Dr. David Fajgenbaum
👤 PersonPodcast Appearances
And then I'll hit join studio.
I've got a friend who's here helping with the audio.
So we're double mic'd right now.
I don't know if I've ever answered a question this way, but when I was in medical school, I worked out all the time.
And part of that was because for the previous 15 years of my life, I was obsessed with wanting to be a college quarterback.
So I grew up in Raleigh, North Carolina, where college sports are really big and NC State had a football team and I grew up sort of like loving their team.
But I think maybe more than anything is once I started playing football and I started like... I'm biased, but I think football is unique among team sports in that...
You connect with your teammates in such a way because literally like your health and your life is like on the line.
If someone else doesn't do the thing to protect you, you don't need to protect them as a quarterback.
And so I just fell in love with football.
I mean, as a kid, I just was in love with it.
If you could have seen my walls when I was growing up, literally every corner of every wall in my bedroom was covered with charts measuring like how far I could throw a football, how fast I could run all with the goal of getting better.
I get this opportunity to go to Georgetown to play football.
And it was the dream where it was like, okay, I can go to a great university that has a health science program so I can keep studying health science and I can play football.
I really love the coaches there.
But then I got to school and I was there for only a couple weeks before I got this just horribly devastating call.
I said, David, your mom has brain cancer.
You need to come home right away.
So I immediately went back home to Raleigh.
I was able to just see my mom just before her brain surgery.
And then we went to Duke for her brain surgery.
Yeah, so before the brain surgery, they just said it's a brain tumor.
It looks like it's brain cancer, but we need to go in there and actually see what it is.
So, you know, my family and I, we were just so just nervous about everything because, you know, they did warn us before that, you know, the person that comes out of surgery isn't always the person that goes into surgery.
And I remember going back to see my mom with my dad, my two older sisters.
And she had this wrap around her head.
She had a bandage around her head from where the tumor had been resected.
And she had this bulb that was coming out of the incision site.
It drains out fluid from the incision.
No one really knew what to say.
I said, you know, mom, how are you doing?
And she pointed up at her head with this bulb and the wrap around it and she said, Chiquita Banana Lady, which is like referring to like – if you look at the sticker on your bananas, there's like – there's the Chiquita Banana Lady.
She has a wrap and she's got all the fruit.
Her head kind of looked like the Chiquita Banana Lady and like –
It was exactly what we needed.
It was like exactly what we needed.
Like we all burst out laughing and crying and we're snot crying and like our mom's with us.
That's like that's who she was.
this sort of prognosis after that, or what was the timeline?
Yeah, so the doctors came in and they explained that it was, they explained it as grade four glioblastoma, which the average survival was around six months, and I think they said the longest someone had survived, I think I remember it was around five years.
So I spent a few more days at home after surgery and just would not leave her side.
She lived 15 months after diagnosis.
She was diagnosed July of 2003.
She passed away October 26th of 2004.
But while I was home, we had a lot of just really special time together.
One of the things we did was actually go through old home videos.
Like, you know, we had these like Betamax videos.
I don't know if you remember these like old home videos.
Went through them and just we did like all the things that you would, you know, want to do.
all the things that you know you'd want to do before um you know someone like my mom passes and um this was 21 years ago um did all those things and um and uh it was it was really special um and it also as you can even tell this is 21 years later it um
created such a drive in me to just say yeah i want revenge i want to do whatever i can to take this thing on yeah and um i told her i was like mama i'm gonna dedicate my life to trying to help people like you like that's just like full stop like this whole football thing that was fun these last eight years but yeah i'm gonna be a doctor and i'm gonna dedicate my life to just find treatments for this horrible thing that was taking my mom from me
And, of course, the challenge in med school is it's very much a training period, which is hard for someone like me, right?
It's like, you know, I want to take this on.
But I'm in this period where, yeah, I've just got to train, train, train.
So I'm on my OBGYN rotation and just started noticing that I was more tired than I'd ever felt.
And I sort of always was able to run on low amounts of sleep and lots of caffeine.
But I was really, really tired, like this fatigue that I'd never felt before.
And I remember sort of like trying to just like put it out of my mind, like whatever this is is going to go away.
And I went into the hospital to take this medical school exam.
And I remember during the exam, I was like dripping sweat head to toe.
And then I was like, you've never felt like this before.
I also had noticed these bumps appearing on my body.
