So congratulations everyone. You've made it to the very end of the workshop Also, what a congratulate Rashad for doing such a great job of orchestrating things and making sure we all Were online and in our chat rooms when we needed to be I think Jeff and our TAs have done a superb job of Teaching about metabolism. This is certainly the most popular part of the workshop We try and give people a fair bit of time just to sort of play around and every year there's something new And and more expansive in metabolism analyst. So I learned a lot just by sitting back and watching what Jeff and the team have done Today, I'm just gonna try and do something quick before all of you fall asleep And before you get too hungry But this is to look at about the future of metabolomics It's a little speculative And so I'm certainly inviting comments or questions But it's based on Being involved in metabolomics for a long time, but also being involved with a lot of groups around the world Doing some pretty cutting-edge work in bio in metabolomics So most of you are taking this workshop because you're interested in it and most of it because you're realizing that metabolomics is growing in popularity When I started in the field of metabolomics back in 1998 It didn't even have a name We were choosing all kinds of different things. There was only one or two papers published a year As of 2019 there were more than 6,000 papers published in the field and it's growing pretty consistently The worry is Where is metabolomics going is it kind of reaching a peak right now? And you know next year everyone is going to leave and find something else Maybe everyone jumps into the microbiome or someone finds something more in epigenetics Is it going to level off? Which is sort of the way that most things have happened in things like proteomics in some fields of genomics Whereas again that continue to to climb and how much longer will it climb in terms of popularity and interest? When you look at where it could go you have to try and identify where there's some essential bottlenecks in metabolomics Some of these bottlenecks are things that we brought up already One is the fact that metabolomics is pretty labor-intensive whether it's preparing the samples running the samples or even doing the data analysis Another point we've highlighted is that often we're happy if we just get 50 to 100 metabolites when we know that in fact there are hundreds of thousands so we have very incomplete coverage and that's been a real sore point for metabolomics We know the equipment is very large expensive We've also highlighted the challenges of lacking quantification in fact proteomics Basically hit the wall and declined quite substantially in interest because it never really got around the problem of quantification We also have a real challenge in translating metabolomics discoveries either into the clinic into the veterinary classes or approaches into industry Into environmental applications. So the translation from the bench to Real-life applications is challenging and then a lot of money Has driven You know our activities in the omics world Largely sponsored by pharmaceutical companies It was big pharma that pushed genomics. It was big pharma that pushed proteomics It was big pharma that actually started metabolomics. It's big pharma that's been pushing precision medicine. It's big pharma that pushed Big data for medicine So if you can make your field matter to big pharma, then it also helps to Advance the field So I'm going to talk about some trends And I'll also discuss some aspirational thoughts about these trends in metabolomics I'm going to talk about automation I'm going to be talking about efforts to expand metabolome coverage and be talking about Quantification and the importance of that and where people are going I'm going to take some examples of how metabolomics can move from the lab to the clinic and how metabolomics is Coming back into drug development and discovery and becoming more relevant So automated metabolomics is becoming more and more ubiquitous I'd mentioned a couple of these things like Brooker's efforts in NMR to do lipoprotein juice and wine Where it's possible process samples in about five minutes automatically I'd mentioned the software tool called Kinomics some of you guys have used it, but it has a semi automatic method Science has introduced a lipidizer, which allows you to measure Fairly automatically about 1100 lipids and biocrities has been a company in Austria That's been offering kits to do targeted metabolomics first the P 180 then the P 400 then the P 500 And there are other companies as well that are starting to offer these automatic or semi automatic approaches to doing metabolomics They sort of either open the kit or press the button or load the system and you can do metabolomics while you sleep There also a growing number of service providers that also allow you to make metabolomics pretty much automatic too So you don't have to buy a mass spec. You don't have to be Someone who's spending all their time learning the techniques, although it's good to learn them So there are core facilities. I've mentioned the metabolomics innovation center and that's where My and mark currently working that's also where Jeff cut his teeth when he was starting off as a Graduate student in a postdoc their company's like metabolite There's about a half dozen Metabolomic centers in the US. There's the National Phenome Center, which is a network spreading across in Singapore Birmingham and London There's Nightingale a Finnish company that does metabolomics. It's running through millions of samples in the UK Biobank Netherlands has a core facility. These are all places around the world that do Very extensive very high quality metabolomics Measurements and so you just have to send your samples off to them and a few weeks later the answers arrive on your doorstep Of course, it's not free, but these are basically Operated as an at-cost service to to the public We've already seen some of the tools Basil was one that you guys have already used that's essentially automatic NMR Metabolomics you've seen GC auto fit also trend towards automated GC MS metabolomics and then as the kits have evolved the automated LC MS metabolomics So the idea is if if you can get These approaches more automated whether it's NMR whether it's GC MS or targeted LC MS It will make metabolomics much easier for you guys much more accessible for your colleagues And much more useful Right now the real software challenge is trying to automate Untargeted metabolomics and you guys are seeing some efforts with that that Jeff is working towards through Metabolicalist are but it is really really challenging given the diversity of preparations samples and analytical methods But that is an aspirational goal that we would like to see in the future Expanding metabolism coverage This is the Achilles heel to metabolomics and if it's not solved soon, I think metabolomics will become largely irrelevant So in terms of untargeted metabolomics Typically only about 2% of the detected MS peaks are actually identified Most metabolomic studies report fewer than a hundred identified metabolites and yet we know there are hundreds of thousands of known metabolites Most metabolomic studies don't even reach MSI level one many failed even reached level two And in terms of coverage compared to genomics and proteomics We're at roughly one to two percent of what is possible with these other technologies Whether it's clinicians or environmental chemists or toxicologists They generally lack tool trust in the tools that don't have broad coverage and metabolomics really as yet does not have broad coverage And we talked about the the different levels of sensitivity of the tools NMR GCMS and LCMS and we know that the less sensitive methods actually tell us more about the metabolome than the more sensitive methods That's because in things like NMR GCMS. You're detecting High abundance compounds that we know a lot about whereas at the LCMS we're detecting very low abundance compounds that we know almost nothing about It's a challenge so we'd like to be able to go to more known unknowns in LCMS and as yet we haven't been able to figure out how to do that So what are these known unknowns? In many cases we have a really good idea about the compounds that are in the environment We have a good idea about the compounds in our foods A good idea about the compounds that are in our bodies But all of those things whether they're foods or pollutants or even the endogenous compounds Go through something called bio transformation or chemo transformation So these known compounds get metabolized and they're converted to mostly unknown and uncharacterized compounds And so this is called the chemical dark matter in metabolomics and we currently estimate that the number of Unknown unknowns in the universe at least the environment that we live on earth is about five million That's a huge number And you've seen this slide before as if we had to try and