Alan Copperman on How Data is Transforming Reproductive Medicine

EPISODE SUMMARY

This week Harry welcomes a guest who could be considered a “poster child” for the movement to incorporate more data into clinical practice: Dr. Alan Copperman, a New York-based specialist in reproductive medicine. He says the data generated by genetic screening of fertilized embryos is rapidly and dramatically improving outcomes for couples who want children.

EPISODE NOTES

Harry Glorikian: Hello, I’m Harry Glorikian. And this is Moneyball medicine. The show where we meet executives, entrepreneurs, physicians, and scientists using the power of data to reinvent healthcare from machine learning to genomics, to personalize medicine. We look at the biggest trends in patient care and healthcare management.

And we talked to people behind the trends to find out where data is making the biggest difference.

My next guest, Dr. Allen Copperman is director of the division of reproductive endocrinology and infertility, as well as the vice chairman of the department of obstetrics, gynecology, and reproductive science at Mount Sinai medical center. Dr. Copperman is a clinical professor of obstetrics, gynecology and reproductive medicine at the Mount Sinai school of medicine.

And also serves as the medical director of reproductive medicine, associates of New York. He is also the chief medical officer of Semafore genomics, a patient centered health intelligence company, and the medical director of progeny, which operates as a fertility benefits management company. Dr. Copperman is frequently quoted in print and digital media associated press wall street, journal us news, world report, New York times daily news, and more, and is featured on local and national nationally.

Televised news shows including Fox five. New York live CBS this morning, Huffington post and more. Dr. Copperman has been named to New York magazine’s list of best doctors, 16 years in a row and castle Connolly and super docs have annually recognized Dr. Copperman as one of the top fertility doctors, Dr.

Copperman. Welcome to the show. 

Alan Copperman: Thank you pleasure to be here.

Harry Glorikian: Dr. Copperman you know, tell us, tell us a little bit about your journey. I, you know, reproductive health, uh, data analytics, Semafore genomics, you know, tell us where you started and how you’ve gotten into this whole data gig. 

Alan Copperman: Well, I just was formally trained as an OB GYN.

And then I did a fellowship in reproductive endocrinology, and I thought that treating infertility was going to be being a reproductive surgeon. So I started off doing a lot of laparoscopies and taking out fibroids and hysteroscopy using fiber optic scopes do take out polyps and fibroids. And then over time I realized that much of taking care of the infertile patient or infertile couple was going to be done in a dish with an operating was going to be done on eggs and sperm and embryos. And so I transitioned where some more of a surgeon is helping to build RMA of New York, which is a pretty large IVF center. Now that’s taking care of thousands of patients a year.

And with this transition came the need to collect data and learn about what the best way is to take care of patients to optimize those success rates. So I fell back on, uh, the term that you use, Moneyball medicine. Uh, because we want to have the best embryologist, the best egg retrieving doctors, the best embryo transferring doctors.

We want to put a team on the field that optimizes the success rates for every patient and every couple who walks into our doors. So I started to work with our data analysts and our computer programmers to build systems that track everything that we do and help us develop networks of information so that we can optimize pathways to improve outcomes.

And I just got excited about using information to drive better decisions. 

Harry Glorikian: So it sounds like you’re part doctor part geneticists, part mathematics guru, some combination thereof. 

Alan Copperman: Well, I like to rely on the brilliant geneticists, the brilliant mathematicians, statisticians, computer programmers to present information.

So I think that my pieces translational, um, I can speak a little bit of each of their languages and, and, um, then meet the need of the patient by leaning on the support of people who really are the super specialists. 

Harry Glorikian: So when I think about this, I, I, you know, when I, when I put together the book Moneyball medicine, it was all about how data was going to transform medicine.

You’re the in my, in my mind, you’re the poster child of, of, uh, you know, starting, which, you know, as you said, in surgery and migrating to data being almost dare say central to what you do on a regular basis.

Alan Copperman: It is so we’ve taken from the Oregon system operating on a tube in a uterus to the cellular system of operating on an egg and a sperm and taking out pieces of biopsying, embryos, and running next gen sequencing.