They're called blood moles and they're normal as you get older, but they are abnormal to appear rapidly.
And it's like as I was studying for this exam a couple days before, I like noticed these blood moles on my body.
And so I actually handed in my exam and I just walked down the hall to the emergency department in the same hospital that I was taking the exam in.
And I just told them about my symptoms.
And, you know, I had worked in that ER just a few months before.
And doctors are usually really slow to come back.
And it's like, you know, things take a while unless there's something really wrong.
And they come back really quickly.
The doctor came back really quickly.
And he told me, he said, David, he said, your liver, your kidneys, your bone marrow, your heart, and your lungs are shutting down.
We have to hospitalize you right away.
Yeah, there's like your brains left.
But like pretty soon that was going to not be as clear.
But yeah, it's this concept called multiple organ system failure where everything was shutting down.
And so they hospitalized me and I just just went downhill from there.
I started getting really sick really quickly and I knew things were bad.
And the doctors were using the language that I had used when I talked to patients when things were really bad.
You know, we've run a lot of tests and we're on top of things, but we're not really in a position yet to tell you exactly what we think is happening.
And it took a total of about 11 weeks before we finally made the diagnosis.
With that diagnosis came almost immediate use of a type of chemotherapy.
So the diagnosis was what's called idiopathic multicentric Castleman disease.
Castleman disease describes a group of these rare diseases where basically your immune system attacks your organs for an unknown cause.
We call it idiopathic because we don't know what the cause is.
When I heard it the first time, I vaguely remembered like I think I've heard that once in med school.
I was like third year med student and I think I heard it once.
I definitely wasn't familiar with it.
So we were like really happy that it wasn't cancer.
We were like, yes, like this is not cancer.
You know, we thought it was lymphoma this whole time and it's not.
And then there was this, you know, really quick realization shortly thereafter that, you
that my subtype of calcium is idiopathic multisensory calcium disease actually has a worse survival rate than lymphoma does.
And that actually the thing that we were hoping it was not actually would have been better than the thing that it turned out to be.
And I was so sick when the diagnosis came in.
that the doctor told my family, we don't know if this medicine is going to work, but he's so sick that we don't think he's going to survive much longer.
You should go ahead and say goodbye to him and prepare him for not being here.
I don't know if I was mentally... I wasn't totally there, but I do have some memories, and those memories are...
The room being really dark, my family hugging me and crying.
And they just started telling me all the things that I told my mom, right?
Like, you know, what I meant to them.
And, you know, we're reminiscing on old memories.
And then I remember the priest coming in.
I mean, of course, I'd never had my last rites read to me before.
So what is sort of like confirmed my biggest fears, which is that like, this is going to kill me.
Um, but just a couple of days before the priest had come in, the doctors had tried this one chemotherapy.
It's the only chemotherapy they thought to try.
Like there were actually others they could have tried, but this was the one they tried.
Um, and amazingly, um, it, it just started to kick in really within days.
And, um, it didn't last long term.
I relapsed about a month later and
It was a real roller coaster because like the euphoria that we all had when I was feeling better and the hope that we had.
And then just, you know, a few weeks later when it would come back, just the heartbreak.
And that cycle happened a total of five times in three and a half years where I went from being, you know, totally, you know, critically ill in ICU to much better to then back again.
Yeah, that moment for me, I remember very vividly.
It was sort of, I mean, if I think back on my life and these moments, like the moment when I got the call from my dad that my mom had brain cancer,
And the moment I was sitting in the hospital room and my doctor explaining to me that the only drug that had ever been studied for my disease wasn't working and that there was nothing else.
And I was just searching for something like, is there a gene or a protein or a cell or something that we might know about this thing?
Like, give me something, like begging for like some lead.
And he just was clear, there's nothing, like nothing.
You are going to die from this disease.
The chemotherapy is going to stop working.
And there is nothing out there.
That was when everything changed.
If I want to survive, like if I want to spend more time with this girl beside me that I love, Caitlin, and I want to get married to her one day, I want to spend more time with my family, like I've got to activate.
And it was right around that time I was learning about how a drug that was being used for Castleman's was also working for other diseases.
And I was like, wait a minute.
There's a Castleman's drug working for other diseases.
Maybe there's another drug for another disease that could work for me.
Like it just is sort of like this very simple concept.
And frankly, it was the only path.
It wasn't like I was like, oh, it would be great to do this or to do that.
