David Sable is Still Working on Making IVF More Accessible
Thanks for tuning in this week to hear David Sable and I talk about industrializing IVF. Here is the full transcript.
Harry Glorikian: Hello. I’m Harry Glorikian, and this is The Harry Glorikian Show, where we explore how technology is changing everything we know about healthcare.
The first person ever born as the product of in vitro fertilization was Louise Joy Brown.
That was back in 1978, when the procedure was still highly experimental.
Louise was greeted around the world as a miracle baby.
But today, Louise Brown is 44 years old, and every year more than half a million babies are born through IVF.
But here’s the thing.
While IVF is far more routine and more available to couples with fertility problems, it still isn’t nearly as reliable or accessible as it should be.
At least, that’s how my friend David Sable sees it.
David is a gynecologist, an investor, an academic, and a magazine columnist who thinks deeply about the state of assisted reproductive technologies.
And from his studies of infertility services, he’s convinced that society is on the cusp of bringing down the cost and raising the success rate of IVF, so that it can finally become an affordable solution for millions more people every year who want to start or grow their families.
And he thinks one of the keys to the next big wave of advances in IVF will be artificial intelligence.
As you’ll hear in our interview, David thinks most IVF labs today still operate almost like artisanal kitchens, with way too much riding on the judgment of individual doctors and technicians.
He thinks machine learning algorithms could supplement human expertise at many points in the process, and turn what’s essentially a craft into a truly automated and predictable industry.
David and I first spoke about the world of fertility medicine back in early 2020.
And I wanted to have him back on the show because just in the last couple of years, a bunch of new ideas have emerged for how to get more data on every phase of the reproductive process and how to train algorithms to make better predictions and give better advice.
David’s central argument is that IVF won’t truly be democratized until providers have quote-unquote “engineered the hell out of” IVF, to increase success rates and lower the chances that patients will have to pay for more than one cycle of the treatment.
At the same time, he says the concept of value-based care needs to make its way into the IVF world, so that patients and their insurers or their employers only pay when the procedure works, not when it fails.
This is the first in a series of shows I want to bring you about how technology is changing the state of the art in fertility medicine.
Four weeks from how, I’ll bring you a conversation with Mylene Yao.
She’s the founder and CEO of a company called Univfy, and she’s trying to put a lot of David’s ideas into action by using data to provide IVF patients with more effective counseling.
But here’s my full conversation with David Sable.
Harry Glorikian: David, welcome back to the show.
David Sable: Thank you for having me, Harry. Great to see you again.
Harry Glorikian: Absolutely. I mean, for those people who are listening, David is the guy that you just love spending time with. Like when I’m around David, it’s just impossible not to be happy because he exudes such a positive aura that I love spending as much time with him as I can.
And unfortunately, because we’re not nearby each other, I can’t spend as much time as I’d like with him.
But it’s great having him back on the show. So the last time we spoke in early 2020 on the show. In the spirit of those sort of “previously on” summaries that you get at the beginning of a TV episode, can you remind us what you were doing back in 2020? What are you doing now and what’s changed?
David Sable: Well, I guess the biggest thing that changed is now I get to go outside. But yeah, we were we were just a couple of years into our project to, we’re trying to re-engineer the way in vitro fertilization is delivered in the world.
We think that our data, we’ve studied this for going on ten years now, that the true addressable market, the number of people that need the procedure is about 40 times the number that are getting it now. And in order to deliver that, we need to really scale up and follow the kind of tech innovation playbook, standardization, automation, process optimization, defining best practices, which really kind of plays right into it’s like the Harry Glorikian way.
But really what Harry, what you’ve been studying and talking about for a long, long time now, since then we found just a lot of validation for our thesis and particularly the area of trying to morph kind of human intelligence and organic learning with what we’re learning to do in AI and machine learning.
You try to take all the stuff that we’ve developed in a catalog, passing the passing the baton from one practitioner to the next, the way the scientists and embryology labs work traditionally to let’s process, optimize it.
David Sable: We’ve got 40 years of great science and some relatively mediocre engineering. We need to bring the engineering up to snuff if we’re really going to scale this procedure for all the groups that need it that aren’t getting it.
Very interestingly, about a month ago, we had the first meeting of the AI in Fertility. It was just called the AI in Fertility Conference, which is now going to become a regular thing. It was in Dubrovnik in Croatia and it was really, you know, it sounds kind of esoteric, a little, little wonky. What a fascinating, exciting meeting that was.
It was just dominated by scientists and engineers and data people. So I was just like the humble little doctor sitting in the corner trying not to be noticed. But just a terrific, terrific exchange of ideas, very mind expanding, learned a lot, and kind of reinforced a lot of what we believed and made some connections that I think are going to accelerate the whole thing, which is great.
Harry Glorikian: Yeah. I mean, I really do want to double click today into that role of machine learning and other forms of AI and how they can play, you know, a role in improving treatments in, you know, fertility, infertility. Which, you know, as you said, you know, really means improving the success rate.