So we get tens of thousands, even close to a million data points on every embryo that we speak with to figure out if they’re healthy or not. So we need mathematicians to interpret the genetic code, and then we have to translate it back to a human level, develop decision, support tools. So the doctors can talk to patients.

So it starts off with the patient. It ends in patients, but the pathway is just so completely different than it was 3 years ago, no less 30 years ago. 

Harry Glorikian: Oh yeah. I’m, I, I, I was telling somebody I’m like, if I stop reading for a couple of weeks, I feel like I’m woefully behind in what’s going on in the space. So, 

Alan Copperman: Yeah Well, take us a perfect example, uh, expanded carrier screening.

So a couple comes in and wants to have a healthy baby. And we test mom and dad for certain recessive diseases that they may carry a mutation for. So they’re healthy, but together they might be at risk of passing on diseases like cystic fibrosis or sickle cell anemia or to the child. So 30 years ago, we just started doing some screening.

Uh, 10 years ago, there were four or five diseases that ACOG the American college of OB GYN was recommending people will be tested for, and next week we’re going from 284 diseases to 502 diseases that each patient is going to be tested for to see what they carry, what gene mutation in gene or what variant they carry that could cause an unhealthy child.

So the mathematicians and geneticists are feeding us information that we never dreamed possible. And we’re going to be able to prevent unhealthy children from being born. We’re going to be able to knock genes out of families that have, uh, could potentially have caused diseases for generations. That’s that perfect example of using data to drive better medical decisions?

Harry Glorikian: Tell me how you and or the patients are affected by this. And tell me how this. Or if it affects cost in the system. 

Alan Copperman: Right. So they’re really important. Uh, factor in any introduction of technology is the, um, is the fairness of this, uh, of how it’s being offered. This isn’t just for rich people. This isn’t just for patients that have certain commercial insurances.

And is this cost effective as a system? Is this going to create costs to reduce costs? For years in vitro, fertilization was extremely expensive because patients would have twins or triplets or even higher order multiples. And there would be premature babies and millions of dollars in care, maternal fetal care that was being covered.

So it wasn’t that a 10 or $15,000 IVF cycle that was driving the expenditures of the, uh, of fertility. Uh, it was really the downstream costs on mom and children and the unhealthy children that were being born from being born prematurely. These days we use. Sequencing of the embryos. So spending a couple of hundred dollars extra per embryo.

We can find the healthiest and real. We only need to put in one. So we have higher success rates and lower maternal field costs. So money up front spent for testing. A couple for carrier screening will prevent a baby from being born with cystic fibrosis and decades of a very expensive care and human suffering.

And testing of an embryo will allow us to pick out one. So we have a Singleton rather than a multiple, which will have a healthier baby at the end. So I think that the cost of technology is high. But the cost savings can be dramatic. 

Harry Glorikian: And as you were saying, this is, you know, available to everyone or a certain group.

I mean, you’re in New York, you’re, you know, you’re in a unique environment. How does this. Become something that everybody can get everywhere. 

Alan Copperman: Fortunately, there patient advocacy groups like Resolve that are, uh, that are marching on the legislature in every state to request mandates that fertility coverage, the part of every plan for, um, for, for people that have healthcare, uh, which hopefully will, will be all. And, um, there are groups like Progeny that you mentioned in the introduction, uh, which is a company that I’m fortunate to be part of, uh, that works with employers to cover the fertility journey of their employees.

So it’s a great tool for a company to recruit and retain, especially female employees that are driving a lot of the fertility care. Um, and it’s also, uh, a great model because Progeny builds, builds networks of doctors that are committed to efficient care. So back to using data, to drive decisions, we have a report card on each doctor.

So we know that how quickly they help us patients become pregnant with a healthy Singleton pregnancy and have a healthy delivery because that’s actually the cost savings that employers are looking for when they’re providing benefits like this. So hopefully between his legislative change between fertility, between companies supplying benefits for their employees, Um, I think that we’re going to just keep increasing access to care, to both preserve fertility for young women who are immune to delay their childbearing until they’re ready to have a family and to cure in fertility and a couple of who’s struggling.

