Natali_Mis
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Kirk Spano and Bhavneesh Sharma talk biotech, pharma, the unpredictable FDA and why complete response letters are a good contrarian buying opportunity (2:10). Understanding investment potential and unmet medical needs (11:10). CRISPR technology, gene editing disappointment (17:00). How AI is revolutionizing drug discovery (30:20). Why Kirk likes Pfizer, Bristol-Myers and Bhavneesh likes Recursion Pharmaceuticals (38:35).
Transcript
Kirk Spano: Hello, everybody, Kirk Spano and Bhavneesh Sharma. Bhavneesh is a doctor, a business guy, and an analyst on the biotech sector. I have made a lot of money myself on biotech.
As most of you know, I made my first big pile of money on a company called Exact Sciences (EXAS), which I started investing in way back during the financial crisis at below $3 a share, rode it up to $30, sold some, bought it back below $20, rode it up to $70, and it’s gotten to well over $100 a few times and I have no idea where it is now, but biotech is a very hard sector to follow.
I have committed a lot of time to it and it still isn’t enough. It’s one of those sectors that I think requires a specialist. So, I spent the last couple of years really going through a lot of other analysts’ work, and I landed with Bhavneesh. I like the way that he breaks down the investments to concepts that we can understand and hopefully make some money on as companies execute.
The one thing about biotech is that a lot of companies never get over the hump from – in the clinicals to a product. And that means that you can trade these or you can invest.
And if you invest, you really need to have a good background. And you have to understand that until a company gets to having a functional product or is really close, right, to where you can forecast, it is going to have a product that is going to sell, generate free cash flow. You have to be aware of the amount of risk you’re taking.
So, I like the way he looks at risk. I like the way that he breaks down the companies. He’s a doctor, so he understands practical uses and what they might grow into. So, with all that, Bhavneesh, how are you doing today?
Bhavneesh Sharma: Yeah, I’m doing very good, Kirk. And like you said that, yes, the biotech sector is considered very risky. And traditionally, it has been considered risky. Now, one of the reasons is that most investors or traders they try to play the catalyst.
What it means is that they would just buy a biotech or pharma stock about three to six months before a catalyst, which is like a FDA decision date or upcoming data release, like Phase 2 or Phase 3 data.
And one of the things that I have learned in my more than 10 years investing in this sector is that it is for any expert, even for a top hedge fund guy, it is never totally possible to completely predict which way the FDA decision or which way the results of a data will go.
But one thing that is almost impossible to predict is the manufacturing part. So, the FDA surprisingly is very strict about manufacturing, and most companies they outsource the manufacturing. And so FDA conducts the inspection of those manufacturing sites. And sometimes if they are not happy, then they will reject the application.
I have found that these kind of manufacturing-related CRLs, we call them complete response letters, are a very good contrarian buying opportunity because these manufacturing problems are very easy to fix. It’s not like the the company is not being asked to conduct another trial, which takes like two years.
So there might be just some small manufacturing issues or drug labeling issues and usually these are fixable within six months. Buying the dip up, if you don’t have a position in that, it is a very good idea because often the company’s stock will fall like 50%, 75% on the CRL. And then if you buy them, then it might like double over the next one year. That is only for manufacturing-related CRLs.
It is almost impossible to predict and sometimes the FDA just rejects it on any ground that sometimes there is a benefit, but at the same time they say the benefit is not enough and those things are relatively still easier to assess, but at the same time if it is in a very hard to treat cancer and these are very refractory cases and the company’s drug has shown efficacy, then even if it is a little bit of efficacy, it is borderline significant, these patients still have a chance of living little more.
Usually, FDA is a little bit lenient towards the cancer applications, but you never know they still might just say that. And the reason why they do that is because these therapy as you know, the drug prices are very high in the U.S. and they are trying to control the costs.
So they have their own departments, which also assess what is the economic benefit of that. Like, what is the economic benefit of approving this drug at this cost and then how much we can save from this patient’s hospitalization in case a drug is not given.
So that is one of the big factors why they try to – they reject a lot of drugs, especially in the crowded areas. Lot of companies are trying to develop drugs in the crowded areas because they say that, “Yeah, okay, this is like a $10 billion per year market. And even if we occupy 5% of that, it’s like a lot of revenue.”
But the FDA is very, very strict about that. And they will only approve the drug if it really shows a benefit. There are no existing therapies and if the patients really stand to benefit from it.
