Welcome to Fertility and Sterility On Air, the podcast where you can stay current on the latest global research in the field of reproductive medicine. This podcast brings you an overview of this month's journal, in-depth discussions with authors and other special features. F&S On Air is brought to you by the Fertility and Sterility family of journals, in conjunction with the American Society for Reproductive Medicine, and is hosted by Dr. Kurt Barnhart, Editor-in-Chief, Dr. Eve Feinberg, Editorial Editor, Dr. Micah Hill, Media Editor, Dr. Pietro Bortoletto, Interactive Associate-in-Chief, and Associate Editor, Dr. Kate Devine.
Welcome back to Fertility and Sterility On Air. We're at the final day of ESHRE 2026 here in London. I have the pleasure of having Steve Rooks here with me.
We're talking about a really interesting paper on cost of baby as a percentage of median household income. This was from some leaders around the world who many of our listeners will be familiar with. Steve Advises Conceivable, many of their leaders were on this paper, as well as David Sable and David Adamson, who has led some international efforts in IVF, tracking IVF, expanding IVF access.
Steve, this was a fascinating paper. Tell me at a high level, what is it that you wanted to address with this study, and how did you go about doing that? Thanks for inviting me to talk. It was an effort to really better understand.
I've always been a fan of David Sable's and David Adamson's. David, with his cost to baby focus is really, really important to think about it from the standpoint of the patient. David Adamson's focus on trying to understand how to drive improved access and affordability, etc., looking first at the affordability standpoint in terms of cost per cycle to see how that varied around the world.
We decided to bring both of those together and look at cost to baby, but within each country in terms of the cost to baby as a percent of median household income, because that's a better measure of affordability within a given country. Then look at that to determine how does that drive IVF utilization. We looked at it both ways, both in terms of the typical relative measure of percent of babies born via IVF, as well as IVF babies born per 1,000 females of reproductive age, because that's the absolute measure.
We looked at it both ways. It sounds like to me, just to think about it through what you're saying, that we know some things about cycle costs and how they vary, live birth and how it varies, but what's unique about this is you're looking at cost to baby and you're looking at it as a percentage of household income and using two different approaches to try to gauge that. Exactly, to gauge how well, how does that drive the usage of IVF or the ability of the typical family in a country to afford IVF to have it.
We all qualitatively sense that there is a clear relationship between the cost and whether a family can afford it or not and how much it's being used. We wanted to put some hard numbers against it. By doing this, we were able to actually show, and by the way, we got all the data from all the registries.
We did surveys of clinics in each of the countries. We did a deep analysis of how well, and we did both gross cost to baby and net cost to baby. Gross was the sticker list price for the clinics, and then net out of pocket was based on the insurance coverage, tax credits, a range of other factors that lowers the actual out-of-pocket cost for a patient.
There was a lot of research that went into it by country to get a sense of the net and gross because the gross dimension says how well are the clinics reducing the effective cost to deliver the service? The net says how well is society in that country enabling families to afford that gross price by reducing the effective cost based on income and things like that. We looked at it both those ways, and we saw a strong power law relationship, a strong regression. You're looking at an R squared of 0.8 plus, and it clearly showed.
Of course, the usual high usage countries were there on the power law. You had Israel, of course. You had Japan, Spain.
These are the countries that are leading the way? Leading the way. Spain and also South Korea. Then in the middle of the pack, you had Canada, the United States.
Then at the far tail, you had a lot of some of the Asian countries, African countries, some of the Latin American countries, and where they are both high cost as a percent of medium house income, and there's not a social support for IVF. The key thing that we also wanted to do, and we've published the detail here in a paper that hopefully will be published soon in one of the leading more global journals, is to look at the best practices. What do each of the countries do that are leading the way that really enable a lower gross and net cost to baby? Because we looked again at both of those measures.
Israel, again, leading the way because they both have a fairly low cost in the clinic side in the way they're structured there, but they also, of course, strong social support with three IVF babies being covered. Then you have countries like Spain. Spain doesn't necessarily have a strong social support.
It's free and public, but the public clinics are overloaded. Much of it goes through private, but they've got, because there's such a large infrastructure, and I think you mentioned this point once about it's one thing to try to lower the cost of baby, but unless you've got the infrastructure, the IVF clinic capacity there, you're still going to have a wait list. They have much more effective capacity.
One measure that we did that's in the is looking at effective fertility specialist capacity. In Spain, there's not necessarily a formal REI fellowship. You've got OBGYNs who are doing it.
That's a little controversial in the US, but in Spain, it's one of the ways that they enable more capacity. Sure. Yeah, that's interesting.
I'm a scientist, not an economist. I don't understand this stuff as well, but reading you and David Sable and other people, and you talk about the infrastructure in Spain, it seems like there's some economics of scale that are at play here as you build the infrastructure, as you do more cycles, it almost enables you to then do even more cycles and be more cost effective. Is that accurate? Exactly.
One of the things that we really want to do is not just do some nice analysis that gives a paper. It's saying, again, here are the five to 10 steps that countries can take given best practices to really lower the gross cost of baby, and here are the five best practices on the social side to lower net cost of baby. Things like effective tax credits, which is something that in Canada, more of our provinces are starting to do to not necessarily just subsidize, but enable a reduction of your taxes paid to allow you to afford it.