Synthesize all these compounds even though we don't know what they are or if we try to Plug them through artificial guts or artificial livers or artificial transformations It would cost billions and billions of dollars And that's not possible. No government's willing to to make that kind of investment certainly not now So the approach is I think that we're left with is to see if we can come up with ways of generating those structures computationally and Generating their Spectra computationally and that is something that is certainly more feasible So this is given rise to something called in silico metabolomics and this is an emerging trend that's happening around the world It's making use of computers and it's making use of what we know So we've talked about the compounds that are in drug bank or HMDB or others And those when you add them all up there about 250,000 compounds if you run them through this tool called bio transformer it does the Chemical predictions it imagines these things in the liver it imagines them in the gut And it and it does essentially chemical prediction it predicts chemical reactions And right now the estimate is that roughly each compound can produce around 20 different metabolites So these are metabolites and metabolites So that's the assumption and there's good reason to believe that that actually represents a good portion of the unknown or the dark Metabolome so now what do you do when you've generated all these structures? Well, once you've got those feasible chemically or biochemically feasible structures Then you want to try and generate their observables because in metabolomics. We actually don't see the structures We see their spectra. We see their mass spectra their NMR spectra. We see their retention times So what we want to do is be able to predict those observables the spectra ms ms NMR gcms the collisional cross-section and We have to do that for literally hundreds of thousands not millions of compounds But the result is that we now hopefully would have not only the structures predicted or known as well as the predicted Observables or known and from there we can use the the rules and methods that we talked about before about how you do database matching To figure out what your unknowns are So it's a it's a technique in silicon metabolomics it uses prediction of biologically feasible metabolites It uses prediction of observables from retention indices retention times NMR collisional cross-section Molecular weights and ms spectra. The tool I mentioned is called biotransformer. It was developed by a student of mine Yannick Who's now working in the US? with And It's outlined in terms of what it does that uses a range of techniques or tools To take a structure run it through a bunch of reasoning engines and To determine what would happen and if it was in Phase one metabolism that's cytotrome p450 space to metabolism, which is what happens also in the liver Glucuronidation What happens when compounds are in the gut what happens when compounds are out in the environment? Or what happens when compounds are just floating around and exposed to a variety of enzymes, which are not always perfect in their specificity So It's this database which uses known handmade rules as well as machine learned rules has been created It's been tested that seems to reform quite a bit better than commercial tools. It has very good precision and recall So it came out last year and it's being used by quite a few people around the world There's a website It's not as good as we'd like it to be but it's pretty quick It allows you to take a couple of structures at a time and to predict the products and reaction mechanisms And it's now being run to generate millions of compounds and we hope to have it finished in a couple months We've been testing it as well as someone was taking Green tea metabolites giving them to rats collecting their urine and when you do that you often see many many unknowns and so using Biotransformer they took these green tea polyphenols and had biotransformer predict About 30 or 40 different possible structures and they were able to identify 22 of them And they were able to suggest at least 12 novel structures that they're trying to confirm right now So it allowed them to identify many more compounds and they had even hoped to see And it in top of that it was a little able to essentially generate hypotheses about a lot of the unknowns or unknown unknowns That they couldn't figure out so The utility of in silica metabolomics depends on having not only biologically transformed or biologically feasible structures You also have to be able to accurately predict those properties mass spectral properties and Other chemical properties so we know that if you've got a structure you can calculate both molecular weight molecular formula We can calculate approximately retention time We can calculate GC retention indices quite accurately Axia aphia has come up with a very powerful method for doing that As we've learned about CFM ID we can calculate MS spectra fairly accurately and a Mars spectra are also quite accurately calculated infrared and Collisional cross sections can also be calculated very accurately So we talked about CFM ID and how it's steadily improving in terms of its performance Both using machine learning as well as hand-made rules, but there's also great interest in measuring Ion mobility spectroscopy and calculating collisional cross sections And there's a number of instruments now are being sold all the major manufacturers have them Which allow you to predict or actually you can measure collisional cross section But now there are ways to predict them and what's quite surprising is how accurate the collision cross section can be predicted and how it's dependent on different Adducts and the types of addicts that are produced will lead to different collisional cross sections and using things like machine learning or a mix of quantum mechanics and machine learning The error that people are able to get is down to about 3% or less And this is in some cases sufficient to distinguish between isomers. It's sufficient to distinguish between Compounds that we normally can't distinguish And so collision cross section is a very promising route to Identify these unknown unknowns and to do sort of standards free metabolomics Because if you can predict the mass or you can produce the max best spectrum where the collision cross section it narrows things down quite a bit The other thing is that NMR is still one of the most valuable and useful tools for identifying and Characterizing novel or unknown compounds almost anyone who does natural product work or anyone who's ever done novel compound Characterization has always had to return to NMR There are a number of commercial programs that allow you to predict NMR spectra the pretty quick problem is that None of them are free and this has always been a problem in chem informatics. So We've been spending a fair bit of time trying to develop a freeware program that will allow you to accurately predict The spectra and the chemical shifts for any compound in any solvent proton carbon 1d 2d and also predict not only the chemical shifts, but all the couplings and the prokaira ones We've tested this against some of the best commercial ones and the program itself does about 20% better than the best commercial ones And it does almost as well as a very very accurate quantum mechanical ones which take hours and hours, but this one is very fast just a few seconds so there's a clear trend and I think exciting possibilities where advances in machine learning and deep learning improvements in machine precision And how we can measure things are allowing us to accurately predict a whole range of spectral parameters Which opens the door to this idea of in silico metabolomics And it's not just a pipe drink. There's a couple of papers that appeared a few years ago one by Lloyd Sumner on the right Are they predicted a whole bunch of? metabolites biologically feasible ones or Another paper published by group in Switzerland doing natural product chemistry where they used CFM ID to predict a whole range of spectra and use those to identify a large number of previously unknown compounds So it works It's worked in our hands. It's worked in other hands. So this concept of in silico or reference free metabolomics is is feasible Certainly if we can make mass spectral prediction and close the cross-section in a more spectra even a little more accurate. This would be be a huge win and Whether it's predicting the metabolites more accurately would also be a huge win and I think there's still Efforts abroad and also in our labs and other labs to prove that in silico reference from metabolomics can work consistently If it does then the limitations that have been holding us back in terms of metabolite comprehensiveness I think will be cleared somewhat quickly Quantification is something that I've been harping out for a while and it is a trend as well I know many of you are doing or working with Untargeted