Harry Glorikian: Well, it’s interesting cause you, you know, everything you just said was sort of day-to-day to data, right? So it’s data on the analytics of the couple, but you’re also, you said a report card on the physician, which is a really interesting, uh, you know, aspect of seeing who’s better than the others, um, or more efficient.

I’m not sure how, what the report card says, but you can sort of start to pick the really good ones from the not so good ones. 

Alan Copperman: Right? I think that will, what I’ve been using information to do is to help raise awareness of an individual doctor, because everybody, I think thinks that they’re doing a great job.

But the metrics of yesterday are different than the metrics of today and will be different from the metrics of tomorrow. In other words, it was all about getting a clinical pregnancy test positive 10 years ago. And we all tried to I’m 40% on 50%, and now it’s a healthy Singleton delivery, a term. So having a triplet pregnancy is considered a failure by today’s model.

It was a success a decade ago. So doctor that is unencumbered by data or wants to rely on the way things are done in the olden days may not be a doctor that an insurer is going to want to use for their patients because that’s going to drive up costs. So even insurance companies like Optum health and wind, uh, Wind fertility, and United and Cigna and Aetna, they’re developing centers of excellence models.

Harry Glorikian: Yeah, no, it’s well, you know, all my employees would think that every single one of them was doing a great job. It, it doesn’t, it doesn’t work that way. So you’ve got to sort of bend them out too, to know what someone’s good at and where they lie on the curve. Um, how do you incorporate data into the conversations when you’re talking to patients?

I mean, I’m sure that this for most couples is, you know, either overwhelming or it’s, you know, you could be speaking a different language as far as they’re concerned. 

Alan Copperman: Right? I think it’s so important to ask real questions, assess patients, knowledge base, and need them. Somebody who’s a 39 year old that wants to have three children.

Uh, yeah, the conversation is not, why are you here? The conversation is what is the ideal family building that opportunity look like you. How many children do you want to have? If you want to have one child? Well, let’s start right now. We can give you to Clomiphene Citrate, which is a fertility pill. We can do inseminations and we can rapidly progress.

If you want to have three children, then we should probably be thinking longitudinally about. When you’re 44, it’s going to be really tough the next time around. So maybe we should bank some embryos, take fertility, medications, retrieved the eggs, fertilize them, test the embryos, freeze the embryos. So when you’re 44, you can still have a 39 year old egg that has already been tested and found to be normal.

So thinking about what the journey looks like, what the family building goals looks like. And yeah, so I think with family planning, Not I’m trying to not get pregnant. I think family planning is what is your ideal family look like? So for any couple that comes in, we should have a matrix of data that says, what is the success rate per cycle?

What is the cumulative success rate over time? And what is the likelihood of having a healthy baby with each treatment protocol and find decision, support tools, find ways to convey that to a patient in language that they understand. Graphically and writing visually with videos. I think that that’s part of the obligation of today’s position in every field, but especially in our field of fertility is we have to really be able to educate our patients.

And I also find that this helps patients manage a lot of the anxiety that comes with infertility. So when a couple comes in there, this tremendous loss of control, which obviously is going to provoke anxiety, especially when the stakes are really high. So we can provide some control by giving information, setting up realistic projections and predictive algorithms so that they understand when they are going to be coming in for treatment, what the likelihood is of success and what they do next. And that’s all part of, I think, being a good fertility specialist. 

Harry Glorikian: So when I think of your field, does do, do apps play a role about how you help manage a patient or the data that you’re looking to gather from a patient when they’re not present?

Alan Copperman: Yeah, I think many of our patients now come in with fertility tracking apps, with temperature apps, with, uh, um, there are new tools being developed to use saliva. There’s a, uh, vaginal thermometer that tracks via Bluetooth to a, IPhone or to a, uh, to a watch that ends up tracking, um, moments, moment temperature changes.

And there’s, there’s a lot of tools out there. Some of which create anxiety because it just heightened awareness all the time of, you know, the only thing somebody could think about, some of which actually alleviating dieting, uh, all of which provide big data. That’ll help us ultimately map out the journey and personalize it for each patient.