It was like this was the only path was to find an existing medicine.
And that became just my central focus.
So first thing I did is I went to my mentor, Arthur Rubenstein.
He was the dean of the medical school before and he just retired.
And so I went to him for advice and his support.
And he said, David, I'll support you.
And he's been amazing over these years.
But I wanted to go to someone who sort of like could give me advice.
I didn't know what I was doing.
I need to build a team, exactly.
It was like, I don't know what I'm doing, but I need to build a team.
So first went to Arthur, he came on board.
The second task would be to understand what was going on in my blood and in my immune system and see if there was something that was already approved for another disease that could maybe be repurposed to treat me.
And that's when I, you know, I guess they did the equivalent of, you know, covering my walls and poster boards for throwing the football.
And it just became all encompassing.
I got to find a drug for this disease.
I remember turning to Gina, my sister and saying, gee, I need you to call UNC and Duke.
I need you to get all my medical records, ship them to Philadelphia.
I need you to get all the blood samples and lymph node samples at each of the hospitals.
They need to be in Philly because I'm going to get out of here in a few weeks.
And when I get to Philly, like the clock's ticking, I need to get to work.
And and her and Caitlin just got to work.
And a few weeks later, I was back in Philly and the blood samples were there.
The lymph node samples were there.
The medical records were there.
And I just it was all day every day to try to find a drug.
And Caitlin and I. It's like a train coming.
It's a train and it's hit me.
There's no chance it's not coming back.
This was, it's, it's coming and I don't have another shot.
And I had a really big date in front of me.
May 24th, 2014 was Caitlin and I's wedding day.
We were, we were engaged and now we're talking January of 2014.
So I had, you know, about four months between getting out of the hospital and making it to our wedding day.
So I saw all those samples and I started doing something called serum proteomics where the idea is you measure 1,000 different things in your blood or 1,000 analytes or proteins in your blood and then we did something called pathway analyses where we try to understand what are the signals in the blood that are coming from these proteins being up or down.
I did something called flow cytometry to look to see which of my immune cells were turned off and turned on
And then cytokine panels where we measure these 13 different proteins and their changes in the blood.
And what emerged was that my mTOR was in overdrive.
And mTOR is a communication line your immune system uses to turn on, to turn off, to proliferate.
When I saw that result, I immediately remembered that there's a drug called serolimus, the other name for it's rapamycin, that is really good at turning mTOR off.
So like I saw the result and it's like mTOR is on and I was like, oh my gosh, isn't there a great mTOR inhibitor?
Rapamycin is the drug that saved my life.
I listened to that story, and I love that story.
And, of course, it's found on the island of Rapa Nui.
It was hidden in a freezer in Canada.
And you're like, oh, there's one drug right there.
And so I told one of my doctors and I went through all the data and I just said, like, do you think that we should try this?
Like, I know it's never been used before for Castleman's, but like, can we try it?
And his thought process was like, probably it's about a 10 to 20% chance it could work, but it's a 0% chance if I don't take it.
And I'm willing to take that risk.
And so he said, yeah, I'll prescribe it.
Well, at first, I took five pills, and now it's three pills.
But within a couple days, I started to feel better.
And the blood work started to get better more rapidly than it would have otherwise.
But again, I still wasn't ready to say, like, this drug is working.
And so for me, I was like, I'm not going to get my hopes up.
It's going to be a test of time.
Am I going to make it to my wedding day?
Am I going to make it a year?
Am I going to make it longer than that?
And yeah, just four days ago marks 11 and a half years that I've been in remission on this drug.
I mean, I almost died five times in three and a half years before.
And now it's 11 and a half without this disease coming back.
Amazingly, you know, it weakens my immune system in the right way so that I don't attack my own organs.
And I mean, the moment that that drug, the moment that I started thinking that drug was helping me and knowing that it was, you know, always there for something else.
And then certainly as the time went on, when I got to marry Caitlin and then as the years have gone on.
I've just gotten more and more obsessed with this idea because I'm literally breathing and alive because of a drug that wasn't made for my disease.
I just feel this tremendous sense of responsibility that like, hey, David, if you're going to get lucky enough to have one of these medicines help you, you sure as hell better spend the rest of your time trying to find as many more of these medicines help other people.
So that led us to then say, okay, we need to do more laboratory work.
We need to start uncovering more pathways that might be important, more genes, more proteins that are important.