metabolomics and that's certainly been a theme for today But it is an issue that's been brought up over and over again. So even now 90% of papers in metabolomics are Untargeted semi quantitative less than 10% actually use absolute quantification But if that trend continues metabolomics going to be in a lot of trouble And so will many of you in terms of your career path Because in order to translate your findings into something useful you have to have Quantitation and this is the thing that killed proteomics Proteomics never got to be quantitative and as a result most proteomics labs have now merged or evolved to become metabolomics labs Because no one was able to convert proteomics applications So there are a number of not only automated but also quantitative Platforms that are available. I've mentioned the broker one. I've mentioned biocrities. I've mentioned kinomics These are commercial organizations that sell or produce or support fully quantitative metabolomics Now Historically targeted or quantitative metabolomics was was not as exciting or as impressive as untargeted So in the even just until a couple years ago people were generally happy to get 50 Top to maybe 150 metabolites identified through targeted methods Whereas with untargeted methods with a fair bit of effort people could get up to like 300 or 400 But over the last two years Companies like libraries have released other kits. So they now have the P 500 kit which measures up to 630 metabolites That is consistently more than what most people can measure using untargeted metabolomics Metabolon has also evolved its platform and they can routinely measure more than 600 metabolites identify them they can at least Annotate not positively identify about 1100 metabolites and their quantitation is pretty good So Quantifiable methods with metabolon commercial methods like biocrities Those are actually in most cases beating the best that untargeted metabolomics groups can do Likewise, if you go to lipid approaches sci-ex lipidizer, and then there's other Hand-made approaches developed in Australia where people are routinely identifying up to 2,000 lipid species and semi quantifying them quite accurately We've talked about some of the quantitative tools that are available for automatic NMR and for GCMS. These don't give you the high numbers that LCMS do, but they certainly make it fast and quite reliable So in terms of quantitative targeted metabolomics on average, it's 10 to 100 times faster than untargeted metabolomics So you guys should consider that when you're thinking about your experimental design Typically now with the latest techniques coverage and quantitative metabolomics is either reaching or exceeding Routinely what's possible with untargeted metabolomics? So that's an important transition That I think you need to consider There are also some new methods that are being developed that allow you to do quantitation without isotopic standards Looking at ionization efficiency using machine learning techniques And these are pioneered by a group in Estonia, and they've been shown to work in a wide range of applications The other thing to remember is that if you're trying to merge metabolomics data from different platforms say NMR, GCMS IR, UV Or if you're trying to get it from different labs, the only way you can merge your data is if you have quantitative data It will never happen if you use untargeted metabolomics data. It's just impossible So these are things to remember when you're thinking about What sort of experiments you should be performing? And this is being realized by many more senior labs and core labs around the world that are doing metabolomics So right now routine quantification by targeted metabolomics of a hundred hundred fifty metabolites is is is there Up to five and six hundred through commercial labs or through work that many labs are working on internally is now possible through Commercial groups like metabolon. It's possible to get up to a thousand for specialized lipidomics. It's well very easy to get over a thousand metabolites If we were to use ionization efficiency and machine learning and other predictive techniques that we've talked about Could we get up to a thousand five thousand metabolites? I think we can And and this as I say would largely be a form of targeted quantitative metabolomics So those are some key bottlenecks Making metabolomics cheaper faster better more comprehensive But how do you make metabolomics relevant? And I think there's a couple areas where metabolomics needs to go and if you can make it More quantifiable if you can make it more automatic if you can make it cheaper and better Then it can move from the lab into the clinic You guys learned a fair bit today about biomarkers and rock cures and the statistics and methods that Metabolanalyst uses Over the last 50 years. There've been more than 750,000 biomarker papers published in PubMed That's a pretty substantial portion of all of the publications But of those nearly a million biomarker papers less than 250 have actually been approved for clinical use So that's a terrible batting average Right now even though proteomics is celebrating its 35th anniversary There isn't a single biomarker for proteomics that's been approved And when you think about gene chips and transcriptomics, there's only been five biomarker tests that have been approved And the reason why Transcriptomics and proteomics are doing so bad is because they don't do quantization The other thing that many of you probably don't know is that almost all of you have had a metabolomics test Um, probably only a few of you have had a genetics test But all of you, uh, if you're under the age of 25 or 30 have had a newborn screening test When you were born within the first few hours, uh, they took a blood spot They did a heel prick and they collected blood and they sent it into a mass spectrometer And they looked to see if your blood had any unusual metabolites So there are now literally millions of people around the world who have had metabolomics tests It's just you don't remember it because you were a baby Your parents don't know it because it's done without their consent Um, and no one calls a newborn screening metabolomics But it is So if you think about what the status is in terms of metabolite or chemical testing, um, it's pretty widespread the number of approved clinical chemistry tests or metabolomics tests Uh in in this case western canada is over 300 Uh, the number of approved genetics tests, uh, in western canada is about 130 the number of approved clinical protein test is a little over 100 And then I mentioned the statistics about transcriptomics and proteomics tests So in fact metabolomics already is in the clinic and it's doing better than other omics, uh, techniques Why is it so popular and even though we don't even know that it's popular It's because uh metabolites are great at measuring phenotypes Um, these are some studies that we've been working on the last few years We've looked at how you can use metabolites to predict diseases So this is taking someone who's apparently healthy taking their blood or urine and saying well in a few months to a few years You're going to develop this disease So it turns out it's very accurate for predicting preeclampsia. This is high blood pressure that pregnant women develop So at the first trimester when they're very healthy seemingly normal if you take a blood sample from the mother You can predict which mothers are going to develop preeclampsia or not This opens the possibility of having prophylactic treatment You can also detect in pregnant mothers whether the infant or fetus has a congenital heart defect Again in the first trimester trisomy 18 or trisomy 21 again in the first trimester So not only can you use metabolomics to predict what's going to happen with the fetus in a mother You can also use it to predict what's going to happen to someone who's been newly diagnosed with cancer Now most people don't necessarily buy die of the cancer itself They die from the effects of cancer and namely that's catechia or muscle wasting And if you know of someone who's had cancer or died from cancer, this is this is the prime Killer it's it's the wasting that develops and so if you can predict which people are going to be susceptible to catechia Then you can start making interventions Particularly in nutrition and it turns out just by measuring a person's urine You can predict who is going to be susceptible to catechia or not Now predicting diseases is one thing Diagnosing diseases is another and so we've been using metabolomics looking at blood and urine to diagnose Rejection so you can instead of taking a kidney biopsy which is used with a giant needle where they stick it on your back and pull out tissue from your kidney which hurts a lot you can just take a urine sample and Probably more accurately predict if your kidney is being rejected or not You can distinguish between certain types of heart failure some which are very lethal and others which are not Just