Right. And so, if you were to sort of start at a high level, what are we learning about which parts of the IVF process can best be optimized through… I mean, for just to hearken back, I mean, the last time we talked, we talked about the idea of training computer vision algorithms to analyze embryos at the blastocyst stage and help decide which ones are most viable for re-implantation. I mean.
I’m assuming we’ve moved way beyond that in the past couple of years based on what you’ve seen.
David Sable: Yeah, we’ve moved beyond that in a lot of ways. In a lot of ways that was the low hanging fruit of applying artificial intelligence to IVF. And I’ve now met, I believe, 22 companies using some degree of machine learning for the optimization of IVF. And Just about all of them started with “Let’s choose the best embryo.”
And the problem there is it’s a great application. It’s good as a demonstration project, but frankly, the value proposition just isn’t there. But I’ve got ten embryos and using traditional selection criteria, the embryologist puts the best one in the second month instead of the first month. It really doesn’t move the needle when we’re trying to go from half a million babies a year to 25 million babies a year.
But in aggregate, you look at all the micro processes, all the decision points, all the things that have tremendous operator dependence and no set decision making criteria that there’s a lot of “I know it when I see it. This is my way of doing it.” Kind of artisanal.
It’s like we’ve got all these artisanal kitchens that call themselves IVF labs, but we need to we need to to engineer them.
In the best IVF labs in the United States right now, which have the best numbers in the world, you have a single genetically normal blastocyst, the chance of having a baby is about 65, 66%, which having started out in the days when I started doing IVF, where we would take three, four or five embryos at a time and put them back in and generate a 10% pregnancy rate overall and a very high twin and triplet rate because of it.
David Sable: To think that using one, that we have a two thirds probability of having a good outcome is just it’s spectacular. Problem is, it’s not good enough. Really at this point, that should be the floor of every lab and the range worldwide. We’re doing two and a half to 3 million cycles a year, getting half a million babies.
So most labs are nowhere close to that 65%. So what’s keeping us from getting there? And the difficulty with IVF is that it’s a series of steps, each of which needs to be optimized. And we’re judging them by the pregnancy rate weeks later, each isolating the effect of any one part of that process by the time you measure whether it worked or not.
Each thing we did is just drowning in confounders. So we really need a really smart mechanism to sort out what we’re doing right and sort out what we’re doing wrong now for IVF. Machine learning comes in at a perfect time because we’ve gone from pregnancy rates in the low single digits to in the best labs, pregnancy rates 60-odd percentiles with a single implantation.
And that’s all we use. That’s using human machine learning. It’s 40 years of aggregate work by scientists, doctors, embryologists, perfecting their craft in a craft way.
David Sable: And I don’t want to diminish the importance of this. Think when I was giving one of my talks in Dubrovnik, I said, Hey, listen, let’s not think that we’ve been doing lousy work for 40 years and now the computers are going to save us. Look at the steady improvement we’ve had. It’s been remarkable. However, we’re probably getting to the end of what we can do with human eyes looking, using light microscopy, the relatively narrow band of magnification.
There’s a lot of connections that we need to make that we just don’t have the ability to assess. And that’s, as you said, to a point, I’m using optical systems to look at static images. It’s using optical systems to look at dynamic processes. And we now train cameras on embryos as they develop, which we can glean information from.
We’re trying to figure out not only which embryos are going to turn into pregnancies. We’re trying to extrapolate the genetics of the embryo so that we can tell which ones are genetically normal without biopsy. Right.
Moving upstream in the process. We’re using these processes. We’re using machine learning to assess the sperm. If you put a petri dish full of sperm in front of an embryologist, how many sperm can they track with their eyes at a time? A couple dozen, max. These are extremely trained, very talented people.
And then, you know, and we now have systems that track hundreds of sperm at a time, rank them on the basis of the criteria that we give.
David Sable: And you can see a sperm, we’ll have a little green circle around it, and others have a blue circle, then a red circle and others of the exosome, and it will follow the best sperm until another one that comes along that’s better. And the green circle will jump to a different one, and the blue will jump to the one that used to be green. You’re really [doing a] spectacular type of analysis that humans can’t possibly do.
So in terms of feeding data into a computer and having the computer tell us on the basis of hormone levels, age, various bodily parameters, what the best medication stimulation will be, having the computer assist us in changing the dose of the medication each day, which is a decision, traditionally I would have a stack of 40 or 50 or 60 cycles that are undergoing and I flip from one to the next and go over the blood test the ultrasound work and make these decisions, knowing full well that I’m making the same decision over and over and over again, and that if we’re going to properly scale this to where I’m overseeing 5000 cycles a year instead of 200, I want a computer to make these decisions and just spit the outliers out.
Let me look at one percent for particular attention.
Harry Glorikian: Yeah. And, you know, these systems can take in more parameters. I mean, I was talking to Dr. Mylene Yao, the CEO of Unify, and, you know, using their system to look at, you know, the mother and age and other factors that may shed more light on how, you know, will this pregnancy take in that in that woman and the whole financing behind it, which was, you know, an interesting conversation that that we’re going to get out there pretty soon in the next, you know, 2 to 4 weeks.