And so we started getting really involved in that sort of laboratory work.
And in parallel, the next probably big milestone to go from like, okay, we help someone else with my disease was actually my uncle was diagnosed with angiosarcoma, which is a horrible form of cancer, the same week that my brother-in-law was diagnosed with ALS.
I went down to Raleigh to be with my brother-in-law, happened to be the same week my uncle got diagnosed with angiosarcoma, so I went with my uncle to his doctor's appointment, and the doctor explained, you know, there are these two chemotherapies, and they'll give you a couple months to live, but they're gonna stop working.
Start, you know, looking for drugs that could be repurposed.
And his doctor explained, like, there just there isn't anything for angiosarcoma.
But like, there wasn't anything else for Castleman's.
I'm like, you know, I'm here.
Maybe we can find something else for angiosarcoma.
And that's when we came across a study that had been published.
They're like, they're looking at me and they're like, your uncle has a terminal illness.
The last thing he needs is for his nephew to tell him or me that there's a treatment out there that can help him.
Like, that's not what he needs right now is what they're thinking.
And in my mind, I'm like, are you kidding me?
And last time I checked, there's 4,000 drugs in that CVS.
And I know those 4,000 drugs haven't been tried for him.
So until we tried all 4,000 drugs, you can't tell me there isn't a drug in there that can help him.
And so we find a study that had been published three years earlier that basically says that four out of five people with my uncle's cancer have very high expression of something called PD-L1.
I'm here saying like, let's test this tumor for PD-L1.
And the doctor says, I'm not going to test it because no one with angiosarcoma has ever been given a PD-1 inhibitor.
less than 10% chance that this gene panel that you want to order for Michael is going to come out with anything helpful.
So you're telling me there's a 5% chance that this test is going to give us something that's going to keep him alive longer than two months?
So you're saying there's a chance.
And so I don't blame them because when you're a doctor and you do this 100 times and it works 100 times, that is frustrating.
But when you're a patient and it helps you that one in 100 times, it's everything.
And so I got another doctor to order the test.
So we get the test results back.
Well, first I should say, I had him order two tests.
The first of the tests, it came back with nothing informative.
And that was actually the expensive test.
That was a test that cost $2,000.
That came back with nothing useful.
So I will totally give it to him.
The inexpensive test that I wanted him to order came back 99% of his cancer cells were positive for PD-L1 expression.
99%, which is not a guarantee, but it is a high likelihood that therefore a drug that inhibits this might be useful.
And we got Michael on this medicine.
And April of this year marked nine years that he's been in remission from his angiosarcoma.
Other patients have been treated with this.
Other doctors learned about this and started treating their patients.
And it turns out about a third of people with this horrible cancer, previously uniformly fatal cancer, will respond really well to pembrolizumab, to this medicine.
It's now standard of care for his form of cancer.
It's now standard of care without ever doing a clinical trial.
And that goes to show you when you have a disease that's this bad and you find a drug that works this well, you can change the paradigm for the disease for relatively, I mean, as close to zero dollars as humanly possible.
So the reason I think that our system is like this drug works for this disease is because in order to get a drug approved, a drug company has to develop a drug for a specific disease and submit it to the FDA for that disease.
The FDA approves it for that disease.
mentions a single word about that drug working in another disease, they will get fined billions of dollars for what's called off-label promotion.
So when the FDA approves a drug, what they're really doing is they're approving a drug company to market a compound for a specific disease.
And that company cannot market that compound for any other diseases until they come back to the FDA to get that change made.
But every time a drug company does that, it costs lots and lots and lots of money.
So they don't go after all the opportunities they have.
But insurance companies and payers realized, well, if this drug that's approved for this one thing could also be useful in this other thing and it would be good for patients, shouldn't we allow doctors to prescribe things off-label?
And so that's something that happens very commonly.
About a quarter of all prescriptions in the U.S.
Yeah, so it's somewhere between 20% and 30% of all prescriptions written every day in the U.S.
So that includes examples like doxycycline for Lyme disease, where like every doctor in the world would be like, yes, use doxycycline for Lyme disease.
But doxycycline is a cheap old generic antibiotic.
So whoever made doxycycline 100 years ago, 30 years ago, when people figured out it worked for Lyme disease, they aren't going to submit for a label change.
And that gets into the other factor here, which is that once a drug becomes generic, whoever originally made the drug
they stop making money off of the drug because you have generic competition.