There's a range of different things, and it can be things like a country enabling tax credits to clinics to utilize new technology, for example, rapid depreciation. If you were to get the latest and greatest piece of equipment that's going to help you drive more efficient scale, then you could depreciate that and allow you to bring down your costs of charging. One of the limitations, but applications I was thinking out of your study is this is almost like a satellite view looking at each country.
It's very high level. You summarize, let's say, the United States. Even within the United States, my state of Maryland is very unique where about half of my patients have insurance, half doesn't.
Even within our practice at Shady Grove in Maryland, some of our areas have very high utilization, high conversion to IVF. Some have high insurance, but lower conversion, lower incomes. Even within my own practice within Maryland, it's very different.
I'd like that your paper gives us this global view that we can use at a country by country, but do you think it's almost like a roadmap that one could take back to their local region and use the same analysis to try to maybe take to the state legislature in their state, for example, the US? Exactly. Well, you're reading ahead of us. We actually have a version of this that we did for the US by state.
Wow. We've got really good data on using CDC. We were able to get both the absolute and the relative percentage of babies born with IVF versus the cost given the clinics in those states as a percentage of median household income in that state.
We got it down to the state level. There's quite a range. The overall average on a percent of babies born via art is about 2.6% in the US, but it ranges from about six and a half in Mass and also Maryland's up there too, down to half a percent perhaps in Arkansas.
There's a wide range and we're doing the same approach to drill down. While David Adamson will present the poster at ASRM, definitely go look at that poster with David. We're working on publishing a paper that goes into more detail in the same way.
One of the interesting things that we're able to do there in that paper by drilling down in a state regional level was looking at REI density. Again, it goes back to that notion. You can't just lower the cost of baby.
You've got to effectively expand the REI effective capacity. That doesn't necessarily mean adding more REIs, but it will. It's a piece of the puzzle.
But it's helping REIs be more efficient so they can see more patients, etc. We're going to provide a roadmap in the US around here are the things that we see taking best practice from the world and what would it mean in the US. If let's say a state in the middle of the pack wants to get up to 10-12% art utilization, what do you need to do on the clinic side and on the state side? It's very clear the distinction between mandated states and non-mandated states around that utilization.
We definitely need to catch up at ASRM and have another chat to follow up on this because I would love to drill down on that picture within the United States for our US colleagues. I can just imagine the Center for Policy and Leadership within the ASRM being able to take data like this, give it as a toolkit, part of the toolkit for each group that's working with their local state legislatures. Any other take-homes for our listeners? Any other high-level global view things that you thought about from this research that we can do as a field to help improve access? Yeah, I mean it does come down to taking a mindset of what can we do to try to lower our effective unit cost.
And I think in the US in particular because there'll be a bit of a ramp to get more REIs into play is thinking about what can we do to make the healthy REIs be more efficient. And that would be, hey, the ideal patient journey is one REI consult and straight into treatment within 40 or 50 days. That's not happening.
So for each clinic to say, what are we doing that's preventing our patients to have that one effective REI consult, all fully worked up, male factor two, fully educated, and straight into the REI and then straight into treatment. That's what I would suggest is the key thing for any clinic to be asking, stepping back. And that comes from a lean mindset where I came into fertility with a lot of experience in lean and it's all about, you know, folks like Paco is kind of using lean as well too.
And it means stepping back and saying from a patient standpoint, what is wasteful in what we're doing in that journey? That's not adding value to the patient. That's not getting him into treatment faster. Yeah, no, I hear you.
Probably 90% of my patients, that's the first question or one of the first questions they asked me during that initial consult. How long is this going to take for me to get to treatment? By the time they get to me on average, they're two and a half, three years into their infertility journey. They want a baby today.
They're ready to go. The other 10%, they just want to know that everything's okay. They want to be reassured.
Yeah. So yeah, that's great. Well, Steve, one of the things I appreciate about you, when I talk to people sort of on the business side, and I'm trying to get educated myself on this as a scientist, you're very data driven and critical of data and the approaches.
And so I, I appreciate that from this paper and from you and the people you work with. So congratulations and thank you for swinging by and sharing with us. I appreciate that.
Thanks so much, Micah. All right. Really enjoyed it.
We'll see you at ASRM in Baltimore. And David should be part of that discussion too. Absolutely.
Always love talking with David. Welcome back to Fertility and Sterility On Air. I'm Mika Hill, your media editor, and we are here live on the very last day of ESHRA in London, 2026.
And I invited one paper from F&S that was from actually just last month that we just found fascinating. We'll have that in the journal club live from Chicago next month at MRSI. It's the intrapatient variability and the number of retrieved oocytes and ovarian response categories between consecutive IVF cycles.
And my guest today is senior author Ariel Huchwitz. Sorry, sir, if I said your name wrong. It's a pleasure to get to meet you in person and thank you for coming by and sharing this.
So just tell me, what was the question that you asked? Because this was, this paper challenged my thought process. So what was the question that you asked and why did you think it was an important question? Yeah. Thank you for this invitation.
And the question arises, in fact, we are working with algorithms to improve fertility treatments by using or implementing AI analysis and AI capability to improve our practice. And one of the things we were trying to develop is an initial dosage. Right.
What is the correct dose to optimize for these specific patients to be personalized and to be precise medicine, which we believe in. So we were asking ourselves how to develop. We had some challenges there.
