonna be upset and come out with the exercise in a pill.
No. It’s that’s far. Yeah. Okay.
Wrap it up. Anybody on the phone, have anything to add?
Are they awesome to you? Cool. Thanks.