KS: All right. Well, that’s a lot to unpack. So, let me see if I can go through a couple of things, so folks can wrap their heads around it. So, when I talk to people about investing, I describe it as not being as so straightforward as they might think.
The ability to estimate free cash flow yield and how much money a company makes really there’s spreadsheets and now there’s AI. So, it’s not difficult to know what a company has already done or is doing. Forecast in the future is the hard part.
So, it would seem to me that in those cases where there is a drug approval, but the manufacturing gets delayed because the factory isn’t set up right or they had a glitch, whatever reason, that might be one of those situations where when you see it, you should recognize, ‘Hey, this is something that’s coming.
They just have to tweak the factory and the manufacturing process, which may only take a quarter or two or three, and then now it’s going to come back.’ You can take advantage of the short-termism of 80% of investors. The average holding period for most American investors is under a year.
If you can find one of these situations where everybody runs away, right, they’re run away, run away, it’s not getting manufactured this quarter. And say, ‘Well, thank you for giving me the 50% or 60% or 70% discount, I can buy that and I’ll wait a year or two and maybe I’ll triple or quadruple my money.’
That sound – is that something that you see from time to time then? That’s just one of those things that you’re like, “Hey, if I see this, I’m going to recognize that might be an opportunity.”
BS: Yeah. So that’s right. So like you said that I also prepare these discounted cashflow models for valuation of these companies. And if you really – somebody who’s making them knows that when you are forecasting, then even if there is a manufacturing-related CRL, you are just shifting your spreadsheet by another year to the right.
So, it does not mean that the probability of that is not – has not gone down by 50%, like the way the stock is down 50%. So, it is, of course, an overreaction.
And as you said that overreaction – these kind of overreactions, when there is like a blood on the streets and nobody wants to buy, it’s a very good buying opportunity, but at the same time one has to be careful also that like sometime the stock may initially dip and then initially it may stabilize a little bit or maybe go up another 10%, 15% and then make a new lower low.
So, it is important to buy in installments like maybe a one-fourth of your planned allocation when the stock takes a dip and then wait another maybe three, four days and then see if it makes a new lower low, but it needs a lot of patience. And really what happens is that the stock may continue to languish going nowhere and for up to three, four months.
And so, a lot of investors get impatient that it’s doing nothing, but suddenly, what you would hear is that so what happens is now the companies fixes the issues and now they request a meeting with the FDA, which is usually a Type A Meeting.
And what happens is that usually, when the news comes that this company has requested a Type A Meeting with the FDA, then after that usually the stock starts going up. And from there it may go up, like then they will request a meeting that usually the FDA gives a decision in about two to three months. If they approve it, then the FDA decision date will be another six months.
So, another nine months from there. And that is – the nine months when the stock really gets a momentum, but initially, three, four months, it may be very painful to hold it. So, it needs a lot of patience.
KS: Yeah. Full disclosure here, I’ve never owned a stock that went down for a while.
So you touched on it earlier the whole idea of an unmet medical need. And I don’t know if you put it exactly that way, but that’s what you were getting at.
Talk to me about, and talk to the people who are listening about unmet medical needs and how the FDA looks at that. So that as an investor, you could understand what really the potential is.
So, in that realm of unmet medical needs and how the FDA makes decisions, what are you seeing as the standard behind it and then the trends? What is the FDA behaving like now?
BS: So, of course, the FDA has become more selective and they are approving only the therapies which are really making a difference, which we also call as disease-modifying therapies. So if an investor is looking to invest in areas of unmet need, then given that investor has the right kind of resources, then he can also read about a disease and then he can read what are the current therapies and now what the company is working on.
And then he can make an assessment that this company is really developing a therapy, which is going to totally change the treatment landscape of this disease.
And, of course, for that he has to understand the data also, but one thing that here I find useful is that over the many years, I learned that the companies they, of course, they are trying to pump their pipeline, they are trying to pump their stock, so they are very biased about pumping their products. And even if there are a lot of existing therapies already in development, but what happens is that lot of therapies they are being used as what we call it off-label.
Off-label means that the FDA has not approved them, but these therapies for years have a track record of safety, as well as they have shown benefit in similar disease. So, the doctors they can prescribe those therapies based on their clinical judgment even if they are not formally FDA approved and the insurances pay for that.