But. So your talk, though, was on you know, was a SWOT analysis of A.I. and fertility. Right. And for those who don’t know, swot its the strengths, weaknesses, opportunities and threats, which is a great title. I mean, you.
I feel like you’ve been talking about the strengths. I don’t know if you want to go into maybe a little bit of the weaknesses and then the opportunities and then maybe the threats. But it would be great to sort of, after that, get your big take home lesson from the conference in Croatia.
David Sable: Definitely. Well, the strengths are, as we’ve said, each process that we’re doing by the seat of our pants. We’re really not collecting, we can’t collect enough data to make the decision correctly, or we just don’t have the physical capacity to go beyond our level of understanding.
That’s the broad strengths. Weaknesses right now, one is that we don’t have enough data if we’re trying to bring the same type of analysis that we use for meteorology. We’re even looking at the way crowds move from a satellite where you have massive amounts of data points to compare with a relative homogeneity of the data that goes in. So you can make these big macro predictions.
Weather is a perfect example. IVF, we’re doing, we’re just not doing enough cycles and each patient is a little bit different. So the computers have to be very smart and they have to be smart enough not to draw premature conclusions. And we have to be smart enough to recognize that the predictability of these processes is going to vary and not to jump not to jump too quickly to change our processes or or think that if something contradicts 40 years of experience, that is not just that, that we’ve gotten it wrong.
David Sable: It’s just that we need to feed more data into the system and let the system learn from itself. Now, I’m not a data person, as you can tell, but just trying to, you know, my own knowledge of math, statistics, and the way computers work. Second part, second weakness of IVF and AI is that so much of the IVF cycle is not observable.
You know, we take an embryo, we put it back into the body, we wait a week and a half or two weeks to do a pregnancy test. There’s no data to analyze. You know, maybe we will find a way. And I’m actually working on an idea for putting a probe into cervical mucus, which is one of the few entry points where we can infer data about what’s going on inside the body non-invasively. It’s kind of a kind of a reproductive medicine version of an electrode, the kind of electrodes we use for EEGs and that type of thing.
Harry Glorikian: Yep.
David Sable: One of the big black boxes that we’ve been trying to solve that we’re hoping that AI and machine learning helps us with is we’ve been trying to get sperm to talk to us for hundreds of years. And for the longest time we thought with sperm, sperm is a Ziploc bag full of DNA with a tail swimming around. And so what do we do? We assess the way the sperm swims. And we assess the way the sperm is shaped.
We’re taught morphology and motility, the two cornerstones of looking at sperm. Problem is, a sperm’s job is not just to swim around. It’s carrying DNA. The DNA has to be normal. And there’s also very important regulatory functions that the sperm performs just after fertilization. The egg doesn’t do all the work. The sperm actually has has a say in the embryology development in the very early part.
The problem is all we know how to look at is the way the sperm swims, which one of my colleagues used to say, it’s like trying to figure out what’s in the trunk of the car by reading the license plate. You just can’t do it. So we’re trying to employ AI techniques to learn more things about sperm.
The problem is, so far all it’s doing is doing a better job of evaluating morphology and motility. So we’ve got to find a new way of assessing sperm, a new way to look. That is holding us back. So we’ve got these great tools, but there’s a, we need to get the tool to be able to handshake with what the sperm can say. And we just don’t know how to instruct them to shake hands, which is real frustration so far.
David Sable: This problem that I see with what we’re doing so far is we are gleaning outcomes data. We’re trying to compare what the machines observe with who gets pregnant. But the only things that we can test that make it to the pregnancy test, are people that have undergone the processes as we do them now.
For example, when we see we try to train an algorithm to choose an embryo based on whether the embryo, whether the embryo will get turned into a pregnancy or not. The only outcomes that they can, that the machines can use so far, are ones that have already been screened by the norm, by the traditional technique. So there’s a there’s an inherent bias. And one of the data people said to me said, yeah, these data are poisoned from the start.
And what and what they were trying to explain to me that I’m not smart enough to understand is that the machine learning over time with enough data, um, poisons itself. It actually removes that bias that it started with and gets better, which I just kind of blew me away.
And I’m maybe ten years from now, I’ll be able to understand what they were telling me. But it’s a you know, it’s one of the problems we have with women’s health in general is there’s just not that many things that we know how to observe. And possibly the biggest contribution is going to be training these, whether it’s an optical system, whether it’s a probe that’s evaluating the metabolism of the embryos and the eggs by looking at the circulating factors within the media.
David Sable: But we have these means of assessing kind of a mechanism-agnostic observations. See as doctors and scientists we’re biased over trying to fit something into a mechanism. So we watched an embryo divide. We want the cells to be equal in size. We want them to reach certain static landmarks.
At times they all seem to reach the given time, and that seems normal to us. So two days after fertilization, the embryo should have four cells. Those cells should be just touching each other, but not sticking together. Three days later, it should be eight cells. No other noise within the embryo.
They should start filling in the gaps between themselves and the cells should start merging. It should be cell to cell adhesion. This is normal because this is what happens in the embryos that we know turn into pregnancies. Again, using human machine learning. What about the things that we’re not smart enough to observe?