There are a lot of biases in these things. It's interesting by itself. One of the questions we saw and we knew that the results of those initial dosage predictions, because what is the initial dosage? You say, I will give this dose, I will get this number of oocyte in these specific patients according to a baseline characteristic, AMH, AFC, age, BMI, what we all know for many years, all those calculators that were out in the market.
And we were asking ourselves why the results are not so good. And we understood that one of the reasons is a biological reason, because based on those parameters, baseline characteristic parameters, you give the same and you end with different results. And we were asking ourselves, okay, so we understand that we have to admit that initial dosage prediction, the number of oocytes we have to this specific dose, you have this kind of selling effect.
And we were asking, how can we quantify it? So what we did, and this is from where it comes, okay, let's check about this intercycle variability that it comes all days in our practice. We know that, that sometimes you have a patient, you run the operation and you get 10 oocytes in the next cycle or four oocytes in the next cycle, you have eight oocytes and you don't understand why. So within the same patient, we have- Within the same patient, the same time, I mean, one, two, three months apart, same, exactly the same patient, different results.
Right. So we were saying, okay, the initial dosage, you have a ceiling effect. You cannot get the R square of one.
You can get to a maximum of something. And this is of the question. And from that, it arises, in fact, the intercycle variability article is coming from there.
And it's very intriguing, of course, how can we improve that? I have an idea, but, and then, so this is where we come from. Yeah. And then we said, okay, how will we do it? Yeah, so how did you do it? Because this is a great question.
I mean, you guys had this multinational group. You had Europe, you had Israel, you had the US, you had- So we took advantage, exactly. So we took advantage of the fact that, as I said, we are developing algorithms for AI, but AI, as you know, you need big data.
It's true. But very important, it's not related directly, it should be good data. One of the main thing for success in AI or in every dosing is the quality of your data.
And the quality, you know, we are physician, the recording is not always the best. There are different EMRs, and you have to understand the data to take them and analyze them. And the first thing is to clean them.
Right. And to understand where you miss data, how you correct it, where you have bad data, how do you correct it, to understand the data and cleaning, this is of great importance. In any case, we had those big data because of these algorithms that we are developing, and I said, let's take advantage of that.
Yeah. Let's analyze. So we found in this big data, and we had the three continental, we call it, from Europe, from the USA, and from Israel.
And we took all this data together, and we looked at this data, how many patients we have really two identical cycles within six months, not to go into the decline of fatigue that we know, especially for other patients, and to find those identical cycles. And by identical cycles, I mean exactly as much as we can. And we make sure that it's, of course, the same woman, less than six months, and exactly the same protocol, the same treating physicians, so the same practices are kept in place.
So the woman has her own control. She's doing the identical stimulation, dosing, type of cycles, same position. Exactly.
Same dosage, same protocol, same physician doing the pickups and treating them, it's deciding when to trigger all this, the same, see, trying to be as much as possible identical. And we found 800 cycles. Right.
So out of tens of thousands of cycles, we get down to just under a thousand, but the quality of our comparison and our control group is going to be very ideal. What did you find? This is the advantage of big data, that you can be more selective and more precise about you. So what we found was surprising, not very much, but yeah, we found that there is a variation of about 60% in the number of all sites.
We did it in person because you cannot do absolute numbers, because if a woman is 10 and then she has 12, so it's 20%, but if you have two and then she has four, it's 100%. So we are talking about percentage change. And we found that the average is 62.
I think that the number of median will be, because of the statistical things, will be better to look at. And it was 40%. And this is why we mentioned those numbers.
We think that the median will be better and it's 40% change. But you know, I know when you say 40%, it's plus minus 20%, because it's from one to another. So if you're in the middle, you will move.
So if she is, she had four and six, the median is five. So she changed 20% to each size. So 40% change between cycle, it's about plus minus 20% I think.
This is the way I see it. So this is the change. And I think this is the real change we have in our patients.
Yeah. That's interesting. And from what I remember from reading this paper, the change was both positive and negative.
Yeah. This is very important. So we checked, of course, the first thing.
So we take the first cycle in time. I mean, the first cycle and then the next one that was later on. And we check if the change from one to another is increasing or decreasing always.
It was a half. And therefore, when you compare the numbers, the average number for all cycle, the first cycle, I mean, the second will be the same, right? The average. Exactly.
Because it's going up and down. So if you did that analysis, you would completely miss this, what's happening. So for our listeners, I think this is important because when I first read the abstract, I was thinking, well, yeah, everything's the same, but maybe we still learn something from that first cycle.
So maybe they triggered them a day later or a day earlier, but we're not seeing this change is always better the second cycle. We're seeing it's equally better, equally worse. So that's showing it truly is a variability within the patient from cycle to cycle.
Is that accurate? Completely. I wouldn't say that we don't improve. I hope that we improve a lot.
And I'm sure that we are trying to do better, but this improvement probably is not very important compared to the big change of the inter-cycle viability, the biological thing that we cannot overcome. That's life. That's biology.
And biology, that's the way. It's not a, it was a machine. It wasn't happening.
It's a biology. That was going to be my next question for you. Do you think this is just random variability in how many anthropologicals come along each month from woman to woman? Do you think that's what this represents? I think that's it.