using a quick blood test. Normally that process takes two weeks With using a whole array of methods of testing cardiac function Chronic fatigue syndrome can also be distinguished fairly easily with serum metabolomics EoE we talked about some examples of that in your analysis Packages yesterday with gc autofit and the NMR You can also use it to identify polyps and so here you can look in urine to figure out what's going on in the colon And these metabolites reflect the microbial perturbations that happen in the gut when polyps start developing So in fact, um, a number of companies have started taking Metabolomic markers and translating them into tests Tablon has been working on this Stemina has been working on an autism test and a company in Edmonton has converted those polyp tests that I talked about Into a test that's now used widely in the u.s so Metabolomics is being translated to clinic markers are being discovered and many of these markers have impressive performance sometimes significantly better than gene or protein tests So I think if we can get the message across and if you was Aspiring metabolomic scientists can can make can continue to make interesting discoveries I think there's a good opportunity for labs to adopt quantitative metabolomics for clinical translation I think Better algorithms are being developed using machine learning techniques and some of the methods that Jeff mentioned today that allow you to pick out and pull out more powerful Markers Obviously to make something work in the clinic you want a small number of markers not tens or a dozen You want two or three because that makes the test easy and cheap to perform And as I said a number of them are starting to make their way into laboratory debaunt tests FDA approved tests or european medical agency approved tests And so I think this is an exciting time and and it's really has to be driven by Scientists wanting to see their work translated The last thing before you guys all fall asleep Is to talk about moving metabolomics into drug discovery game So metabolomics began as a drug discovery tool It started in the late 1990s With groups pushing a number of large pharma companies to use metabolomics to assess How Drugs are affecting animals in animal studies Now in drug development it takes about 10 to 15 years to develop a drug So everyone's hoping for a covet 19 drug Unless someone finds a drug that's already being used They are not going to develop a new drug for covet for another 10 years And it's going to cost at least a billion dollars for the company that does it Now it turns out that metabolomics is really useful in drug development It can be used to discover drugs. It can be used to assess the safety of drugs It can be used to assess the response of drugs Or the response of individuals to drugs. It can be used to assess drug testing In phase two and phase three and it can also be used to follow up Patients after the drug has been approved And it turns out that the fat the metabolomics can be used the entire length of the drug development pipeline where Other omics techniques are usually just focused on the discovery side has made metabolomics Useful once again for drug developers And this is just highlighting that the different phases where Metabolomics is is being used It's being used to discover drugs. It's being to help with toxicity screening It's to develop preclinical efficacy biomarkers. It's done to do toxicity assessments in patients It's done to do clinical safety biomarkers and clinical efficacy biomarkers all the way through So no other omics technology in fact no other technology can find utility at every every step of the pipeline Now traditionally the way we do drug discovery is we have used genetics We've used things like GWAS techniques to identify Marker genes many GWAS studies cost millions of dollars and take several years to perform of those Potential genes that seem promising on average about half of them Can be targetable And then of those genes that people find are useful targets only about half of them Can be developed into useful assays involving proteins And then when people start doing high throughput screening to see if they can find a drug Typically only about 20 of those screening assays lead to a lead compound So roughly you've got this 20 success 50 50 percent 20 success just to get to your lead compound And then things get really tough Once you've got your lead compound you have about a one in 500 chance of getting the lead compound through all the different phases of trials And even if you can get the lead compound to complete phase three Even when it's marketed most drugs only succeed about 50 percent of the time Some of them have side effects. They didn't anticipate some of the efficacy isn't as great as they thought And usually someone comes up with a better drug which makes your drug kind of useless So when you look at the total time 20 years the total cost more than a billion dollars and the total success rate Point zero zero one percent if you multiply all those ratios together It's no wonder that most drug companies have stopped doing drug development On the other hand if you use metabolite based drug discovery you have a much better chance So rather than doing GWAS study if you do a metabolomic study to see which metabolites have changed gone up or down Then in fact right away as you guys are learning today. You can zero in on some pathways And you can do that pretty quickly with metabolites. It's not even days. It's more like hours So once you zero it in on the metabolic pathways and therefore some of the critical proteins and genes Then you can start going to databases like drug bank, which lists all of the Known drugs that target certain proteins and many other kinds of databases like Brenda Which allows you to start choosing some potential inhibitors of enzymes or metabolic and metabolic proteins In many cases it turns out those effective inhibitors Probably already exist or have already been discovered and some of them are actually Food-based some of them are nutrient based some of them are already existing as approved drugs And so again using those techniques because we know so much about metabolism And we know so much about how small molecules target various enzymes transporters and proteins It's possible sometimes to get a lead compound quite quickly And what's more is once you've got your lead compound if it's already an approved drug or an already an approved metabolite You can go straight to trials. You don't have to go through the FDA Because it's technically an approved compound And what's more you can use metabolomics to monitor the drug or and its performance Throughout the trials and the tests Now this might seem pretty fancy Fancyful in the sense of yes, could you do this for less than a million dollars? And can you do this in a matter of days or weeks or maybe months? Um Well, in fact, it's actually happened and this was one example which is a discovery made in 2010 roughly about two hydroxyglutarate So it was a metabolomic study They were looking at people who had or developed brain cancers and they found that there is a specific metabolite Two hydroxyglutarate that was elevated in people who had brain tumors And it was also associated with certain people with an inborn metabolism who also developed high levels of cancer But two hg is very abundant in many brain tumors And it turns out that they were able to very quickly figure out what was the enzyme that was causing that and it was a mutant Isosichrite dehydrogenase that leads to the production of this onco metabolite So within a few years literally after they figured this out They went through They did the screens. They found a drug in 2014 Thanks to the metabolomics efforts, and then it was approved in early 2017 They were able to use analogs that had been discovered in the 1970s For studying tc metabolism to model this particular drug and develop it So they're able to use that that large body of history or historical work and known enzyme inhibitors To develop a drug that is already in the market and in fact several other idh inhibitors have already appeared So this is an example of how you can have very accelerated drug discovery where you use metabolomics to Identify the target and use the huge body of metabolism And enzyme inhibition studies that have been published over the last few decades to develop leak compounds very quickly So in terms of the future metabolomics, I think There's several areas that that I think are exciting Areas where a lot of people are working towards where I think is a group and there's new newly trained metabolomics people that you should try and focus on and as I say these are in the areas of automated metabolomics expanding metabolome coverage improving ability to quantify in metabolomics Translating metabolomics not just to the clinic but to other areas from environmental to monitoring to animal Water quality assessments and then trying to get metabolomics Engaged in drug discovery because I think they're clear examples where it's leading to completely novel Targets completely novel drugs and allowing drug development to become much much more rapid than before