Maybe there’s a pattern of variation of the thickness of the zone of placenta that we just it just never occurred to us to measure. Maybe there’s something else that our eyes can’t pick up, but if we use a different filtering system will light up like a neon sign to a computer optical system when it compares looking at these embryos with 100,000 outcomes.
So it’s in a way it’s you know, we’re waiting for the this new means of assessing data to tell us what we’ve been missing, which is what’s really exciting. But it’s also it points out our weaknesses. We can’t we’re not that good at telling the computers what to look for just because they’ve trained your super human eyes on it.
Harry Glorikian: Yeah, that’s. But that’s I think that’s happening in more fields than I can shake a stick at, where the the system says, “Hey, you know, I noticed this” or “I found this” or “Here, look here.” Or “here’s a pathway for something that you had not considered.”
Or if you’re going to make this drug, take this, you know, process. And the human never would have thought about doing that. You always have to check and recheck. You just you cannot assume the machine is just right. Right. And, you know, once you take it out of your lab and put it someplace else, it may need some retraining, you know, based on that population.
But I expect that this is going to, you know, we’re going to find things where people are like, I never would have thought of that. So now when you were there, though.
Because I’m curious, this was your first sort of AI in IVF International conference. Did you get a sense of, how do we the US, you know, and what we’re doing here compared to what’s happening on an international scale, is the best work on IVF and other technologies coming from the U.S., is it coming from other countries or are you seeing different pieces? Because, you know, I think about all the rules and regulations in different countries. They’re all different.
David Sable: Yeah. Thankfully most of it is in areas that are tightly regulated because they’re not touching the patient. And so they, we’re seeing, this particular conference, I saw great work from everywhere. It’s you know, you just kind of sit there and just in awe of some of these presentations, whether they come from Europe, Asia, the US. We had one embryologists we know very well from Mexico, just fabulous work on alternate optical systems.
And it’s, you know, the general level of excitement was really palpable because part of it was, these were data people and embryologists moreso than doctors. So that it’s, you know, doctors always we love the wisdom bit, the clinical judgment bit, the part that relies on our experience.
And we’re a little bit more stubborn about letting data push us off our preconceived notions and the concept of a unbiased observer with mechanisms to be to find later make a lot of doctors uncomfortable. Okay, doctors know what works and through repetition can comes ingrained.
What was exciting about this conference was just how open everyone, the doctors included, were to be passing the baton of data assessment away from just our own brains. Which is a real big step in clinical medicine. And I think part of it is in IVF, we’re all frustrated that for all the progress we’ve made, we’re still not as certain as we want to be. You go and get your appendix out and you wake up, your appendix is out. That’s pretty well certain.
In an IVF cycle, you can do everything right in someone who’s, you know, when they walk in the door, you think they have a extremely good chance of having a good outcome. And it doesn’t always happen. And that’s very frustrating for us. And obviously, a quantum higher frustrating for the patients that are going through it. So I think the work is being done everywhere. The nice part about this is that a lot of the work doesn’t, it’s not that costly.
A lot of it just comes from people that are really smart. And the the computational power is not is not a barrier. People can get processing power, memory storage. That all costs nothing nowadays. And if you’ve got a system in place to allow collection of data, then you feed it in and you say, What’s it going to tell us?
And the nice thing is that you’ve got different populations of patients in different areas. So, for example, the Israeli population of IVF patients may be very different from the population of IVF patients in Spain for lots of different reasons. And since everyone’s doing it in all these different geographies, we have an opportunity to merge these data sets to get away from the kind of the inherent collection bias that a lot of health care research is prone to. And I’m kind of drifting away. Am I answering your question?
Harry Glorikian: No, no, no, no. It’s spot on. I mean, I, I always think to myself, half of our battle is getting all, getting enough data in one place so that you can start to train these systems.
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And now, back to the show.
Harry Glorikian: Back in 2020, you said, look, I have my list of ideas. You’ve got your, you know, your chart. There were 23 things on it back then, if I remember correctly. I’m curious is. Especially after this conference. How long is the list now and. You know. What have you maybe gotten rid of that that failed or what is new and most promising?
David Sable: Well, the list has morphed into a map. As we we found as we were looking for solutions to the particular problems that we want to solve, we found that the solution is often very overlapping. For example, one of our big challenges is to move as much of the IVF process out of super overbuilt, very expensive laboratories as we can.
So the way we do that is by closing the engineering system from retrieving the egg to freezing it. We make it so that egg never sees the ambient air, so we don’t get all the things that we do in the 1000 square foot laboratory for air quality and things like that. We want to take that part out of it and we can do it if the egg never leaves the box.
How do we get the egg never to leave the box. We let the egg retrieval be done in places other than the laboratory. If we’re going to do that, we need to train other professionals to do the stimulation with the fertility drugs and retrieve the eggs. So we need to have non reproductive endocrinologist do these procedures.