This is the thing. And of course then comes how can we deal with it? Right. Yeah.
I think it's the question of ways of primordial follicles that begin to grow, you know, three to six months before. Now we are just doing the best we can in what we, with what we have from this primordial follicles growth. We begin to know it's not true that we don't know all these pathway, the impulse signaling, the AKT, all these early pathways that begins the growth of this primordial, but we don't really influence it.
Right. We don't have control on that. It's a result of what happens and we cannot really change it.
So we can take advantage. So one of the question is whether we should wait to have a better AFC count on day three. I know some doctors that do that.
I never have, but I've had patients ask me. Yeah. So I'm asking myself, we try to look at the data and to check if we are trying yet.
I believe that the AFC should be a good indicator of this specific cycle. Yeah. We know the KPIs and AFC is important and so on.
Unfortunately, we are not yet able to show the AFC index of that cycle will be a very good predictor and then we will be much more, maybe doing better waiting to a good month of good AFC cycles, but it's a way we should go. I think that one of the problem we have, at least with our data, is probably the AFC quality, technical quality of the AFC in our data, which is not the best probably. And therefore it's difficult to show that, but I believe in it.
Right. Yeah. No, that makes a lot of sense.
You know, one of the paradigms that my mentor, you know, Alan DiCiardi taught me and now I teach my fellows is that your best predictor when you're in your second cycle is no longer age. It's no longer AMH. It's what you did in your first cycle.
You guys didn't exactly address that question, but this at least challenges that paradigm. So, you know, I know a lot of your authors, Nikos Palisos, Eduardo Harrington, I talked to Elisa Hochberg. I was asking them, maybe can we do some modeling and see, Eduardo and I have looked at prediction modeling for our US fertility prediction kit.
We're doing a SART predictor model. And so one of the things I'm hoping that your group, maybe ours groups can collaborate on, or somebody can look at in the future, how much does that previous cycle play into prediction? And should we add that to these models if someone's already had a previous IDF Yeah, I'm sure now I completely agree. So when you look at the women and you ask yourself, okay, what's the outcome? This is the first discussion, the consultation in the beginning.
We want to know what will be in discussing together. I think that there are really two now main parameters. One is what you did in the previous cycle, if you have one.
And one is the baseline characteristic. What is better? Right. A great question.
And you'll write it and it's very easy. And yeah, it's very stimulating. So I will tell you, we did in our ideal model, we call it ideal model, false model, that we found.
So what we did is a correlation between the number you have in one of the cycles to the mean of those two cycles, right? If you have four and the mean is six, so what's the correlation to achieve? And we say, okay, an average between those two cycles is the ideal thing. Four and one, six of the five is representing the best, this patient. Let's look one, three, four and one, three, six.
So we achieved a 0.83 and this is the ceiling effect we call it for the initial dosage. And it makes sense. So of course, if I had four cycles, maybe it would be better, but this is what we have.
And we looked at three cycles with less data. Yeah. This is one of the interesting things I learned that I was not aware of this foresight model and the ceiling effect.
But for our listeners, I had to educate myself on this. Essentially it's, there is a ceiling for what a prediction model can get to, because there will always be biologic variability. And so this kind of gives us an estimate of where we think that ceiling might be.
Exactly. So you have this parameter of the previous cycle now, and you have, you're looking for the second cycle now, and you have those Bayesian characteristics. What is better? So I can tell you that for me, the previous cycle, I will have to run it.
It's easy, right? The previous cycles compared to the next cycle, not to the average. And to see the R square, I see it will be less than 0.83. We know because the mean is 0.83, so it will be less, maybe 0.7 something. I can tell you, because as I told you, we are developing a lot of algorithms and we developed the initial dosage.
It's something we did and we achieved initial doses based on the Bayesian characteristics, AFC, AMH, BMI, and H. The maximum we achieved after really doing a lot of correction to selection bias, because you have to remember that physician gives higher doses to all the patients, and you get less all sites in those patients. Then something that we do, you have to correct for it. And you have problem, what if, because you gave to a patient only one dosage, and what if she will receive the second dosage? We don't know because in the data, she receives only one.
In any case, after correcting everything, we did a sigmoid approach, a monotonic constraint, that doesn't matter, but we achieved 0.66. So it will be, I was taught by you, that the previous cycle is the best indicator, but now I think they're about the same. So ask me what my feeling is, we're going to analyze it and do an article just to see what's better. Perfect.
That's what I asked your coauthors to please do. I want to see what the data looks like. You will be, completely.
So we will do that. We are going to, because we have this initial dosage, so this is the best way to analyze and predict the numbers mathematically based on the Bayesian characteristics. So we have this initial dosage algorithm based on AI, and we'll compare to the previous cycle, a prediction to predict the second cycle.
And let's see what's better. I believe they will not be far one from another. Now I was, I thought the previous one was better.
Now I think it will be about the same. We have a discussion between us, what will be the best predictor. I actually think you're probably right on that.
And I guess we'll see what the data shows. What I loved about this paper though, is it sort of challenged or at least shifted my paradigm slightly. And I'll give you my take on point.
And then you tell me yours. To me, I would say this is overall reassuring data. Yes, there's variability.
It's plus minus. I think as the physician, if you get a good first cycle, tell your patient, we're going to do the same dosing. You may get more, you may get fewer, there will be some variability, but overall it's going to be good.