The following is a presentation of TFNN. The Morning Market Kickoff with your host, Tommy O'Brien. Good Thursday morning everybody, I'm Tommy O'Brien, company alive from TFNN. Quite a market day as we anticipate the opening bell in about 24 minutes. Blowout earnings from the video. We got everything trading higher this morning except for a few equities with their numbers last night. Rivian, one of those. Great car company. Boy, the stock a different story. Nonetheless, we kick it off with the S&Ps. We're up by 1.35% folks. All of that right now on the heels of those NVIDIA numbers. We'll jump into them in a moment. We're going to jump around to the indices first. S&Ps up by 67 points, 1.35% degree. NASDAQ 100 is over 2% right now folks. We're going to kick off the trading session up by 353 points. That's a 2% acceleration. Keep your eye on the fact though that we're not even where we were less than a week ago, which was 18,000 in the NASDAQ 100. It was quite a little pullback into those earnings, but boy, the bar was pretty high. NVIDIA came out with the numbers after the bell right away. They beat on the forecast. You pulled back a bit. I was in the den. Everybody was anticipating those numbers. They come out right at 4.20 PM Eastern time last night. I said, is this, you know, the bar was just too high? Maybe the bar was just too high. They beat on earnings, they beat on the forecast, and then the market digested those numbers and said, no, we're going higher. They came out with some, some, some more quotes in particular from the CEO, pretty strong in terms of indicating where this market is going this year and next as he expects demand to continue surging. The Dow inching towards 39,000, 38,908, the Dow up by 610% and the Russell is flat. Check that one out, right? If you're a believer in AI powering this market forward, the Russell's not where you want to be. NASDAQ 100 up 2% Russell, barely flat this morning. And you were in the positive, but check it out. The Russell since about 4 AM Eastern time given up almost a full percent down about 20 points from that high we made earlier this morning. Bitcoin trading up $160, $51,115, little volatility there. You jump over to crude, crude trading up, excuse me, down 54 pennies. We were just at that $78 range and that's kind of the area, right? We go to a daily real quick on crude. You can see even from where we backed off, just kind of the upper boundary range from 78 to 79, we're rolling over a little bit. Crude just continues to chop around in the high 70s. You jump over to gold, gold digesting the numbers on last night, up to 2045, we're down to 2034, gold technically flat on the session. And you jump to notes and bonds. Now we got a little bit of jobs data this morning as well that we will get into, but that is secondary in terms of what's going on in this market and what's going on in growth stocks. The 10-year right now, negative by five ticks. We're talking about a 10-year yield of 4.33%, 4.33% the yield on the 10-year. As we jump around and let's go over to the VIX as this market goes straight up. We got the VIX sucking out all that volatility in the video earnings, man. We're at a 1424, we were at a 16 VIX yesterday. You jump over to the dollar index as we finish the wrap up, dollar trading higher as we got a little bit of higher yield coming at you. We're above 4.3 in the 10-year, the dollar going from 4.3, excuse me, 103.43, we're back above 104 right where you were as of the close of the session yesterday in the dollar. All right, it's 9.10 in the morning. We got 20 minutes to go to the second until the opening bell. And it's going to be a wild one, man. Where is supply going to equal demand on this open? You got Nvidia shares. Now, it's pretty simple math. They got about 2.5 billion shares outstanding. I think it's 2.47, I was just looking at where you came in. Yeah, 2.47 billion. Nice round number. It's not often that round with buybacks and stuff like that. Usually those numbers, 2.47. So we call it 2.5 billion shares. You're going to open up $75 about. Yeah, right now you're trading at $750. You're going to open up $75. So what's that going to add? 2.5 billion. Man, that's going to be quite a number, man. 75, yeah, 75, 150. Almost $200 billion in market cap you're going to add overnight. And that is probably going to eclipse the record that Metta just set in terms of the run. Now, what should happen is that we should continue to set these records because inflation, just the raw power of the numbers that you're dealing with, you're dealing with bigger numbers. You should constantly be sending records in terms of the move that you've had in one day as these companies get bigger and bigger and bigger and the moves that they have on a daily basis are going to be larger and larger. But it is kind of ridiculous that NVIDIA is going to take that throne right out of Metta's hands and they just set that number. I mean, jump over to Metta now. So NVIDIA, up by $75. Now, what's interesting is you jump over the Analyze tab. All right, we've been talking about it. And they had about a $60 move priced in. OK, so $60 move implied. You're up right now about $75, so just over the implied move. I mean, yes, you've got to move to the upside. So you had to be directionally correct. Now, we've been talking about on this chart, $800 is probably where this thing's going to climb to, man, especially after those numbers. You're talking about an A to B, C to D. And remember how this thing plowed ahead of that B point, right? I mean, I didn't think it was going to go from $500 to $800 when you crossed the B point on January 8th and it's February 22nd right now. But nonetheless, that's where we find ourselves. And you look at where we were pre-market. You were all the way up to $775. $800 is that price point. So NVIDIA, they're going to get quite a boost on the open. It doesn't matter where you look right now, especially into the growth stocks. Amazon shares, you're up what? Almost $4. What is that, 2.5% to the upside for Amazon shares? You jump over to Google. Google shares up about $3. That's going to be a 2% list. You jump over to Microsoft. They're big into AI, $7 to the positive, almost a 2% list on Microsoft. Excuse me, Apple shares. Not quite to the same degree, right? Apple, more going on than just AI. Up less than a dollar. It's remarkable. Apple continues to underperform. Keep your eye on that one, man, because Apple continuing to underperform. You got Buffett dumping some of his shares in the last quarter. I imagine he may see something. For some context here, folks, Apple is where you were trading at at the end of 2021. End of 2021, okay? You pull up Microsoft. Microsoft is a solid, what? 15% above that price level right now. Easy. Let's see where Google is. They're a different animal. Yeah, Google right back to where you were in 2021 as well. For some context though, NVIDIA at the end of 2021 was trading at 335. We'll cherry pick the high. So you're going to be more than 100% up off that number. Pretty remarkable. All right, let's take a look at these numbers, man. NVIDIA set for the biggest gain in nine months. Biggest gain ever is what they should say, man, in terms of what they're going to gain. The number's there. The biggest one probably is that you're talking about forecast for the current period, okay? Current period, revenue, $24 billion. Market was looking for under 22. Huge beat, especially in the context of expectations already sky high. Yeah, they're going to add $200 to $250 billion in terms of the market cap. Going to be the biggest pop ever. And yeah, some of his quotes were pretty strong. This is talking about the CEO. Generative EI has kicked off a whole new investment cycle. Where are they investing in? They're investing in his chips. It's going to double the world's data center installed base over the next five years. Sam Altman's trying to increase that rate, right? It's raising $7 trillion from the Saudis, et cetera. And it represents an annual market opportunity in the hundreds of billions of dollars. Remarkable, man. Now you're going to have some competitors, okay? How about the data center? Yeah, spending on AI related infrastructure, data center revenue, fiscal year 47.5. I mean, the growth on this job, man. We got a lot to talk about, folks. We're coming back. We'll talk to our man, Kevin Hinks, from Fast Market, from the Schwad Network. Don't go away, folks. It's going to be an interesting program. We're just getting started. We'll be right back. If you're looking for potential trading setups in the stock market, then Rocket Equities and Options Report is a newsletter you should try. 