You have multiple companies that make the identical drug and the price plummets per pill.
And so no one in our system makes any money off finding a new disease for that drug.
Except it's more complicated than that, because you can only sell a drug for one price, regardless of what disease you sell it for.
It always has to be the same price.
So what that means is that you have to pick the first disease that you get your drug approved in.
You have to pick the optimal market for that drug for the optimal price because pricing is actually not based on the cost of the medicine.
Pricing is based on the value for that disease.
So the fewer competitors there are for a disease, the more expensive the drug.
The rarer the disease, the more expensive the drug.
There's all these factors that affect how expensive the drug is going to be.
And if you're a drug company, you have to maximize your profit.
So you need to come up with the –
highest price for the highest number of people.
But it might be that a low number of people at a higher price is better than a high number of people at a lower price.
And so you can imagine it gets really complicated really quickly.
And it's all about the first disease you get your approval in.
So companies have to be really thoughtful and strategic to maximize their profits about what their first approval is.
Once they get that first approval, now they have to remember that they can't change the price for the next disease.
And so this is this horrible economic issue, which is just so depressing because like on the other side of these economic issues are people suffering.
The next big milestone was early in the pandemic.
I was actually driving down to Raleigh, North Carolina, had my wife in the car and I'm listening to the radio about this pandemic.
And I'm sitting there thinking, you know.
gosh, this involves the immune becoming activated and causing all these problems.
And gosh, it's going to take us months or years to come up with new drugs.
I really wish there was a lab somewhere out there that was really good with inflammatory stuff and could repurpose drugs and could direct drugs at this thing.
And then I was like, oh, maybe we should do that.
And so we decided to create a program called the Corona Project, where basically we redirected my 15-member lab to focus specifically on COVID
And early on, as you'll remember, there was a lot of drugs that were repurposed.
Some worked, some didn't work, but there was a lot of repurposing.
This is the first time we did like a very concerted effort to be like, what else is out there for this one disease?
Very much informed by what we'd done previously.
And COVID, of course, there's lots of controversy about what worked and what didn't, but the two drugs that unquestionably worked incredibly well were dexamethasone and tocilizumab.
They saved millions of lives and they were, you know, old drugs have been around for a long time.
And so that further got my wheels turning on like, what if we could create a system to automate what my little lab was doing for one disease, but we did it for all diseases and all drugs simultaneously.
And thankfully, in parallel to those dreams, the field of machine learning, artificial intelligence has matured so much that we can actually do that.
So in my case, you know, you can think about this.
We use what are called biomedical knowledge graphs, which are just sort of mapping out like every medical concept on a map.
So you can imagine like if you have this giant wall and
and every single gene, every disease, every protein, every pathway was put against the wall.
So if we were to start with that concept and say, well, what do we do for me?
Well, you'd find Castleman's on that wall.
It would only be there in one place.
And what you'd find is you'd find an edge or a line between Castleman's and activated T cells, because I discovered that T cells were activated in my disease.
You'd find another line to mTOR activation, because I discovered that mTOR activation was really up in my particular immune cells.
And then you would find a drug from T-cell activation and mTOR activation to serolimus.
Because serolimus is able to inhibit mTOR activations and able to inhibit these activated T-cells.
And so now within this giant graph of every disease, every gene, every protein, you would find Castleman's with lines or edges to these two concepts and then lines or edges to serolimus.
And you would see a connection between them.
And so now imagine doing that for every disease, every gene, every protein, basically what the world knows about all of medicine.
This leads to this, leads to this, and this reverses this, which reverses this, reverses that.
Well, now what we do is we train machine learning algorithms on all of those known treatments.
So like the serolimus for Castleman's, sildenafil for pulmonary arterial hypertension, insulin for diabetes.
Imagine training this algorithm, because machine learning algorithms are so good
And so we're giving the machine learning algorithm lots of information about known treatments.
And we're saying, this is an example of when a drug works for a disease.
And we do it thousands of times with all of the treatments that are out there for all the diseases that are out there.
And then we say, okay, algorithm, now go and score how close of a pattern the connection is between a known treats relationship for every other drug versus every disease.
So if a toenail fungus drug works
looks like there's no way it could work for pancreatic cancer, you need to give it as close to a zero as possible, 0.0001, right?