It's going to be within a plus 20% on average range from that first cycle. So as a physician, don't stress too much about the cycles a little bit better, the cycles a little bit worse. It's reassuring for the patient.
As long as you're doing the things you know to do as a trained physician, there's going to be some variability each month. You can't control that unless as you say, we end up learning that maybe we should do an AFC to start a cycle and then go to the next month if it's really poor. So I found it overall to be a reassuring take home message for physicians and patients.
What was your overall take home? Exactly. I think that in addition, of course, this is a very important point. And I would say also that we have to be modest and to understand that we are limited in our understanding and that when we define a pre-responder for the first cycle, be modest.
I always say you cannot learn from one experience. You have to do a second try and it might change not only because of protocols or because of you. You will try to improve the protocol.
We're all doing that. But be modest and let's give a chance, a second chance, what we call, and try again. And there are good chances that you will move from a pre-responder category because we in our article, we checked, we didn't talk about it, but we checked the movement from one category to another.
And again, about almost 50% were moving from a pre-response to EPO response to normal response or to IP response, those classical categories. And there was almost 50% moving from one category to another. Amazing, right? We say she's pre-responder for one cycle, but in fact, in the next cycle, she will be EPO responder or she was EPO responder, she will be normal.
Usually they're not moving to, they're moving one category. Most of them. Yeah.
So the changes. It's modest. Yeah, exactly.
So be present. Don't be too assertive and give it a second chance. This is what I learned.
And really, I would like to learn more about the AFC prediction capacities, because this will be, I think, something that we can do. How to combine, when we evaluate a patient, to combine those both things together, not just to compare them, try to combine them as the paradigm for defining their viral reserve will be also a challenge. Yes.
I love that take-home message. Be modest, be humble, use all your tools you can to improve the patient. Well, I love this paper.
I really appreciate you and your team doing it. I'm excited to do this as a journal club next month. So we'll go, we'll spend a whole 45 minutes diving deep into this article in Fertility and Sterility Journal Club Global.
Good morning, everyone. It's day three of ESHRE. I'm here with Micah Hill and Agata Zielinska.
Her title was Restoring Chagosin-1 Prevents Chromosomal Errors in Aging Human Oocytes. Good morning. Good morning.
Very nice to be here with you both. It's great to see you and meet you. We're really excited to hear about, learn about your research this morning.
Yeah. So I had the privilege of reading your presentation, asking you to come here and actually watching your presentation. And I think it's really fantastic.
So for our listeners who are not at ESHRE, can you just give us a quick summary of what you did and what the scope of the research was? Absolutely. So what we're interested at Ovalabs is developing therapeutics that reduce IVF failure, especially in women of advanced reproductive age. And that's, of course, a very important topic because when you're in your 30s, the success rates reach 40%, but once you reach your 40s, it's as little as 13%.
And the challenge is that in most countries in the world, most women belong to this advanced maternal age group. So for instance, in the UK, 63% of all patients start IVF when they're above the age of 35. And the challenge there is that the success rates are so low because women are born with all the eggs they will ever have.
And that means that the eggs age alongside the rest of the female body. Now, because the eggs age, that means that when you're trying to conceive in your 40s, you really have to rely on the cell that's multiple decades old. And those older cells, they make more frequently errors when they segregate chromosomes.
And that leads to an abnormal chromosome number, aneuploidy. And if you start with an aneuploid egg, you simply can't produce a euploid embryo that can produce a viable pregnancy in the majority of cases. Now, from a clinical perspective, there's actually nothing you can do right now to improve the quality of the egg when it comes to its chromosomal status.
And this creates a huge challenge, especially for slightly older women who are above the age of 35, because they simply have to go through multiple IVF cycles. And what we are working on is developing strategies to improve the quality of the human egg so that it's more likely to inherit precisely the right number of chromosomes and produce a viable embryo. And in my talk, I was focusing on our lead therapeutic, which is based on Shugoshin-1.
Now, this therapeutic addresses a key mechanism behind egg aging, and that is when women become older, the amount of chromosome blue in the egg that is supposed to stabilize the chromosomes goes down. We were able to identify that Shugoshin-1 is a key factor that declines with age in mammalian eggs. And when you introduce Shugoshin-1 back into the egg to a level that would otherwise be seen in a young woman, you can really improve the fidelity of chromosome segregation, and you can reduce the frequency of this key error, which is called PSSC, premature chromosome segregation.
And in essence, what this means is you help the egg to have the right chromosomal status before fertilization so that hopefully those eggs can then produce more healthy embryos. And is that at the level of the sister chromatids, or where in the chromosome does Shugoshin-1 work? So Shugoshin-1 protects what's called centromeric and pericentromeric cohesion, so it localizes precisely to that part of the chromosome where the natural blue is also localized. And in this way, it can protect the complex so that the chromosomes don't fall apart prematurely, but you have beautifully cohered the sister chromatids until anaphase onset.
And so that's both for meiosis-1 and meiosis-2? So it's those errors, PSSC errors, they really manifest themselves as aneuploidy after anaphase-2. But what's really important to remember is that cohesion gets deprotected during transition from meiosis-1 to meiosis-2, so during anaphase-1. And that essentially means that if you want to most of age-related aneuploidies, you really have to intervene before the egg has extruded the first polar body.