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Let's continue taking a look at some of NVIDIA, man, because it's gonna all be about NVIDIA on the open. Pretty remarkable. NASDAQ 100 popping 2%, you know, quite a remarkable pullback in the last couple of days. You get it all back on those earnings, but nonetheless. Now, let's jump around here. So, this article here is talking about that NVIDIA profits are rising more than its stock, and it is interesting to see. And this is how some of those PE ratios, if they ever make sense, folks, when they get to the fantastical level that some of the PE ratios on NVIDIA have gotten to right now and in the past, is that you gotta crush the earnings going forward, right? I mean, that's why you have PE estimates, even based on future earnings that are just through the moon sometimes, is because these companies are gonna grow their earnings at a rate that is gonna dwarf what you're looking at in that regard. Now, here's what we talk about, okay? How about a 486% year-over-year growth and earnings per share? How about that, man, since the middle of 2023? Folks, it's only February of 2024, okay? Massive earnings growth, almost a 500%, okay? They just came in at $5.16 per share. The market was looking for 460. They're forecasting 24 billion in the next quarter, as we talked about. The market was looking for 21.9, somewhere in that regard. Yes, and this is the chart I wanted to look at here, okay? This goes back to the middle, almost the beginning of 2023, it looks like, okay? In red, you have NVIDIA's stock price, which with the axes on the left, okay? And in the black, you have the PE ratio, okay? And look at what's happened. You've had the PE ratio, which is in black, was at one point peaking at 65 to 70, okay? And all it's done is come down as the stock market has accelerated higher, and you're talking about a price-to-earnings ratio that is well below some of the loftier levels that it was at, especially towards the beginning of 2023. Not a lot of people would be aware of that chart when you take a look at it, man, versus the rod up in the equity that it's had as the price has continued to accelerate, the price per those earnings has actually decreased as the earnings have just grown 500% almost on that number. Let's see, we have that one. Yeah, there's the number there. Now you're looking at, NVIDIA's gonna be right at the number, right? They've pulled back a bit. So to take the title away from Metta that they gained just 20 days ago, I mentioned, what do they got? They got 2.5 billion shares outstanding. They need about a $200 billion. Stocks gotta be up about $80 to get the record, and you're up like $75 right now. So you're gonna be right at the top of this chart, man. Pretty remarkable the run that it's had. Now Metta is not quite the size of the company that NVIDIA is, but yeah, you're gonna have NVIDIA shares soaring past the likes of Amazon and Google today. Now the one thing that I find myself trying to anticipate at least is the competition that you are gonna have coming down the board. And they referenced it slightly in one of these articles. The stock traded about 30 times forward earnings as of Wednesday's close. You got AMD at 43 times, just finishing up where we are. The whole NASDAQ 100 trades at 25 times multiples, and you got NVIDIA at only 30 times multiples. That's one way to put things, okay? Is this the one they talk about? I wanna get the one they talk about for competition. Yeah. All right, I'll have to find it when we come back after the break. We got three different articles we're talking about here. Yeah, that's the blowout. Maybe this was it. They're gonna have competition coming from them. Yeah, here it was, they make a note to it. NVIDIA faces risks, including mounting competition. Folks, they're taking in $24 billion in revenue over a 90 day period, and almost 50% of that is going to their earnings. If you don't think people are coming for that market, they are coming for that market. They got quite a head start, though. They're gonna be the leader. And as they said, so you got AMD recently began selling a line of accelerators called the MI 300. They're looking for 3.5 billion from the product this year, up from an earlier projection of 2 billion. We just talked about, though, they're trading at a PE of 43 versus NVIDIA at 30. And then, of course, NVIDIA, if they're in the lead, they're gonna keep going, though. Analysts expect the company to soon unveil more powerful accelerators. They have to navigate China. That's gonna be a problem for them in terms of China. Okay, in terms of data center revenue, China accounted for a mid single digit percentage. In the fourth quarter, we expected to stay in a similar range in the first quarter. Yeah, and they got new chips that are compliant to Chinese customers. Keep in mind, though, they're selling second grade chips to China. And China is definitely gonna be working immediately on making sure that they get the top chips and not just the second grade chips that fall under the restraints of the US government. There we go. That's the last part I wanna get to here, okay? Companies such as Amazon, Meta, Microsoft, and Alphabet's Google are NVIDIA's largest customers accounting for nearly 40% of its revenue, 40%. That's not going away, folks. That's the most remarkable part. Now, you're gonna have these companies looking at the possibility of spending less and getting into that business themselves. But if it were really easy, everybody who's doing it would be doing it. And that's not what's happening right now as these stocks and NVIDIA, everybody wants NVIDIA chips, they're top of the line and their next chips are probably gonna be top of the line. And this is a once in a generation, once in a multiple generation opportunity because once you get the lead in AI, how does somebody ever take over that title if you've reached that status? And it's interesting how this almost becomes a Department of Defense type issue as well because we're at the forefront of technologies that are gonna overtake the globe so that money's just never gonna stop when you put it in that regard. Interesting when you look at it at that angle. Yeah. Now, they only forecast quarter by quarter usually. They're not talking about multiple years of revenue. I mean, there's so much volatility, right? How do you look forward a year when you can barely look forward sometimes 90 to 180 days in terms of where this industry's going or what type of orders you're dealing with. But the fact that he talked about in terms of this trend carrying through 2024, carrying through even possibly the next couple of years, some of the words that were used, it's a strong indication that this is not going anywhere. All right. What else we got going on? So we talked about as we come into this break, NVIDIA is gonna trade higher, boy, Rivian, right? Joking with some friends last night. The slide continues. Yeah, watch out for this talk. We'll talk a little bit about it when we come back after the break. You got Rivian shares down $3, which is a 20% acceleration, folks. You put this thing back on the daily. Where are we gonna open? We're opening it like the lows of last year, 12 bucks. You were just up to $28 in July, folks. My goodness, right? You might break all-time lows on this equity after they pushed it out to the public and ran it up to $179. I know, right? And yes, $179 is a cherry-picked high, but this thing went public November of 2021. Kudos to them for pushing it out right as the market was peeking out at highs. And you lasted for two solid months above 100. Two solid months above 100, this thing traded at. Well, yeah, we're gonna open at 12 bucks. Stay tuned, folks. We're coming back for the open. S&Ps up by 65, NASDAQ 100 up by 341. We'll be right back. Are you ready to take charge of your financial future? TFNN is your gateway to the world of trading and investing. Whether you're starting out or scaling up, TFNN empowers traders and investors of all skill levels with top-notch investing systems, strategies, and techniques. It's time to protect and grow your money with insight you can trust. Join us live Monday through Friday during market hours for exclusive content that moves with the markets. At TFNN, we bring the trading floor to you. Our seasoned hosts are here to answer your calls and questions live on the air. 