But what happens is that these companies when they are reaching out to potential investors and like VC funds, there – if you read their investor presentation, so what they write is, this is going to be the first FDA approved therapy, this disease, which is legally not wrong, but it is misleading because investors think that unless FDA approves a therapy, it cannot be written by doctors, which is not true.
So, to learn to how to distinguish between this, now I use a resource, which is being used by the medical doctors worldwide and I have been using it since my clinician days called up to date.
It is not very expensive really. It is available also to the non-clinicians as well and not really expensive probably like $500 a year. But it contains the articles like review articles on different disease, which are written by the experts in that.
And so that is what I always do. When I’m researching a company, I will first – a particular part of the pipeline like this disease, I will first go to up to date and read about it and what are the latest developments which are there.
And these latest developments keep on changing, so it is very difficult – very important to be updated about that. And then after that when you know this is a current treatment landscape and up to date also, we will mention often the therapies which are in development.
So, that is also very useful to assess the competitive landscape of that therapy. And then after that you get a good background about the disease and then you go back to, “Okay now the company is developing this treatment.”
And then you are able to assess it better that how disrupting it’s going to be and make an independent decision rather than just – otherwise, you just have the company’s promotional resources, like investor presentation, which are really very biased.
KS: Right.
BS: And so, I find that approach very, very useful.
KS: So, you’re saying that companies talk through book.
BS: Yeah.
KS: So one of the things I always point out to investors that read my investment letter or my clients at my investment firm is that all information that is published is out there, right? So, it’s all out there. However, it’s not all out there for free.
There is a lot of stuff, whether it’s the energy industry or biotech or technology, that if you’re not paying for an industry level publication or two or three, you’re not really keeping up because what ends up in the press and what ends up being required to be in financial documents from companies or in their presentations, a lot of times that’s partial.
A lot of times it’s slow to get out there.
And that’s one of the reasons I use technical analysis, because if I see a price move that is not explained by public free to get news, that means one or two things. It means either there’s something out there that I should know, or it just means traders are going nuts and betting on something.
Today, a lot of it is just traders going nuts and running back and forth and, “Oh, I’m going to bet this way, I’m going to bet that way and I’m going to bet faster than you. And I’m going to beat you, and they’re making their two points.”
But the moves that you can break down and there is a movie called The Matrix, where the star, Neo, starts picking out digital bullets from the sky, and it’s because he can see the code. But one of the things that I’ve been watching, because I was a Quant, oh man, almost 20 years ago now, I’m getting old.
The quantitative approach to watching the markets yields clues the way that the charts used to. The charts, I think, are behind now and they’re open to interpretation like going to an art gallery. Two people can look at the same picture or same chart and come up with a different story.
But if you look at the math underneath, you can start to break out, “Look, there’s algorithmic accumulation going on.
There’s big players that are trying to get this and they’re buying the dips.” And then you see that chart move up. So, there are clues out there to when you really need to look for information and sometimes you can just follow along. That’s the whole point of trend following. But with biotech, I think the public, and this has been my experience over three decades of investing.
I think with biotech, and I remember when they decoded the genome and I told somebody, “It’s going to take them 20 years to really get to figure out how to use this.” And they said, “No, it’ll be two years.” And I’m like, “That can take two years. It’s going to take between 10 and 20 years.”
And here we are 20 years later, maybe a little bit longer than that. I forget when the genome was first really decoded, and they still have lots of gaps to fill in over decades, but it’s been over 20 years and we’re just really starting to see big applications in genomic medicine.
So, let’s go to gene editing. Tell me about gene editing, because this is where some of those stocks got destroyed and now some of them are going up. So, tell me, what do you see in the whole space of gene editing? And then we’ll pivot into how AI is going to affect all this, but in the gene editing field, do you see big things coming in the next five years? Is it longer? Is it right on top of us? About where are we in that whole process?
BS: So, gene editing, when Jennifer Doudna and their team, they – she’s the woman who discovered CRISPR and she got the Nobel Prize for that. And when they initially published their paper, then when they started these companies, the publicly available companies, then the anticipation of the investors was that “Okay, we have found a cure for every disease under the sun.”
That we can really go inside the gene and we can do whatever. So, there are genetic diseases, but at the same time, these companies were also targeting cancer as well. So we have three publicly available CRISPR gene editing companies.
CRISPR is the technique that I like although there are a couple of other techniques, but they have not really done very well. And these are (CRSP), CRISPR Therapeutics; Intellia Therapeutics (NTLA); and Editas Medicine (EDIT). So, these are the three big publicly available companies.