And so when we do IVF, we're trying to get eggs that have already extruded that first polar body. So tell us a little bit more about what this work does and how you did it. So one of the key benefits of our strategy is, for us, the egg itself is a patient.
So this is not a systemic intervention, but it acts ex vivo. Now, this means that you don't really have to worry about systemic exposure to the patient and the potential side effects. It also means that you don't have to worry about impact on future fertility because we're not in any way interfering with the ovary or the rest of the reproductive system.
And what we do is we introduce a small additional step in IVF because we deliver the therapeutic in vitro to a slightly immature egg through an ICSI-like microinjection. Now, we've optimized those microinjections so that we can really deliver picoliters of solution in an incredibly precise manner, but we introduce it before the polar body is extruded. And this is in order to prevent also those meiosis one errors.
So you were putting it into the culture media or directly into the immature oocyte? We deliver those therapeutics directly into immature oocytes, and this allows us to ensure that the amount of the drug that enters the cell is precisely what we intend it to be. Right, because if you put in too much, you're going to get too many chromosomes not segregating, correct? As with any therapeutic, any intervention, there is what's called like a therapeutic window, which means that there is a level of the drug that gives you the positive effect without side effects. And we're able to identify this therapeutic window is relatively broad.
So this is not something that's of concern. But nevertheless, of course, if you were to overdose it in 100-fold levels or above, then you would over tether the chromosomes and prevent their segregation. But nevertheless, this therapeutic window is really quite broad, and we can very reliably deliver those picoliters amount of the drug directly into the egg before it segregates its chromosomes.
So you injected Shigoshin-1, and these were M1s? So we have been injecting both GV and M1 oocytes, but from the therapeutic, so it works in both cases, because what's really important is that you build the therapeutic level of the drug, which in our case is Shigoshin-1, before the egg segregates its chromosomes. And from that perspective, you can do it either in GV or in M1, and we're very lucky because those eggs are a byproduct of standard IVF procedures. So under patient consent, we've been very fortunate to have screened that in more than 1,000 human eggs from more than 300 patients today.
But nevertheless, from an applicable point, it would be most beneficial to deliver it just before the polar body extrusion, so late in the M1 stage, so that you limit the amount of exposure to in vitro culture. So unlike Eve, I didn't get to attend your presentation. I'm dying to know, what were the results? What did you find when you did this? So as in cases of most of fundamental research, we started this work with mice to really identify what factors are important to protect centromeric cohesion.
And we've identified that Shigoshin-1 is really a key player. Now Shigoshin-1 has been known to be important in protecting cohesing complexes in mitotic cells, but it was not known that it plays a role in mammalian meiosis. And when we then delivered Shigoshin-1 into mouse eggs, we could almost completely eliminate this phenomenon called PSSC, that is the highest driver of unemployed in aging eggs.
So in mice, we're able to reduce the fraction of eggs with those chromosomal errors from 61% to less than 10%, which is of course a huge improvement. And what we've been working on since is translating this technology to human eggs, which of course is always more tricky. But nevertheless, through a lot of optimization that we've done to date, when it comes to the increase in efficacy that we can achieve in human eggs, in our pilot cohort, we're able to increase the fraction of healthy eggs from 47% to 71%.
And these are human eggs. So we were able to achieve almost a double improvement in the quality of healthy eggs. In the mouse, you've already had development of these eggs to blastocyst and to pup stage.
Where has this gone in the human so far? So that's correct. We do a lot of safety and efficacy evaluations in the mouse model because when it comes to this particular defect, so this unemployment that's driven by chromosomal dissociation, the mouse model actually mimics this particular issue very well. And in this case, you can do very robust studies to test the safety and the efficacy of your therapeutic.
Now, when it comes to human eggs, what we really had to validate is that it works across a range of patients that are treated with different IVF protocols that have potentially different ancestries and also different disease etiologies. And this is what we've been focusing on extensively. So we work now with 13 IVF clinics from seven different countries that provide us with the immature eggs.
And we're very happy to see that this effect is consistent across those very diverse patient populations. And now from the safety and efficacy perspective, we're going to the next steps. We can do a lot of this work on activated eggs, so on parvenodes.
But of course, the next step would be to progress towards clinical trials and then to work with the regulator requirements to get to a data package that allows you to taste this safely in humans. And why do you think that you're able to reduce it to the 40% range of aneuploidy, but not to less than 10% as you do in mice? So from our perspective, the fact that we get this almost twofold increase in the fraction of healthy eggs in humans is already amazing because this would give an average IVF patient close to double the number of healthy eggs they can use for subsequent treatments. But it is true that we can't get to this 10% as in mice.
Now, one possibility is that we still need to optimize the way that the therapeutic works to achieve maximum efficacy. But the other reason may be simply biological. So in mice, most chromosomes are identical or very similar to one another.
They're all what's called telocentric, so where the centromere and the kinetochore is always at the end of the chromosome, they're of a similar size. Now, in humans, this is very different because the chromosomes, they have a huge range of sizes. And what this means, there are those very big chromosomes and the tiny ones like chromosome 21, for instance, but also chromosome 22, chromosome 90.