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We get the stock market open. You get Nvidia shares up by 10.95%. We're trading at $745. That's a lift of about $70, as I mentioned. Deal with about 2.5 billion shares outstanding for Nvidia if they want to hit that, excuse me, 200, what did I say, 200? Yeah, 200 billion is what they need to market at cap to be the most ever, just an arbitrary number. Nonetheless, you'd have to be up by about $80, but you jump over to the analyze tab, man. You're talking about a company, folks, this morning that's valued at $1.85 trillion. $1.85 trillion. Absolutely remarkable. You jump over to Google, they're at 1.8 right now. Everyone's getting a lift, obviously. You jump over to Amazon shares, 1.8 as well. So, Nvidia, a larger company than Amazon and Google at this point, pretty remarkable. You got Amazon shares with everything in this market, catching a lift by 2.4% right now. We jump around to some of the big dogs, Microsoft, up by 1.8% right now. Companies like Salesforce, accelerating higher, up by 3% right now. Google shares up by 1%. They actually gave it back on the open there. You jump over to Apple, up by about 9.10% right now and I referenced Rivian and it's not stopping. Probably justifiably so. You're down 25% for this equity. You closed it out yesterday, above 15. Right now, we're trading at 11.61. And the deal with Rivian is that they're in trouble. Period. Rivian has about 1 billion shares outstanding. That's easy for simple math to give you the exact number. They're looking at about 957 million shares. Okay, the company clocks in at a market cap of $11.2 billion currently. Folks, if somebody ever comes to you with an idea of a company and they've incorporated this company and they tell you that they have master plans, they tell you that they got a deal going on and Jeff Bezos is a participant and they say, we're gonna push paper out to the public. We're gonna do it at about 100 to $120 billion market cap and we haven't even sold the product yet. People will buy it. Kudos to you, man. And that's what happened, which is absolutely remarkable. You got a billion shares for this company, okay? So it's simple math. When I said that you were trading above 100 for a solid eight weeks when this thing went public from November until where we were when the market really fell apart in January, this company had a market capitalization of $100 plus billion back then. On ballparking, I'm sure the outstanding shares maybe changed since then, went up or down. But nonetheless, it was 80 to 100 billion. If you take that cherry-picked high, my goodness, you were dealing with a market cap that was well through the, what is that? Nine figures, nine figure, right? Nonetheless, this morning we're at $11 billion, okay? And look at these numbers.  This is their EV production, okay? And a big problem here is that they are well below everything, they're laying off people. They're gonna build 57,000 vehicles this year, basically right in line with where they were last year. Folks, a pre-revenue company for $100 billion doesn't just start producing 50,000 vehicles and do it this year and do it next year. The market was looking for 80,000. On a percentage basis, you have a 0% growth versus what? 23, almost a 40% growth priced in?  Again, the size of this company right now, they need to be ramping things up. They're not the type of company that should probably be scaling things back with the market capitalization that they have. Excuse me. Now, you go take a look at what this looks like on a chart. When this company went public, how many cars did they produce? 1,000 that year. And I think they were pretty much towards the end of the year. 2022, they made 24,000, 2023. They made 57,000. They said, guess what? We're just gonna make 57,000 again this year. And the market said that is a problem. Yeah. EVs are a problem, man. You know, these are beautiful cars too, man. When I see these cars, I have a couple times just literally found myself, you know, just kind of going around, taking a look at it. Yeah, Rivian lost $40,000 on every vehicle that was delivered in the last three months. 10,000 more than it lost in the third quarter. Everything's going in the wrong direction. I actually found myself on these numbers last night saying, and I didn't plan on buying a Rivian just yet, they're expensive vehicles, man.  You can pretty much get anything you want top of the line. But they are beautiful cars, man. But I found myself saying, hold on a second.  What was it, Daywoo, right? Was that the company that just stopped existing? People had the car. And before you know it, four, five years later, the company didn't exist, you can't get parts, et cetera. You know, somebody probably buys them if they go kaput, worst case scenario. But I found myself thinking that last night, which I have not thought. There's no way that they should be not growing the amount of cars they're producing. Absolutely no way. They're losing more per vehicle than they were losing last year. Capital expenditure gonna rise $1.75 billion somewhere in here, they lost like $2.5 billion. The number, yeah, here you go. Still expecting an adjusted loss of $2.7 billion before interest, taxes, depreciation, and amortization. They're gonna lose that before you factor in all the real life stuff that matters when running a company. Yeah, be careful on Rivian shares, folks. Great car, but boy, that is quite a lesson when you think about the price destruction from that level. Absolutely remarkable. All right, let's see how Navidia's trading. Six minutes into the trading day. There you go, you catch a pop on the open. Yeah, can't fault somebody for selling ahead of that number because there's volatility, man. That's what's so great about options, folks. You know, we didn't talk to our man Kevin Hicks this morning, he's busy, he couldn't make it, but we will talk to him on Tuesday, but check out Fast Market because even if you don't trade options, folks, we air it live every day, 12 noon Eastern time, right here from the Schwab network, okay? Even if you don't trade options, understanding the applied moves in equities is just so advantageous when you're trading equities and options that will give you so much of that information even if you're not trading the options that underlie them themselves.  You jump over to Microsoft shares up by 1.7%, 409. Amazon up by 2.5. Google one of the other big, oh, look at Google, man. Pay attention to how these stocks react on days like today, folks, right? Google's only up three tenths percent. Everything else is playing hard. So pay attention. If you're really looking for AI to carry things, pay attention to those types of moves on days like today where Google can't find a bid and everything else is dramatically higher. They obviously all have different facets of the business, but that's indicative of buying and selling right there. All right, what else do we got going on? Oh, AT&T's got some issues this morning, man. AT&T grapples with wireless service disruption across the U.S. I was watching Bloomberg this morning prior to the program and Jonathan Farrow was laughing at them justifiably, show man, yeah, here's the quote. Some of the customers are experiencing wireless service interruptions this morning. This is the quote from AT&T. We are working urgently to restore service to them. We encourage the use of Wi-Fi calling until service is restored. That's your game plan. Gonna be interesting to see how a network like that experiences nationwide problems. Started at about 3.30 in the morning. Seems like it's still carrying through and you jump over to AT&T and they are down by 2.5% on that news to 1657. Yeah, interesting as we rely on those networks so much everything does, right? How does that look like go forward going forward as interruptions gonna interrupt more than just our cell phone service? Stay tuned, folks. We still got a lot to talk about, we'll be right back. Are you ready to take charge of your financial future? TFNN is your gateway to the world of trading and investing. Whether you're starting out or scaling up, TFNN empowers traders and investors of all skill levels with top-notch investing systems, strategies, and techniques. It's time to protect and grow your money with insight you can trust. Join us live Monday through Friday during market hours for exclusive content that moves with the markets. At TFNN, we bring the trading floor to you. Our seasoned hosts are here to answer your calls and questions live on the air. 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A fund's prospectus and summary prospectus should be read carefully before investing. An investment in the funds is subject to risk, including the possible loss of principal. The funds are designed to be utilized only by sophisticated investors such as traders and active investors. Distributor, foresight fund services, LLC. This program is brought to you by Vista Gold, traded on the NYSE American and TSX under the symbol VGZ. Folks with the S&Ps holding onto most of those gains were up by 61 points right now in NASDAQ 100, up almost 2% at 363 points in the positive. You get the Dow right now at about 38,900 and you jump over to the Nikkei. So you got the Nikkei.  Pretty remarkable, man. There's your high in December of 1989. And here is your new high in February of 2024. Always make sure you're diversified, folks. Okay, as much as you love this tech rally, okay, if you're depending on that wealth or anything, especially near retirement, always make sure you're diversified. I hope we all learned that during the pandemic. We had tremendous winners and losers. Always make sure you're diversified. Don't think that something like this could never happen in America. I don't think it will, okay? But, and you're entitled to think that, but make sure you protect yourself and you make sure you protect yourself to some of the tail risks that are out there. If you can, okay, if you have the ability to protect yourself, depending on how much you have at risk, you just see links like this, okay? Nobody thought, now this was bubble-ish, back in 1990, okay? But if you told somebody, man, if you buy here, you're not gonna see that money again for 35 years, right? Now nobody would have thought that's happening.  