Now, there’s nothing inherently difficult about them, but it’s just that they’re not trained to. So where does AI come in? Well, here we need to develop the decision support so that a obstetrician gynecologist without subspecialty training here can feed the data in to the system of the system, tell them what the medication regimen should be. And as the data points, the blood test, the ultrasound, or actually replacing the blood test with urine tests to move them into the patient’s home as that gets fed into the system.
David Sable: The AI algorithms tell the OB-GYN what the medication changes should be, so that everything can be done in all these different places. [There are] 45,000 OB-GYNs in the United States versus 1200 reproductive endocrinologists. So if we can move this to places that are closer to where the patients live, to doctor groups that may want to offer this as part of an IVF cycle, they may offer it at a different price point.
They may, they can certainly run that laboratory cheaper. They can put it into a procedure room which costs $75 a square foot instead of $1,500 a square foot. And so here we are setting out to solve a real estate problem. And guess what? Our tool is artificial intelligence. It’s a multiplexed sort of three dimensional model that we’ve put together.
And all these different places we find relying on computer vision algorithms, AI, machine learning to help us out is going to be the overall process optimize it. We want to get to a point you mentioned before, financing it. In my model, we’re going to get to a point where nobody pays for an IVF cycle if they don’t have a baby.
You know, it’s it’s like it’s just it’s one of the enormous unfairness of having done this for 20 years. A patient would say, Doc, this cycle has to work because it’s my last one. You know, we just mortgaged the house. There’s no way we can afford another cycle. It’s like you just you just feel like you’re just not worthy of this type of thing.
David Sable: So as the data gets better and better, and like the work that Dr. Yao is doing at Univfy, the predictive analytics, become more and more helpful, then it just becomes an actuarial equation. Then we underwrite, spread the risk. When we make the data that good, we can invite the insurance companies to do what they always do and they’ll say, okay, this is the kind of data we need. Insurance companies, they insure car accidents.
It doesn’t have to be a disease that people die of. And if the insurance companies don’t want to help you and AI can open an insurance company, it’s a heck of a lot easier than a lot of stuff that we do. So it’s just going to lend itself. So we want to solve the problem of better, more equitable cost and access. What’s the tool we’re going to use? Artificial intelligence. It’s really it just kept popping up all over that map in ways that it’s great.
It’s a great convergence of the technology. And let’s face it, AI is in its early years or early stage of its development as well. So these are going to these as these become increasingly sophisticated, it’s really going to accelerate that higher results, better access, better experience for people, and ultimately being able to take care of, you know, 10 to 20 million people a year and independent of where they live, what their geographics are, whether they can afford it, that type of thing. So it’s really it’s a very it’s a great convergence that way.
Harry Glorikian: Well, you’ve talked about this idea of opening up a Bell Labs of infertility, where the goal would be to standardize, optimize everything about IVF, eliminate the uncertainties, make sure that the success rate for the patient is as high as possible, you know, And that, you know, it’s funny, right? Every time I talk to somebody in IVF, they always say we want to have an implanted embryo that leads to the birth of a, you know, healthy baby.
David Sable: Right.
Harry Glorikian: So how do you. Are we getting any closer to your vision of a Bell Labs of infertility. I know it’s not under one roof, but, you know. Are we getting closer to it? You know. Are we on our way or you think we’re still at the early stages?
David Sable: Well, we really are, which has been, and the great thing is it’s happened organically. You’ve got the companies that are focused on AI for IVF of almost entirely. All of them have moved from point solutions to much more comprehensive tackling of each of these arbitrarily performed highly operator dependent things so that they’re now instead of saying, okay, we want to sell choosing the best embryo for $2,000 a cycle, they moved away from that.
It was it was never a viable business plan to begin with, but they moved away from that to okay, we’ve identified two dozen things in an IVF lab that we can gather the data on, process-optimize and just keep engineering the IVF process. And they’re selling this service or they’re pitching the service or they’re trialing this service in some very well run IVF labs. Secondly, we’re seeing some startups that are actually opening up their own practices.
You know, this is like it’s like the the tail wagging the dog. It’s like we’re going to build it from the start. We’re going to be we’re going to completely engineer things with the goal towards starting with very comprehensive data collection and trying to with each node of the IVF decision tree, figuring out what we should be doing on a database on a data basis, rather than starting from the traditional practice and trying to be convinced to deviate from it.
And which again, to me, if these things can happen organically, that’s so much the better because then you don’t need to convince someone who’s already busy, who’s already making a good living to try to do something different. And it’s important, too, because we’re trying to offer a different type of service.
You know, as I probably said in the last time, that, you know, IVF is the Four Seasons and the Ritz-Carlton right now and there’s nothing else. So we need entrepreneurs, founders, progressive minded doctors, scientists and data engineers to create the Airbnbs and the the Hiltons and the Sheratons. And as long as you get 8 hours of good sleep and a clean bathroom, that’s what we’re solving for. You know, it doesn’t have to be the $15,000 suite, the Four Seasons here in New York.