And they are prone to aneuploidy for an additional mechanism, and that is due to loss of arm cohesion. And Shugoshin and any other factor that protects centromeric cohesion is not going to play a role there. So one possibility is that we can still perhaps optimize it further to get a slightly higher improvement.
But I think it's unlikely that we can get to 10% with just a single therapeutic because in human eggs, you have to be acting on multiple pathways because aneuploidy doesn't arise through a single mechanism. But that's exactly what we were working on. So we have an additional therapeutic in our pipeline that tries to address the different mechanisms.
Yeah, that's fascinating. One of the questions that Eve sort of alluded to at the start of this is a lot of these errors happen in meiosis I, and you sort of said that, and targeting at a phase I. But most of the eggs we get during IVF conventionally are M2s, and so you've already had that take place. From a clinical workflow standpoint, what are the things you're thinking of as you think about this as a therapeutic that would fit into our current models of how we do IVF? This clinical implementation is exactly what we're thinking at very actively at Ovalabs because what we're trying to do is to introduce very minute changes to current IVF protocols so that this can be applied in labs across the world with minimal amount of effort and changes to how IVF is done.
But nevertheless, if you really want to reduce age-related aneuploidy, you have to act on the egg before the first polar body is extruded because otherwise we've missed the window of opportunity. And this is really why currently IVF success rates are quite low for older women because by the time when you get the egg out of the ovary, those errors would have already been primed in that egg. There are two possibilities that one can consider.
So one of them would be to go through IVM in vitro maturation. And in that case, you can retrieve those more immature eggs and do the maturation in vitro and apply the therapeutic at that point. But what's probably even more interesting, and that's something we're focusing on, is to deliver the therapeutic just hours before the polar body is extruded.
That would require retrieving the eggs just slightly earlier. And in this way, you can still rescue aneuploidy, but actually limit the amount of in vitro culture and also limit the extra time you're adding to current IVF protocols. So you think maybe like a day seven trigger for an older patient, and I would think that we probably wouldn't apply this routinely, but in someone who has a higher degree of aneuploidy than expected or failed IVF cycles, could you envision a protocol where we would trigger women on day five, six, or seven to try to get eggs that are somewhat developmentally competent, but have not yet gone through meiosis? Or how do you envision this clinically? I think that would certainly be the first step.
So starting with patients who have failed multiple IVF attempts or starting with patients who are really of that vast maternal age group, and they have very low IVF cycles. So that would be the idea for the pilot study. And this would be done exactly as you've envisioned yourself.
So essentially retrieving the eggs earlier or changing the duration between trigger and the egg retrieval. But going forward, if this is effective and one can really achieve higher success rate, one can of course envision that maybe it would be a global change in how IVF is done so that you routinely retrieve the eggs earlier and make sure that you optimize for egg quality before fertilization. And do all of these patients, do you envision that all of these patients would then have to do PGT to look at the downstream effect of this? Or do you think it would correct it to such a degree where we wouldn't necessarily have to do PGT? So in an ideal world, this technology is aiming to reduce egg aneuploidy to an extent that you really get a high fraction of healthy embryos.
And that means that you perhaps don't even have to screen for aneuploidy if you can really reduce the degree of errors to such a high extent. Now in the experimental setup, and when this technology is introduced to the clinic, of course, you want to do ultimately all the testing to make sure that this is safe and effective, and to also see to what degree you can address the aneuploidy. So technologies like PGT will be certainly very important in the evaluation steps.
But if we can really achieve the efficacy that we can see in vitro, the idea would be that perhaps you can really make such a big improvement that you don't even have to consider doing PGT in some cases, especially in the women of the middle age. Well, this was fantastic research. This is one of the most exciting abstracts I've seen in a long time.
I'm glad you've caught this one. This is like one of the holy grails of us for infertility. We have not been able to treat a delayed increase in aneuploidy, and actually see something that has even a hint of potential is just exciting.
Thank you so much, and we'll definitely do our best to take it forward and to bring it towards clinical trials. Thank you. Hi, everyone.
We are back. The next abstract we're going to be talking about is Restoring Fertility After Gonadotoxic Treatment, the First Successful Autologous Transplant of Immature Cryopreserved Testicular Tissue in the Human. And I am joined by Dr. Woubert and Professor Gosens.
It's good to see you, too. Thank you for coming by and talking about your research. We're very excited about this.
For our listeners who are not here, can you just give us a brief synopsis and tell us a little bit about the work that you presented? Yes, of course. So fertility preservation is offered to boys that need to undergo very high gonadotoxic treatments when they are a child. This boy, in particular, had testicle cell disease.
So he needed high chemotherapy prior to a bone marrow transplant. So he was at very high risk of becoming infertile. So for this boy, we stored some testicular tissue when he was a child.
And now, 16 years later, we have transplanted this tissue back because he remained infertile and he wanted to have children later on. So this was the first time that immature testicular tissue was transplanted in a young man. And after one year, we could really see that spermatogenesis had re-established.
So some spermatozoa have now been stored for later use if the patient wants to start having children. Yeah, I think it's revolutionary. And I think it's a huge problem that we face for boys, not just with sickle cell, but with cancers.
And how do you envision that this will happen in the future? Of course, if we are going to look for patients with malignant diseases, then we need to be sure that there are no malignant cells that are frozen with the testicular tissue that is preserved as a little boy. So we need to be sure that there are no metastases inside the testicles before we could use it in case of malignant disease. That's also the reason why we started with a non-malignant disease, like in this case.