And now what happened is that these – now one – I mean, I don’t know I should call it a mistake or, like these companies are – have very big scientists on their panel, but they were targeting genetic diseases which are very, very common, very prevalent, but at the same time, they have very other effective therapies available. And like really the people are not dying if you don’t give them the gene therapy, so – the CRISPR gene editing therapy.
Now, one thing which went wrong was the efficacy. So, people were thinking that this is going to be a one-time cure, that we are going to just edit the gene and then this is going to be – now the patient is completely cured.
And the reason why this should be the ambition of these companies is also due to the very high cost of gene editing. These companies are charged – the minimum is 1 million per patient and some of them are charging like $2 million, $3 million per patient. So, from the cost point of sense, also this pricing would really make sense for a curative therapy.
But what happened was that the human body is not a computer where you can just go and put a new chip and it is going to be now fixed for another five years.
The human body is very complex, that is the problem. So, what happens is that whatever the problem is there, the kind of environment is there in the body, it will find some way to overcome that curative thing that you are doing.
So what happened is that they were able to show efficacy. There was no problem. Efficacy was there. They were able – for the sickle cell disease, they were able to show that there is a significant reduction in the blood transfusions.
But what happened is that over – it did not – the efficacy did not last very long. It did not last beyond maximum, I would say, two to three years. And this gene editing cannot be given repeatedly.
Like normally, what you would do is, you would dose a drug repeatedly, but here, you cannot just give this gene editing every six months that “Okay, the efficacy is going less, so let’s give another dose because they are very expensive.” So that was a big disappointment and that is why the stocks of these companies have been down.
So, one thing is the efficacy and the second thing is the insurances are, especially in the U.S., they are really questioning and it is not being covered by lot of insurances like sickle cell disease is a very, very common disease in the big cities.
And so insurances will say that, “Okay, if he comes with this worsening of the disease, you can just give him some blood, painkillers and then send him home.” And that is very easy for us even if he comes every six months, but here spending $2 million, $3 million for that is, like they will say that they have their own criteria for that.
So, that gene therapy is not available to all the patients, insurances are not covering and the insurances also have a reason for that. Like I said, the efficacy because they are also saying that this is going to be just working for two to three years, and after that the patient is going to go back to his same previous regime of repeated hospitalizations, so we don’t want to pay.
So that is why it has been a big disappointment. Now – but it is not completely a disappointment. One of the areas where these therapies may work is what I call, they’re called in vivo therapies. So, in vivo is where you are trying to edit a tissue inside the body. Otherwise, you take the cells out, like for example, these blood disorders like sickle cell disease and thalassemia, you take out the cells, you edit them and then you inject them back, so that they go inside and multiply.
But XYO, you inject these edited genes through a lipo – nanoprotein particle which then goes to the tissue which is usually the liver and then it fixes the gene. So, Intellia Therapeutics is one of the companies that I like in CRISPR gene editing and they are also working on some interesting applications for that. So, that is one company that I like in CRISPR gene editing.
KS: Because they’re doing the in vivo?
BS: Yeah, that’s right.
KS: Okay. So, it sounds like the story in CRISPR and gene editing is they can do a lot of things from a treatment standpoint. It may not be a permanent cure and it’s really expensive.
So, from a cost standpoint, from an economic standpoint, they’re not there yet. So that sounds similar to solar energy and a lot of technology, nuclear energy, where we can do things, but it’s super expensive and it makes it not practical.
What do you think the timeframe is for them to make it more permanent on the cures, all the treatments becoming cures and shaving 90% of the cost off, which is what it sounds like they need to do? Is that a two, three, four-year thing or is that a decade out or longer?
BS: That is really very ambitious because one problem is that we – CRISPR gene editing is – the patents are only available to these three companies.
There are other companies who want to move into this area and they want to address some other diseases for which right now the gene therapy or gene editing is not being logged in, it is very difficult for other companies to do that.
So these companies, I think, it’s a very ambitious thing because these companies already have a pipeline. They are already pumping hundreds of millions of dollars into conducting clinical trials into these ongoing applications.
And most of them are targeting the disease which are very crowded like already the gene therapy is also being tried in that.
Now, here, one thing I would say is that gene editing has really disappointed because when the data came out, then the hope was that now gene therapy is where you are replacing the whole gene. Gene editing is where you are going inside, making some cuts and there is – the whole gene is not going to be bad, it is just removing that bad part and then putting in normal part.