David Sable: So if we give people the same 65% probability of having a baby with a genetically normal embryo at different price points or maybe where the retrieval is done, one place the storage is done somewhere else and the transfer is done somewhere else, it allows the patient to move away from this kind of travel agent model where they go to an IVF center, they make all the decisions for them and give them one big bill to a model where they can comparison shop.
They can say, okay, I can do my egg retrieval with my OB-GYN here for X dollars, most of which was covered by insurance. I could send the embryos to a central cryo storage place and then when the time comes to thaw, the eggs, transfer, fertilize them, develop them and transfer one, we can go to an embryology lab. And the embryology lab, which likely in the future is going to have a lot of this engineering built in. It’s going to be mechanized as well.
We’re looking at ways to invent sort of the Da Vinci surgical robot for embryology. Whereas all these things that are done manually now by people that some of them are more talented than others, all of whom are subject to fatigue and the quality of their work tends to decline as the hours go by.
If we’re looking to scale up IVF to do ten times as many cycles, we need someone help them out. And if we can do it by machines that don’t get tired and marry the AI with robotics, so much the better.
Harry Glorikian: So I almost want to say like, who gets the world of fertility? And I guess what I’m saying is, is I see lots of people now from, say, the tech world or understanding AI saying, I can come in and, you know, come up with a process to develop a drug. Right?
I always like it when they’re married with somebody who actually understands the biology, but they feel pretty comfortable that, you know, the data is there for them to sort of move this along. Is that happening in fertility or is it still.
You know this only the people that really have been working in it and understanding are taking a crack at it. Are you seeing. You know, people that really understand the software coming in and saying, I can help solve this problem.
David Sable: We’re seeing a movement in the right direction on all sides. A couple of data points. One is I started investing in health care in the early 2000s, and there there was a real segregation between tech people and healthcare people. In health care, people just didn’t speak tech and they didn’t speak data. In tech, people almost to a person, tended to underestimate the complexity of biology.
You know biology is is, I am I am always overwhelmed by how complex it is. And that’s a function of the limits of my own imagination, intelligence, and just how complex biology is. And a lot of people from the tech side would come in thinking, well, it’s just one more data set, so let’s apply the same rules to biology. And we’d have a likely outcome at the pace that we’ve come to expect. And the pure data tech side.
And you know, over over the decades there’s been a gradual realization [that the] biology really is that complicated. And we’re seeing that in a microcosm on the people on the tech side that are trying to attack, trying to get into IVF. So they’ve gotten that really quickly on the medical side.
David Sable: The only semi positive thing I could say about the existence of COVID, this is something that happened shortly after you and I talked last time, is that in the middle of 2020 IVF in the United States and a lot of other countries just shut down. They turned it off. They didn’t do any cycles at all. The equipment needed for anesthesia and things of that sort was triaged elsewhere.
Nobody wanted to come in and be in the IVF centers for fear of passing the virus alone. So it’s kind of shut down and reopened that summer, the middle of 2020 or towards the late summer, they reopened IVF. And of course, what did we all do that ran IVF programs? We brought everybody back in.
And once we had this big backlog, we said, okay, let’s so let’s do these cycles, get the patients through, because we don’t want to keep the waiting list really long. And of course, when you want to have a baby, you want a baby, you don’t want to wait. And they very quickly found that they were operating very close to capacity beforehand. And if you try to increase this new gear, let’s just turn up the, let’s just make the assembly line go faster.
Well, guess what? The assembly line couldn’t go any faster. And they had a very quickly ran into the limiting factors, the number of doctors to do procedures, the number of embryologists to do procedures. And almost collectively as a field, everyone’s eyes opened up. And say, you know, this whole engineering thing makes a hell of a lot of sense.
So the difference in embracing the concept of using the kind of the textbook for innovation that we use in engineering really offers a hell of a lot more than we thought, than the traditional artisanal kitchen model of running an IVF program. So the the movement in the right direction has come from both sides, from two very different reasons.
But I think to answer your question is, yes, they really are getting it and working together with a kind of a it’s not a uniform vision, but at least a converging vision for what where this can all go.
Harry Glorikian: Well, there’s nothing like a good old fashioned pandemic to cause change, Right? Mother Nature has a way of of keeping us on our toes. You know. We’ve got a Democratic administration in power now. Right. And. I’m wondering if the politics around funding.
For all of this work, you know, has gotten any easier. I think the last time we talked, you said if there’s a sperm in a dish, you can get a grant. But if there’s a sperm and an egg in the dish, forget it. You know, the NIH won’t go near it. Has that gotten better? Is it worse? You know. Because this isn’t going to get funded all by itself. We, you know, every science needs federal funding or grant funding or things like that to move forward faster.
David Sable: Yeah, we need the basic science funded. There’s funding for the engineering aspects of it because it’s more modifiable, more predictable. As a person that does venture capital, if I can model it, I can put a rational price on it. If I could put a rational price on it, then we can find funding. It’s the stuff that’s not modeled, the stuff where we just have to do things and let the experiments meander along in a in a process of discovery.