Harry Glorikian: There’s a lot of new analytic tools that have been emerging, you know, hardware, software, et cetera. You know, you, you work with Eric at Semafore and he’s, you know, known him for a long time you know, totally different level of thinking that guy, but where, where do you see, you know, things like artificial intelligence, machine learning, you know, all these other methodologies playing a role in the data analytics side of, of what you’re being exposed to. 

Alan Copperman: You were mentioning Eric, Semafore who is, I truly believe one of the great geniuses of our time, and I love getting to work with him because he’s, he’s brought me a little bit into his world. Like we’ve done work, uh, assessing the transcriptome of an embryo.

In other words, You take , an egg, you fertilize it, you create an embryo and certain genes get turned on or off at certain times. And that’s going to predict the behavior of an embryo. So we’ve been learning about RNA sequencing together on embryos. And Eric has got this visualization of these gene networks.

In other words, it’s not one piece of DNA that gets transcribed into RNA. That is going to predict behavior. It is entire networks that work together and predict cellular behavior. So we’re really working on a molecular level and then he creates these networks and information, uh, in silico or basically, um, inside of the computer world.

And. The goal is that figuring out how to perturb different moments of this network of information can predict the behaviors. And the whole point is to take either a embryo and predicted behavior or use this knowledge on a macro level for a patient or a population of patients and change the trajectory for one that’s heading towards sickness or in our case infertility to one that’s heading towards health in our case fertility.

So that’s what I’ve been working with Semafore and Eric on is developing molecular tool. Um, and we’re going to vary quickly get to whole exome sequencing for most patients. So we’re going to get deeper knowledge and then we need the phenotype phenotypic information, the history to go with the genome to get information what their DNA is.

And all of that is going to help us to develop better predictive models and to take better care of patients. 

Harry Glorikian: You know, when I think to myself back when I was at Applied Biosystems and we were saying, yeah, we’re going to do the, the first reference genome to where we are now. It’s, uh, it’s mind boggling sometimes when I think about it, I mean, on one hand, it’s sort of, we’re used to it because we’re living it.

But on the other hand, you’re like, Wow in a pretty short period of time, data has really changed. 

Alan Copperman: Ithas an a perfect example of that is, uh, the, the new you’ve mentioned Eric and Semafore, uh, this new genetic carrier screening test that we were talking about before, instead of asking a patient, are you Ashkenazi are you Sephardic?

Are you, uh, Chinese? There will be ancestral inheritance markers baked into the test. So we’re going to know somebodies ancestral heritage. And so we’re going to know when they have a variant of a gene, whether in that population of people, it has been described, whether it causes disease or not. So it’s going to provide more meaningful information and it’s called residual risk.

If somebody is negative test negative for, uh, uh, for the, for a disease carrying state, whether based on their background, whether it really has been described and whether the negative is a true negative. So it’s using the information from the exome too uh, to have translational tools that will really inform a patient and therefore enable a patient to have a healthy child.

And another example is cancer. I mean, we’re not going to be calling cancers, breast or ovaries in the near future. They’re all triple negative, triple positive, where the mutation is. So we’re going to use deep information about mutations in DNA. Um, Uh, segments to figure out what the predictive behavior is of the tumor and what the right treatment is for that particular molecular disease.

That mutation that is driving disease in that patient. 

Harry Glorikian: Pardon me? In the back of my mind, I was thinking about, I know that you’re utilizing this data and fertility and with children, but some of this data can be extended forward into helping, you know, different adults with different diseases and, and diagnosing them so forth.

I mean, the data is not solely in the fertility space per se. 

Alan Copperman: Right. And there’s overlap too many areas of health, but cancer is that perfect, uh, opportunity for fertility specialists to work with oncologists and internists. So a patient that comes in and they’re, the grandfather had pancreatic cancer, the grandmother had breast cancer and the aunt had ovarian cancer and and then you go back generations and generations. And this, this family has just been tragedies in sick, sick people forever. And we realized that there’s a, there’s a mutation it’s they have Lynch syndrome, for example. And there’s a. Uh, P 53 mutation and we can do in vitro, take the eggs and sperm test the embryos, half of the embryos are going to have mutation find the embryos that don’t put the embryo into that next generation. And that’s that family that has suffered for generations will have health for subsequent generations. It really on a level that some mathematician and geneticists somewhere figured out these brilliant tools and are putting it into our hands. And then on a human level, we’re preventing suffering and promoting health. It really is such a great time to be in this field. 