So the hope was that gene editing would be better than gene therapy. Gene therapy is a much older technique, but the results from gene therapy have been better than gene editing in the same disease. Like for example, sickle cell disease is one of the biggest areas in this gene therapy and editing.
And bluebird bio (BLUE), a company, about one-year back, they got approval for their gene therapy in sickle cell disease. And their data was better than CRISPR Therapeutics, which is a company which is in the CRISPR gene editing. Now, they also got approval for their gene editing therapy for sickle cell disease, but the gene therapy data is better than gene editing.
So, there have been – the results have not really matched up to that hype in CRISPR, I would say in human trials. Now, in – there are other areas where CRISPR is being used, like for example, in agriculture, et cetera. And over there, there are companies like, I mean, that is not, I want to go into much detail because I don’t really totally cover that, but in the agriculture and all, still CRISPR gene editing has shown some benefit.
KS: Okay. Let’s move on to the article that I asked you to write that you were already working on. So, I asked you to do something you were doing. I saw a couple of executives on Bloomberg from pharma companies and a couple of private equity guys who big wallets get good information.
And they were discussing how AI is going to impact drug discovery. And the thing that I thought was the easiest to understand was one of the private equity guys came on and he said, “Look, when they do drug trials, most drug trials don’t move past Phase 1. And he said, maybe one out of 10 drug trials finally get approved. And he said, he didn’t know, but he said, he thought it was less than that.
So, he said, but with AI, what we are starting to see is that rather than 5% or 10% of things that they submit that they’re working on getting there to ultimate FDA approval, he said that AI might double or triple that rate because you – first of all, you get rid of some things that you should never bothered with in the first place, but then you make improvements much faster after you start to accumulate data.
So, he said that we might be able to discover drugs two to three more times more efficiently. And he said, that doesn’t mean that every drug is going to get FDA approval. It just means that we’re going to fail a lot less and we’re going to succeed quicker.” Does that make sense to you? How are you looking at AI and drug discovery?
BS: Yeah. So, AI has really revolutionized drug discovery. And now the reason for that, and this also answers your question that why this executive is saying this. Now, the reason is that there are lot of therapeutic targets inside the body and usually, like whether you – this therapeutic target is on a protein or a gene et cetera, but before AI, it was very difficult to find a good target and then develop drugs against it.
So, the only way to find if really a drug would work against that target was to conduct the trials, which we start with the Phase 1 trials, before that we have to first conduct the preclinical trials which are done usually in mice et cetera and then you move on to the Phase 1 trials in humans.
But that was the only way to really check that it was like shooting blindly that okay you have a target, there is some background to that that this target may be effective, but really, the drug has to go there and completely bind and block that target either block or either stimulate that target for it to be very effective.
And the only way to do that was to conduct these trials and then if it fails and then do the other trial and sometimes they will succeed in the animal trials, but they will fail in the human trials. But now with the AI what has happened is that, one thing that has happened is that before AI, we did not have the structure of the major proteins in the body.
So, proteins, the way in the human body it works is that we have the genes and there is a DNA, which codes the information for a protein, it passes it to RNA. The RNA will then make the specific protein for a function and the protein will then go and do that function and the protein may be either good or bad.
So if it is good and it is not working that may cause a disease. It’s a bad protein it goes and works that may cause a disease. So you – these drugs will really act against these proteins that they will either block the bad protein or they will fix the good protein so that it works. Now, we did not have the structure of these proteins, the three-dimensional structure of these proteins.
Now, one of the biggest breakthroughs that happened in the drug discovery was about 10 years back when Google published their database of The Human Protein Atlas. And it was published by Google.
And using their AI techniques, they were able to find the three-dimensional structure of all the major proteins in the human body and they published it for free and then they invited the researchers that look into that and now try to develop the drugs using these structure of proteins.
So that was one of the biggest breakthroughs. And this is a project which – it is such a big project Google (GOOG) (GOOGL) spent over $1 billion for that and they published it for free. And really, it was very difficult for any other major, let’s say, university wants to do that, then they don’t have the resources to do that. But when the database was opened, now the researchers they started looking into that.
And now using the AI really what you can do is that you can simulate, you can like what you can do is that you have, let’s say, protein and you have a three-dimensional structure of that and you can really look – you can really simulate that which of these targets are going to be most beneficial.