But if leucovorin looks really promising for a subtype of autism, because the pattern of connections are there and there's a clear intermediary between that subtype of autism and that metabolite, give it a high score, so you get a 0.99.
And so now what we do, we do all 4,000 drugs, all 18,000 diseases.
So it's about 75 million scores that we generate, that our machine learning algorithms generate.
And then that gives us a list in rank order from the things that are 0.99 all the way down to things that are 0.00 of every drug versus every disease.
And so we come across matches that are incredible that we never could have imagined that now the algorithm is saying, you should really look at this.
You would have to, as humans, think about 75 million possibilities.
Like my lab's really good at looking at like dozens of possibilities for like one disease.
Like my lab can spend like a year and we get through a few dozen for one disease, right?
But like we could never think about like 75 million possibilities and then compare them.
And I'm not saying AI is perfect, but directionally, it's really good.
The things that are the 0.99s are way better than things that are the 0.5s.
Yeah, I guess there's two probabilities here.
I think that one is that what is the likelihood that there is a drug out of those 4,000 that could work for that disease?
And then what's the likelihood that you or anyone else is going to find it, right?
Because it's just like, A, does it exist?
I think that A, does it exist?
uh this is obviously a really hard thing to guesstimate on but like i'm gonna say somewhere in the realm for any given disease somewhere in the realm of maybe 10 to 20 that there's something out there um and then in the realm of are you or is a team going to find it in time for you it becomes much lower than 10 to 20 likelihood right just because the the steps that have to happen right
And so for us, you know, we're going to be the organization that is going to identify and unlock as many of these drugs as possible.
So that way we don't have to be throwing Hail Marys so that like when you get diagnosed, it's, oh, wow, you have pulmonary arterial hypertension.
You should just take this medicine.
Oh, wow, you have glioblastoma.
You should take this medicine.
And so we've intentionally taken the approach of let's use AI and data to find the best uses for the best drugs possible.
so that we can move them forward so that way we aren't doing Hail Marys.
But the reality is, Latif, is that people are suffering all the time, and we are contacted all the time, and we want to help any way we can.
And we're going to be making our algorithms publicly available in about nine months' time.
But until then, we want to continue to improve them.
We feel this tremendous responsibility that once we share it, that it's out, right?
And so we're going to continue to improve it over the next nine to 12 months, but then we will share it.
So the intention will be for doctors and researchers to use it.
So that way they can come up with new areas for research.
They can think about it for their patients.
But the reality, I think, is that patients will also use it.
Yeah, so we call it the matrix.
Everything has to have an acronym in my life.
It's so it's ML aided therapeutic repurposing and extended uses matrix.
Repurposing R, I in, and then it gets a little sloppy then, extended.
We're using the X in extended for matrix uses.
Eustace doesn't get a letter.
Okay, because you're a fan of the movie or something?
It actually is a matrix in that it's 4,000 drugs, it's 18,000 diseases.
So we're building actually a matrix of drugs versus diseases and a fan of the movie.
Yeah, so we're still working on some of these things.
We're actually literally talking about prototypes and processes, but I can tell you that there'll be the ability to type in the name of your disease or maybe the drug that you care about, probably more likely the disease that you care about, because most people care about diseases as opposed to drugs, but then actually it'll look at a rank order list for that disease.
So say like, oh, wow, I care about ALS.
These are the 4,000 drugs ranked in order for ALS, according to this AI platform.
That, I'm almost certain, will be available in a format like that.
But the bells and whistles, we still have to work out.
I mean, what you're describing would be sort of like a holistic patient support treatment tool.
And we're really not building that.
You know, I hope someone does like someone should build that.
But we're not building a tool that is, yeah, is going to be that sort of treatment copilot, though.
I would love for someone else to do it.
Yeah, and these are in the same list that we're getting on our medical team and our research and development team.
We're giving you the same results and same scores that we're getting because we feel this obligation or this responsibility that if we're going to put our eyes on them, the world should be able to put their eyes on them.
Here are the tools that we use.
Our medical team uses these same machine learning algorithms.
You can use them too, but it's important to remind them that when our medical team uses those machine learning algorithms
and they come up with something like lidocaine for breast cancer, we still then go on to do a bunch of laboratory work of lidocaine in breast cancer.
And then we think about doing the right clinical trial of lidocaine in breast cancer.
So it's not like we use the algorithm to immediately move forward into action.
We use it to then plan out what to do next.