In the US, in fertility and reproduction, it’s still yeah, there’s almost nothing there. In other countries you’re seeing a lot more openness to grant funding and things of that sort. But the problem is the pools of capital that they put into it are so much smaller than the US pool. But the big I guess the outweighing the the outlying factor here is the population replacement issue. And this is something I did a lot of work on in 2020.
I looked at the total fertility rates, which is the number of children that each woman of reproductive age produces. And the number, the magic number is 2.1. So every woman during her career is a potential mother. And just to put it in as dry terms as possible, in order to keep the population constant, needs to produce an average of 2.1 children, 2.1 because of early childhood mortality. The US right now is about 1.7. Taiwan and South Korea are about 0.9.
There’s a lot of parts of the world, including China. China is well below 2.1. So we’ve got a lot of countries that are looking at becoming where Japan is right now.
David Sable: Japan’s population actually shrinking. Right. So that there’s going to be a policy emphasis in all it will come to the US. It’s come to many other countries already. For decades, Israel does IVF almost for free until you’ve had two children. China has gone from a one child policy to a two child policy and is on their way towards a encouraging multiple children. To address this, in northern Europe, assisted reproduction, fertility is free on the consumer, essentially free on the consumer.
That’s covered. And it’s going to, you’re going to see that here in the US as well, because there’s going to be a policy incentive to try to compensate for the fact that in almost every developed population we try to get pregnant later, we still want children. But the the probability of having a child over 30 is so much less per month.
And in aggregate that results in just many less people being born, people in their mid thirties. There’s about four and one half million 32 year olds and four and one half million 33 year olds in the United States. Last year we made 3.8 to 3.7 million babies. So the people that are their prime reproductive years right now and the average age of first child now is 31 in the United States.
So we got four and a half million people in total making 3.7 million babies. That’s per year. And so you’re going to see a leveling off of population growth.
David Sable: As it stands, people are still passing away later and later in life. So you haven’t seen the population shrinking, but we’re certainly seeing an ageing of the population and that has far reaching effects on the economy, on the labor force to take care of the older people and the productivity per person year.
So you start breaking these down, there’s going to be a lot of reasons for the NIH to start funding experiments where you have a sperm in the egg in the dish together, and I think that’s going to happen. Now, there are other political currents that are going in the other direction, certainly, And with the Dobbs decision and Roe v Wade being repealed, without getting into the politics of it, there is a lot that’s up in the air about the way IVF will be delivered in the future.
And it is a very intensive debate about that. How much does this affect IVF? And from state to state, we’ll be the, efforts to change the way IVF is practiced, some of the laws that are being passed to restrict abortion get interpreted in ways that say, well, it’s restricting IVF also. So there’s a lot of noise around that aspect of it.
But in general, I think the overall trends, if we want to maintain our populations, maintain our economies, overcome the inherent difficulties of getting pregnant as we choose to do it older, later in life are going to point towards basic science work being funded. I just hope it’s sooner rather than later. Yeah. Very long winded answer to your question.
Harry Glorikian: Yeah. No, no. But I mean, you know, there’s there’s always this. Let me throw the pebble in the pool and nobody thinks about all the ripples that it’s going to cause as it’s going out because they, they haven’t gone through the science to understand the implications of what they’re doing and some of those implications, You’re like, No, that’s not exactly what I wanted.
But hey, you know, you got it because of what you did. So it’s an interesting situation. So moving on to a more exciting topic. You know, I always read your tweets or I try to keep up with them and. You know, you’re working on a book. I mean, you had a funny tweet with which I was very, you know, lucky to be part of, in the form of a memo addressed all your peers, saying “To all my friends and colleagues, would you please stop publishing books until I finish writing mine?” So. What’s the book going to be about?
David Sable: Yeah, I’m trying to figure that out myself, but I enjoy writing and you start going, and I’m sure you know this better than I do, because you’ve been tremendously more productive than I have. You start when you start writing about one thing, and then your brain tells you one thing, and your fingers are, the words that come out of your keyboard take you in a very different direction.
So right now we’re working on a, you know, just kind of driven into this cul de sac where I’m developing an idea about and this is going to sound totally ridiculous about how we’re going to use cervical mucus. Now, cervical mucus is one of the most unbelievably well engineered fluids in the universe.
It’s just it’s a extremely interesting in the whole world is rolling their eyes now say what could be interesting about cervical mucus but it’s the only externally available non-invasively obtainable fluid that changes what it does at different points of at different points of, in this case, of the month. And that change correlates with something that goes on inside the body that we have no other way of studying.
David Sable: And let me let me let me digress for a second and feel free to edit this entire thing out if it makes no sense. In medicine, one of the first things we learned is the difference between clean and dirty. You’re the body. What does the body want to do? It wants to protect its circulatory system, its nervous system, its skeletal system. So no bacteria, no viruses, no nothing gets in there. And in exchange, the skin, our skin is just covered with bacteria.
And there are other areas of the body that, upper digestive tract, the very tail of the digestive tract, nasal tract, part of the respiratory tract, we allow bacteria and things in that, and then we put these mechanisms of defense in there. And every one of these mechanisms is the same every single day of your life, every minute of the day. If something gets low in your respiratory tract, you cough and you sneeze and you’ve got special immune cells that go after it.