So, they will – like there are different areas where the antibody can go and bind on that protein. And using these AI models, they are able to calculate very fast like within a period of hours or days that which of these areas are going to be most beneficial and really the binding of the proposed antibody to the site on that three-dimensional structure of the protein will work or not.
And so it is literally like they are like playing, they are doing everything in their computer, simulating that. And after now, this – the AI model that told them that, yeah, the drug is binding good at this – whatever drug you are trying to develop is binding well at this site. And this site from our past scientific literature has shown a contribution to a disease. Then, now you have increased probability that, yeah, this is going to be a drug which is going to work in clinical trials.
Before that this simulation could not be done because really, we did not know about the structure of these proteins. And now we have that information, thanks to Google. And that is why earlier the drug discovery, the companies would just go and blindly shoot against these targets by conducting clinical trials. Now, they do these simulations and only then move the drug to the animal trials and then to the human trials.
So, the drug discovery which used to take like months to years now takes hours to days. So, it does really not only does speed end up the process, it has also increased the probability of a successful outcome.
KS: All right. So, I’ll try to summarize, this is how I’m thinking about it in my head. 25 years ago, we started to be able to map the human genome.
Over a couple of decades, we got most of it done, but we had a problem with the proteins.
Google released a database.
And back in COVID, we started doing mRNA shots and there’s other applications for mRNA. Two years ago, maybe, I don’t remember exactly when it was, maybe it was last year, but it was recent that AI modeled everything they had to do with proteins and got it like 98% right, and they’re just cleaning up the last little bit.
So, the AI took all this data that had been around for a decade and other data that had been developed over decades and started putting it all together. So, I read pretty much everything I can get my hands on that has to do with Pfizer (PFE).
And the reason why I’m interested in Pfizer, and I follow most of the pharma companies, because I just know that as they go through their expiration gluts and then they replace it with new revenues, that watching that moving of their revenues is one you can usually get a bargain.
So, everybody goes, “Oh, they have three drugs expiring, I better sell the stock.” Well, a year later, they have three new drugs, right? So, what I have seen with Pfizer is the data that they have is off the charts. I don’t know if it’s the most, I don’t know if it’s the best. I just know it’s more than I can handle.
And so, I read what they do and they are combining mRNA and the proteins and gene editing and gene therapy and AI and all these things are starting to come together to the point where suddenly their pipeline and several other companies, Bristol Myers (BMY), I mean, there’s a number of companies out there that their pipelines are super full at a time when everybody is fretting about, “Oh, look, this drug and that drug and the other drug are going generic.”
And what they’re not understanding is, they’re still going to get some revenue and the new stuff is legitimately better than the old stuff. It used to be that the pharmaceutical companies would barely just change the formulas a little bit, get a new formulary, get a new patent and continue the old thing.
Well, the new treatments, the new drugs, from what I can tell as, I mean, I’m pretty educated on this. I don’t follow it as much as you do to down to the company level, but from a theoretical standpoint, I can tell there’s a wave of new drug therapies coming.
And I don’t know if it’s going to start slow and then snowball, but over the next decade, I would imagine that everything that we have a drug for now is pretty much going to be replaced with something better. And knowing who’s going to win there is I think going to be important.
So, I like Pfizer as, I’m not a big company guy, I like small and mid-caps, but from the big company standpoint, to find the dividend stocks that I think will keep being dividend stocks, I like to see underlying growth. I think Pfizer has big underlying growth. I think that Bristol Myers is right behind them. I think Pfizer is a little bit better, but Bristol Myers probably doesn’t suffer from as many patent expirations.
So, those two companies are stocks that I’ve recommended in my dividend service. So, I have a basic margin of safety service where I’ve recommended those two stocks because I think that A, the dividends are super strong, retirees like that, and the risk from the current price is very low, and the kicker is that I think growth is going to be much higher than people think.
So, I look at this from the standpoint of, “Okay, where can I find an investment for a certain group of people?” Like, I think growth investors can probably buy Pfizer because of the mRNA platform. I don’t know if growth investors want Bristol Myers, but I think retirees probably want them both.
As AI gets into more and more, the GLP-1 companies all had their stocks go straight up, but now little itty bitty hymns is doing biosimilars. And I think they’re going to make a ton of money because the GLP-1s from the pharmaceutical companies are such shortage and the government really likes them because if everybody loses weight – three quarters of us are fat, if everybody loses weight, that’s good for health, right, and then that’s good for Medicare because the government doesn’t have to pay as much.