So our feeling is that we as a nonprofit at EveryCure, we're only going to be able to go through like dozens.
I mean, if we can get to the hundreds, I would be over the moon about it.
But like there are still thousands of diseases that like could potentially benefit from our scores that we'll just never be able to get to unless it's like because the list that Matrix is spitting out is just so big.
It's so big and it's so powerful.
The thing is, when we look at the top, we are blown away by the number of promising drugs.
And actually, some of the cases, there's actually been clinical trials that have shown the drug works.
But someone stopped after the small trial because there was no way to commercialize it.
So one part is, let's make it available to the world so that other people can pursue these things that we're not able to go after.
And the other is sort of probably a little bit inspired by
Maybe we shouldn't be so paternalistic in medicine, and maybe we should allow this information to be out there.
Of course, when I say that, I do cringe just a little bit because I don't want us to create problems by putting this out there.
But it feels like the responsible thing is to share the scores.
but to appropriately caveat them and disclaim them to say, like, these are for research purposes.
At EveryCure, our nonprofit, we don't take a score and then put that drug into a person.
We evaluate it by MDs, PhDs, and MD-PhDs.
And then we do laboratory studies.
We work with experts to get into guidelines.
So we want other people to take a similar process.
A couple of things immediately come to mind.
I mean, number one is patient harm.
A patient taking a medicine that causes harm to them that had not undergone the studies necessary to evaluate it in that disease.
Now, the good news is that every drug we score is already FDA approved for something.
So we're not, there's no drugs on there that like someone would be like, oh my gosh, just never got a regulatory approval.
They all have been approved.
Actually, I will tell you, there is a drug repurposing platform that I will not name on this podcast where literally one of the top five predicted drugs for Castleman disease or predicted treatment for Castleman disease is car fume exhaust as a treatment.
This was the top predictions.
You'd be like, you know what?
And I was like, oh my gosh, that's the problem.
I just haven't been inhaling enough car fumes.
That's why my Castleman's is out of control.
I just needed to inhale car fumes.
So I say that not just to say that, oh, my gosh, this one platform had this bad prediction, but it's to say that AI is going to make silly predictions that make no sense.
That's why humans have to be a part of this and humans who can critically evaluate this and say, like, this is not good.
So I think the most important thing is going to be.
I think how we communicate around these scores when they are made publicly available, that these scores are intended for our research team to find things for us to research more.
These were not ever intended to be, you know, scores to say this thing that's number one is what I should get because that's going to save me.
And so I think it's going to be context is going to be really important.
Your point is that you can say it all you want, but for that, that just may not stick, right?
I think that the way to make it stick is...
I think it's trying to explain that we don't use these predictions to decide how to treat someone.
This is not, and maybe even to use the terminology, this is not a solution engine.
And we at EveryCure do a lot of further work.
We hope you, if you're going to use these scores, will also do further work.
And so if you're a patient, that might mean working with a research lab to do the work to figure this out.
What we recommend is talking to the disease organization that you're a part of, whether it's the ALS Association or you name it.
It's talking to a lab to see if there's further work you can support.
But none of those options are go take this medicine.
And you can really see the concerns.
I think that where I go when I think about COVID is not so much ivermectin, but I go to dexamethasone.
So dexamethasone saved millions of lives during the pandemic.
It was the only drug, Latif, that was recommended against when the pandemic started.
It was whatever you do, don't give people a corticosteroid.
Corticosteroids weaken your immune system.
Literally, there was no recommendation for what to take.
There was only a recommendation for what not to take.
Well, some amazing pioneering doctor in the UK still decided to do the trial of dexamethasone, and it worked.
It reduced mortality by 35%.
But the prevailing medical system believed that it would actually be harmful.
So we didn't know what to do.
We just don't do dexamethasone.
Turns out dexamethasone actually reduces risk of death by 35%.
So I'm so glad that someone asked the question, are we sure dexamethasone shouldn't be used?
I'm glad that Dex saved millions of lives.
And I'm glad that there was sort of one doctor who was willing to go against everyone else.
And so I think that the whole point of this is to find dexamethasones, not ivermectins.
If there's a drug that someone thinks might work for a disease and it's snake oil and it's not working, we want to study it.
We want to prove that it doesn't work.
If there's a drug that looks kind of promising but no one's studying it, we want to study it and prove that it does work.
We just want to prove that they work or they don't work.