Harry Glorikian: So where do you see reproductive health moving next with the help of all this data?

Alan Copperman: I think that we’re using information to triage patients into pathways.

We will be dosing, uh, patients appropriately based on maybe this a genetic sequence of their FSH receptor. Um, we’re going to develop new markers for embryos, um, to figure out which ones are going to be healthy. We’re going to use information to figure out what patients are going to be responsive to different treatment algorithms.

Um, and sometimes it’s even important to know who is not going to get pregnant. And we also, and maybe have them be okay with adoption or egg donation or sperm donation. And we’re going to develop better tools of the uterus. I mean, we think of the uterus is being really receptive to embryos that’s it’s job, but maybe some people’s genes are not turned on and off the right way.

There’s a different sequence. And just finding a way to optimize a window of implantation so that a healthy embryo is going to and plan to become a healthy child. And that’s where we’re going in. This information era is just collecting more and more data to figure out scenarios that work and don’t work.

And then to keep vectoring towards achieving goals and vectoring away from achieving failure. And that sounds obvious, I guess when I say it. But it’s really not as easy to do. You need tremendous amounts of information to develop models that can then personalize it. And then a patient into a group that is going to follow the pathway that has been laid out by millions of data points.

Harry Glorikian: The area has changed so much. Um, I’m not sure if medical schools in all these, you know, different areas are keeping up with, um, what someone needs to know. Um, So, what would you recommend to people? 

Alan Copperman: I think that medical students should be taking bioethics classes because we’re going to get to this we’re rapidly approaching where we’ve even passed the areas of medicine that we haven’t been prepared to make decisions on.

Uh, I would tell them to take genetics classes and math classes, because this big data, you need some computational power to both assemble appropriate models and to interpret them, um, And then, uh, and then we still need the skills and hopefully some compassion and thoughtfulness and a background of, of incorporating the huge amounts of information and then transplants.

And an individual will allow us to deliver efficient cost-effective precise, compassionate, and successful care. And that’s hopefully the way we’re going to go. 

Harry Glorikian: I like the hopefully, um, it’s so fascinating how much data is changing, how we do what we do. I guess one of my final questions is how have you seen this change, the business model.

And I guess what I mean by that is I’m always looking at as a, as an investor in the space is. How is the incorporation of data, uh, tweaking the model or blowing up what was, and maybe creating something that’s new, uh, new opportunities for businesses, I guess, you know, similar to Semafore or Progeny, but do you see things changing rapidly?

Alan Copperman: Um, do I see opportunities. For, uh, using for digitizing any medical field and, uh, sure information and will allow us to innovate, um, and reproductive medicine. We’re such a high-tech field that people are always engineering, new widgets, and coming up with new,- I’m working with a company called Tomorrow, which is a really cool startup that is using robotics and RFID to transform the way we, uh, cryo-preserved eggs and embryos. So instead of having tanks and tanks, all around the country that gets filled with liquid nitrogen manually or with a, uh, an autofill device. Um, there’s robotic storage that has a label and with the radio frequency ID of each canister around each specimen.

So a patient can track where their eggs and embryos are, and maybe essentially eventually stored choice a bio repository with tens of thousands of other specimens words. Going to be kept safe. And there’s a vision of using information, using data, using software, using just like somebody would go on Delta and it’s find my luggage.

It would be nice to know where your eggs are. You froze your eggs to preserve fertility. Um, and it’s just cool innovation. That’s patient focused and scaling this opportunity of taking a cell that’s less than a hundred microns. And um, and having a safe journey for it. So I think that there’s tremendous opportunity in our field.

Harry Glorikian: I, you know, I wish you great success with, uh, you know, your, your day job, some of, for Progeny and on, uh, all the other, uh, projects you’re working on. 

Alan Copperman: Well, thank you. It’s really been a privilege to be part of your show. 

Harry Glorikian: And that’s it for this episode. If you enjoyed Moneyball medicine, please head over to iTunes, to subscribe, rate, and leave a review.