The stomach is filled with hydrochloric acid. So if a bacteria dares get through the esophagus, it’s, you know, it meets the death pool. So and these are evolution has put these things together extremely effectively. One of the things that evolution has given us is cervical mucus, because the upper reproductive tract is sterile.
The inside of the uterus is sterile, the fallopian tubes, the ovaries leading into the abdomen. That’s a pristine environment. So we’ve got this cervical mucus, which is like the bouncer at the bar. It’s like this incredible thick mucus doesn’t let anybody in. No, no, no. You’re not getting through that door because that’s a pristine area. Except, one part of the month, the bouncer stands back, opens the door a crack and says, okay, you, you, you, you, you, you can come in, the rest of you can’t.
And now all of you, you’re you’re allowed in and with this very nuanced, sophisticated way, they allow the sperm in for fertilization to occur. It’s the only part of the body, the only part of the body’s defense mechanism or the only border between clean and dirty that has a regular interval where it changes to allow something physiologic to happen. It’s it’s a brilliantly designed thing that nature has given us as a gift.
Now, where it gets interesting is we talked earlier about women’s health and how in women’s health there are many things to measure and all these things that go on inside the body. And I’m not just talking about getting pregnant, you know, things like pain from endometriosis, menstrual pain, what triggers early labor and things of that sort, [that] we have no way of studying. Well, the cervical mucus changes regularly.
At the same time, a lot of these pathologies occur that we have no way of studying. Which tells me that the cervical mucus probably has a different gene expression pattern. At these points that it does, when the bouncer is just sitting there keeping the door closed. So if we had a mechanism and cervical mucus is readily available, it’s easier to get cervical mucus than it is to draw a tube of blood. The patient can do it herself.
So there must be some means of studying cervical mucus. It’s going to unlock some of these diagnoses, some of these unknowns of women’s health. It’s going to be like a new, vital sign. Like this entire new area. It’s like Nobel Prizes. You’re going to, like, blow out of putting that AI probe into continuous assessment of the cervical mucus in ways that we’ve just never imagined.
Harry Glorikian: I don’t know why I think about molecular cartography, right?
David Sable: I’ve been writing about this for….
Harry Glorikian: I don’t know if you know, there’s a company called Resolve Biosciences. I have them on the show where you can actually see the gene expression patterns of which cells are doing what, depending on physically where they are. And I’m wondering. If that could be a door opener to understanding this better. But you know I’m I’m riffing right now.
David Sable: So it’s it’s so you asked me what I’m writing about like I think I’m writing about re-engineer the way we help people get pregnant. And then suddenly I’m knee deep in talking about what size probe we would have to put in the cervical mucus. And, you know, it’s it’s like these little esoteric detours. I have no discipline the way I write. So at some point it’s I don’t think anybody will ever read this stuff. But that’s why people like you who are so productive, you know, what are you on like your 12th book now?
Harry Glorikian: No, I think I’d be dead if I was that far. Somebody would either I would have killed myself or somebody else would have killed me if in my family. But no, I mean, but, you know, I have a I have a strong feeling there’s a book that’s going to come out hopefully soon that will help people understand, this area, the processes around it, you know, areas where it can be improved. I don’t want to say how to, but at least get more people in tune with it so that they can contribute to it if they so wish.
David Sable: Yeah, I hope that book comes out too. And I, I just have doubts as to whether I’m going to be the one writing it. I have to write the book. But. No, I appreciate the the implied vote of confidence that I may have some input into something like that. But it’s a fascinating intellectual area.
And, you know, it’s the I just need to get my published words per life, per hour of life lived ratio up a little higher. And because I think that more seriously, there is a there’s a hunger for this knowledge communicated in a way that can be absorbed in mass because the people that were trying to, you know, getting back to the core of it, the people that we want to help and the suffering that we want to reduce, people that can’t have children or people that have miscarriage after miscarriage after miscarriage is suffering is is say, as intense as anything in health care and in order to get treatment to them and also give them knowledge of what’s happening and hope that it’s going to happen quickly as well as keep them looking for these things.
It’s it’s important and it transcends our own personal contributions to it because there’s 20, 25 million people a year who go through either they can’t have a family or they have outcomes that are heartbreaking or they are genetically predisposed to have children that can die in their earliest years. We can make a dent in that. That’s that’s huge.
Harry Glorikian: Well, David, you know, I can tell you that, you know, after getting to know you, you’ve opened my eyes to so many things around here. So I cannot imagine that, your contribution to this space has been huge. And I am looking forward to a book that sort of helps, you know, outline this space and detail some things for most people. So I only wish you incredible luck and I look forward to reading it.
David Sable: Oh, well, Harry, you’ve been a friend for a long time. Love, love talking to you about these things, whether it’s over a cup of coffee in Cambridge or or over wi fi, and looking forward to charting the progress as we go along.
Harry Glorikian: Excellent. It was great having you on the show.
David Sable: Thanks for having me again.
Harry Glorikian: That’s it for this week’s episode.
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