Yeah, I think where my mind is is that –
I think I'm still so appreciative for what doctors do for patients and that doctors bring just this laser focus on helping the person in front of them.
And I'm so grateful for all the things that our biomedical system has figured out are true.
Like this drug works for this disease.
Like I'm so grateful for that.
So I don't wanna break down any of that.
Like I want everything about our doctors caring about patients
and the relationship, and I want everything about all the known knowns, like where we know this drug works for this disease.
I think what I really, really want to bring forward is uncovering the unknowns.
So that way those doctors can use, that unknown can become a known, so it can be easy for them to use it.
I don't want to create some crazy new system where patients are picking drugs off AI.
Like I want to use AI so we can find out what we can elevate to the level that a doctor feels comfortable.
That steroid actually could be useful for this thing.
Never would have thought about it.
They did a clinical trial and it works.
So like I'm going to do that.
So in my opinion, it's not about breaking down the system.
It's about enabling the system to do exactly what it's trying to do.
but that we're caught up in these assumptions that I think we have around what we know and what we don't know.
And I think we're really, we're certain, we're very good at what we know.
Like I totally, I believe everything we know in the system is rock solid.
I think we're just not as good at understanding what we know to be not the case versus what we just don't know at all.
Like the amount of our, and actually there's a term for it that the computer scientists use, and that's the ignorome.
which is basically the things we don't know about medicine.
Like the ignoram, I think, is a lot bigger than most of us in medicine want to appreciate.
And I think if we can be a part of uncovering the ignoram and making it less ignoramus or whatever it is, then I think that that's where we can serve medicine, doctors, patients, and not to try to break it down.
And it's really about lifting things up.
So I actually think that during this discussion, I think you've actually sort of opened my eyes a little bit just because you sort of like highlighted to me, I just told you the three most special months of my life were the last three months of my mom's life and we weren't fighting for a treatment.
Yet I just try to extend other people's lives with the drugs we already have in my own.
And so I think everything is context dependent.
And I mean if – I think that what he said is conceptually correct.
But I think that when you feel it, when you experience it, especially when you experience like the positive side when you do make it –
It creates a new sort of value that you put on if you do get extra time.
But of course, at the end of the day, this is like philosophical around like individual versus collective societal.
Like, you know, there are people that will say like, I want to die at 70 years old because I don't want society to have to take on my burden.
Like even if I'm not sick, like I just should die at 70 years old so society doesn't have to pay for my costs.
And then there's other people who are like, we're only on this earth once, so I'm going to squeeze out all the time I can get.
I'm going to live as long as I can get.
I don't think that I'm sort of on either camp.
I think you can be reasonable and decide.
I mean, I should also share about a patient recently that –
that I had helped to discover a repurposed drug for.
And I mean, it got him out of the ICU.
And I remember sitting with my team and jumping up and down.
And like, when we got the news that, that he was responding to this medicine, it was like, what was the sickness he had?
He has calcium, the same exact subtype that I have.
Uh, it was just, I was so excited.
I remember I cried tears of joy.
I was so happy that we found this drug for Paul.
The next couple weeks went by and he kept getting better and he got out of the hospital and I got in touch with him and he explained to me, David, this drug got me out of the hospital, it turned everything around, it saved my life, but I feel horrible on it.
It makes me nauseous, I'm vomiting all the time, it's controlling my disease, but I don't like the way that I'm living.
And he was a 70-year-old gentleman and he decided to go off that medicine
for the exact reason you mentioned, Latif, is I want to spend the time that I've got with my kids and with my wife.
And I was like, Paul, but we already found something for you and it got you out.
Like, we've shown that we can do it.
Like, let's try another one.
And he said, no, David, I don't want to.
And then the two of us just cried, you know, tears of sadness, you know, cried tears of joy before that cried tears of sadness.
He knew that if he put me on the case, he knew that I was going to be all in, and he knew that I'd been able to do it once before, but he told me he didn't want to.
I was very sad, but I felt that it was absolutely the right thing, and I 100% respected it.
I understood that for him at this moment in his life, that was the right decision.
And he passed away a few days later.
Maybe that's the big takeaway, is that it seems like everyone wants to tell us what everyone else's decision should be.
So like, it's best for society for you to do this, or it's selfish of you for you to do this.
But I think that maybe that's the real fundamental thing this comes down to.
It's got to be that patient's decision.