Yeah, and so you transplanted the tissue back and did you put it back into the testes where it belongs? Or can you also talk about whether or not it would be possible to do a heterotopic transplantation as opposed to an orthotopic to the same space? We opted to replace the tissue under the skin of the scrotum on four different places and also inside in the testes on four different places. And we saw, if we made the comparison in this one patient, that we see that the grafts that we placed intra-scrotum, that they were more fibrotic and we could not see spermatogenesis there, but the grafts that we removed from the side that we placed intra-testicular, they were less fibrotic and we saw spermatogenesis in two locations where we were grafting inside in the testes. And did it get to the point where there was any sperm in the ejaculate or is this, you saw evidence of spermatogenesis on biopsy of that tissue? Well, we of course checked also the semen.
We did not expect to find spermatozoa in the because the grafts that were transplanted do not make connections with the ejaculatory ducts. So that's why we need to remove the grafts after a certain time and we chose to remove them after one year. Then those grafts, part of those grafts were minced to find, to look for sperm.
We did not find sperm after mincing. The tissues were then sent for enzymatic digestion and there we found sperm in one of the intra-testicular grafts. But on histology, we could confirm that in two grafts there was complete spermatogenesis.
And I'm curious about his hormone profile. Do we think this tissue became hormonally functional? Did we see any changes in his hormone profile? We checked it, of course. We were checking in every follow-up visit and by start checking his hormones and his LH, FSH was higher.
His testosterone was within the normal limits, but we were not expecting to see some changements in the hormone because the tissue we replaced is so small. So we were not expecting to see, but of course we tried to look for prediction of the spermatogenesis, but we could not see it in an endocrine changement. But we did also some ultrasounds to follow up the grafts and there we could see vascularization in the different places.
And we also checked the function of the somatic cells on histology and we saw that indeed they were functional and the lytic cells were producing testosterone. Okay. So that's probably you're saying because the amount of transplanted tissue was so low, you weren't even expecting hormone changes in this patient.
Yeah. I mean, that's interesting what we're seeing in the female. I mean, some people are advocating to remove ovarian tissue and then replace it back into the female for menopausal hormone replacement therapy.
I can't help but wonder if men similarly, although the idea of men taking out part of their testes as you might be getting really uncomfortable, but taking out part of their testes and avoiding TRT in the future. What do you think about that? If you are going to remove it pre-pubertal and then are going to replace it like we did in very small amounts, we don't expect that it will work. Yeah, that makes sense.
I think you will need to remove a lot of tissue and maybe you have the contradictory. Which I think would compromise the effect. I mean, I've got lots of thoughts about it in the female.
I don't know that it's a viable strategy either in the female, but I think it's being done and it's being talked about. But I think this obviously has tremendous potential, right? I mean, especially when you have a pediatric male patient who doesn't yet have ejaculate, you don't have a way of collecting sperm from these patients, short of in vitro gametogenesis or creating gametes in other ways. I mean, this seems like an extremely promising, I feel like we are inching towards an extremely promising way of really preserving male fertility.
So I think this is incredible work. So, I mean, this was just such a fascinating abstract. Anytime we have like the first case of something happening, I think we learn so much and see what's possible and where the limitations are.
From your standpoint as researchers on the scientific side, on the clinical side, what do you see as the next steps both with this patient and with other patients that you're waiting for the transplantation of? Well, on the research side, it's important to evaluate what is the best site to transplant the tissue? What would be the best fragment size? And also how long should we maintain it in the body? That are research questions that still need to be answered. It would also be important to have biomarkers that could follow up the graft survival and the graft function in a non-invasive way. That would help us a lot.
And then on the clinical side? On the clinical side, we need to look if the sperm we found, the amount is very few in this case, but we need to look as well to the functionality of the sperm and if we do XC, if there is embryo, is there fertilization, is there an embryo? Can we make this patient happy with this and give him the child wish that he wants to have a biological own child? Yes. Yeah. Do you think that he would require more than one transplant? Did you use all of the tissue or do you have more tissue to take? No, we did not use all the tissue.
So we have still vials that are frozen that he banked pre-pubertal. In his case, there were very few spermatogonial stem cells that were inside in the frozen testicular tissue, but we have still the possibility to do a second graft in the future, if necessary, and the person is open for it, if necessary. And how close is this patient to wanting to have a child? What are the next steps? I can tell you he wants to marry first and then we can see afterwards.
Yeah. So you'll have to keep us posted, maybe not X-ray 2027, but maybe 2028. It's a bit too early.
I hope that it's a successful and then we will see, but we need to see. There are still a lot of steps that we need to take. Yeah, but I think it's so compelling and I'm glad that it sounds like he's open to having his data published and shared with the rest of the world.
So I wish him the best and I wish both of you the best in terms of seeing this through and the tremendous steps that you're taking towards preservation of male fertility. This concludes our episode of Fertility and Sterility On Air, brought to you by Fertility and Sterility in conjunction with the American Society for Reproductive Medicine. This podcast is produced by Dr. Molly Kornfield, Dr. Adriana Wong, Dr. Elena HogenEsch, Dr. Selena Park, Dr. Carissa Pekny, and Dr. Nicholas Raja.