So, you have these tug of wars going on and you have these imbalances. I think it’s interesting what AI is going to do. Of course, we know that a couple of years ago, Watson from IBM diagnosed a whole bunch of patients and prescribed treatment plans and was about 3x better than doctors. And people said, “Oh, the AI is smarter than a doctor.” I was like, hold on a second.
The AI doesn’t have legal liability. Doctors are forced to start at least intrusive, right? The AI can say, but I think this is right. And the doctor, I think, often knows, but they’re like, I got to go through the steps, so I don’t get sued in case we got to prove that we’re doing it in order.
So, I think that there’s lots of neat things out there. I think AI is not only going to help with drug discovery, I think it’s going to limit legal ramification. I think it’s going to reduce litigation.
So, I guess the last point would be, as AI gets applied to different industries, whether it’s healthcare or housing manufacturing or whatever, or energy, as AI gets applied, I think, some of the really big opportunities are in the companies, not that are creating AI, but are – that are finding a way to use it effectively, because they can increase their margins and they can do more without – if they had to hire three people before to do the new thing, maybe they only have to hire one now.
And I think that that’s what’s coming because of AI. I don’t think it’s going to wipe jobs out. I think it’s going to get rid of a lot of the bullshit work and it’s going to get rid of a lot of the trial and error and it’s going to make us more efficient and we’re all going to have Fridays off most of the time.
So, give me a couple of companies that you think that in this new developing arena, where AI helps to remove mistakes and it speeds the process up? Where do you think we’re going to see some breakthroughs? What companies are you looking at going, these guys could be a big deal?
BS: So, I have one topic in the AI. Now, AI in drug discovery right now almost every big company is using it because they have the resources and they have the data scientists to look at this proteomics atlas and try to develop the disease, but the important is the companies which have their own software, their own databases where they have already modeled like billions of targets from these proteomics and genomics.
And one of these companies that I like is Recursion Pharmaceuticals. Its sticker is (RXRX). And this is a company which has their own software called Recursion OS, and they have already modeled billions of potential targets in the proteomics and genomics.
One thing that these big pharma, they have so much of resources that for the smaller companies to compete with them in AI and drug discovery is very tough.
But despite that Recursion Pharmaceuticals has developed its own software and this has been such a big breakthrough that NVIDIA (NVDA), which we all know, the big GPU company probably the best pick in the AI right now, they also invest in other companies like VCs and they invested about $150 million in Recursion Pharmaceuticals stock last year and that was their second largest investment.
So that shows the potential of this company’s software platform. Now this company has partnerships with other big pharma, but at the same time, they have about four of their own pipeline programs.
And I just bought this stock recently because the reason is that they also have the clinical data from these targets, at least, there are four data releases, which are going to be coming over the next about nine months. And on looking at them, I have a high probability of success. So, this is my number one pick in the AI and drug discovery space, ticker is RXRX.
KS: Ok, that’s awesome. Bhavneesh, thank you very much for talking with me today and giving us the 30,000-foot view of what I think are some pretty important topics. A lot of healthcare stocks, pharma stocks, biotech stocks are pretty beat up right now. So, I think that there’s probably some opportunities out there.
So, I’m happy that we have made the connection. And for folks who are looking for more biotech, like I said, I went through about a dozen biotech analysts, and in the affordable range, we have Bhavneesh. There’s biotech institutional analysts out there and I get their stuff when I can, but some of the multi – multi-thousand dollar platforms, that’s a little bit outside my scope.
So, I rely on you, as the expert, to answer my questions and help me look in the right direction. Because my experience with biotech, other than I found one that I understood that I thought that the risk was lower and the potential was higher and I just thought that everything was going to work.
The rest of my experience with biotech, other than a handful of buying Amgen (AMGN) or Biogen (BIIB) 15, 20 years ago, is that the small companies have been very hard to invest in for – really since the financial crisis, and I think that might be about to change.
Now, I don’t know who wins. And that’s what we have to look for is the winners, but there’s so much blood in the streets and small-cap in general that I think that there are some opportunities and it’s really we just have to – we have to get inside the haystack, right, and find those needles.
Thank you very much and publish your AI article on Margin of Safety investing. I know that it’s at your service, too. And let’s make some money in biotech and making people healthier.
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