But nonetheless, you are higher. And yeah, you got a lot of companies making a lot of money over there. It's not the bubble that it once was. And yeah, and that produces the Nikkei's overall market capitalization of $4.6 trillion, the highest number out there, yeah. Now, you jump over to Yen, okay? There's the Yen, we're pushing 150, 61, just kind of chopping around where we've been recently. It's gonna be interesting to where we go forward with the Yen, the dollar as well. You don't have to be a technical trader to realize that we are pushing boundaries that resistance is intact at about the 151 range. You're back up to where you were in November. You jump over to the dollar index, which is gonna be slightly an inverse of that, obviously factoring the euro and other currencies as well. But you get the dollar right now rising.  Pretty remarkable move we just got, man, to 104.34. We're back at 104.10 as the dollar surges higher. You jump over to yields, not quite the same move in terms of producing that type of a reaction. We do have a little bit of higher yield this morning, but not quite the move you might expect when you see that type of move in the dollar index. We have 4.33 is what we're looking at right now. Now, some of the numbers we got out this morning on top in the video, okay? You got jobless claims out here, barely 200,000, 201,000. Initial claims decreasing by 12,000 to 201,000. Continuing claims dropping to 1.86 million. Continuing claims, always one week more delayed than initial claims. Initial claims, we're looking at the numbers for last Friday, okay? The week ended February 17th, Saturday, technically. And for continuing claims, they go back one more week for 1.86 million. Nonetheless, what makes you think that we need a cut, right? Everything is running a little hot. Now, some of the takes in here, other things are a little bit deceiving if you're looking at some of that data, okay? You know, this is one take, okay? Drilling down to the state level data, there's a breadth of labor market weakness has increased, implying the conditions might not be just quite say, are less sanguine. Then they appear on the surface potentially. You got Morgan Stanley, you got Rivian talking about layoffs right there. Rising number of layoffs at big corporations, may signal jobless claims could rise in coming months. Although so far, the figures have remained low. You can't deny the data, man. You know, if we were not so low for so long, okay? Then what would make you think that we somehow, look at this pop we're getting right now, man. Look at this pop we just got. Then we just get something because the 10 here just jumped, what? Six, seven basis points as I was talking about it. That's the 945 AM bar. Yeah, not sure if we just got something out there, man, but we got the yields dropping a bit and we got movement everywhere. As we get the NASDAQ 100 continuing higher up by almost 400 right now. You jump over into video shares keeping our eye on them. Look at that, man. So you are, you're adding, remember 2.5 billion shares, man. The thing's up almost 100 bucks. You're adding $250 billion market cap overnight to that equity. Now, you're just back to where we were trading. What was our high recently? Was that it? Are we back to the highs? We're well over the highs, man. What was the high? 746, that was the high. We're trading 771 so far this morning up by $97, 14.4% to the upside for Nvidia shares. Jump over to AMD. Of course, they're catching a lift. Demand surging 7.6% to the upside for AMD shares. This market's not stopping, man. What makes you think we need a cut? Right? Look at this market, man. Now, this is, so 5122. We've been talking about these S&P. Excuse me, the A to B, C to D projections. 5122 is where you're at in the S&Ps. We'll see if we bump into any resistance. That's about 60 points above what we're trading at right now. I'm not sure what these channel lines are mean in here. We were in a channel at one point. This is just trying to figure things out. We break out a maybe a more recent channel from January. But look what we're doing. Let's extend that to the right. Let's put this on a little bit of a shorter term timeframe. Let's zoom in since we've been on that portion and you can see this trend that we had here at one point we're breaking back into that channel line right now. Remarkable strength in the markets even as we face a 10 year at about 4.3%. And the case is very difficult to make that we need to cut rates as this market plows forward, right? I mean, think about it. The reason why you cut rates is you make capital cheaper. It's that simple. Now consumers, the housing market in particular, but think about the amount of capital that's being spent by these companies as we look at it across the board right now where equities are across the board. I mean, you get the Dow inching 38,900 right now. We are a stone's throw away from 40,000 in the Dow. We're about to break all-time highs in the Dow. NASDAQ 100, or what? A stone's throw away, we'll call it from all-time highs and the S&P is crushing all-time highs today as we speak Russell, you do catch a bid, but yeah, the Russell continuing to underperform. All right, what else we got out here to talk about this morning? We talked about Rivian. We talked about an NVIDIA, of course. Yeah, this one's an interesting one. How does this even happen, right? It's unfortunate it always takes the aftermath to figure things out.  You're supposed to rethink the safeguards before bad things happen. They're just fortunate that somehow this got found out before bad things happened in terms of those parts that were just knockoffs. They're gonna form a new coalition. They aim to keep unauthorized components out of the planes, nonetheless. Pretty remarkable it takes that for that to happen. We jump over to Boeing shares this morning. Excuse me, up by 810% with the market, 20304 for Boeing shares. But yeah, they got some issues going forward, man. To put it lightly, this market's got no issues. None as we just go one way right now. Look at this pop, we just got on the open, man. 945, we're up by 15 points in the last five minutes in the S&Ps. NASDAQ 100 is approaching 2.4% to the positive. Amazon's up by 2.8%. Google finally catches a little bit of a bid up by 1.2% right now. Microsoft's not stopping up by 2.6%. The market cap that's being added right now is hard to even comprehend. All right, folks, one more segment. We'll jump around and see what else we got going on in these markets. Don't go away, we'll be right back, folks. A gold report. As a precious metal, gold is still king. It continues to hold the most effective safe haven in hedging properties across the global major trading hubs of the London OTC market, the US futures market, and the Shanghai Gold Exchange. The gold report. Tom O'Brien publishes his weekly gold report every Monday morning for subscribers, consisting of coverage of the XAU, HUI, GDX, the Dollar, Bonds, the South African Rand, as well as 25 different mining equities with specific buy-sell recommendations. The gold report. New subscribers get a 30-day money-back guarantee so you have nothing to risk. Subscribe to Tom O'Brien's gold report newsletter now at TFNN.com. You might think that if you want to be successful at trading in the stock market, you're going to need a crystal ball. After all, it's impossible to predict the future, right? Like any endeavor in life, before you decide it's impossible, get some advice from the experts. You might find that it's not so impossible after all. For daily market overviews that give you direction on the key indices, selective stocks, and commodities, subscribe to the opening call newsletter at TFNN.com. The opening call newsletter is written by Basil Chapman, creator of the trading methodology known as the Chapman Wave. The Chapman Wave up-down sequence gives you an edge in identifying price turns, finding the peaks and valleys in stock prices.  First-time subscribers also get a 30-day money-back guarantee. If you're not satisfied, let us know and you'll get a full refund within 30 days of signing up. TFNN.com, educating investors. The reality is that navigating financial markets can be risky. Markets can be chaotic and difficult to understand. Having the latest market advice can help you turn this chaos into a key for creating winning trades. At TFNN, we understand that it can be hard to find reliable market news. That's why each of our market experts offers their very own market newsletter. They must have tool for every trader out there striving to find an edge in today's markets. TFNN newsletters cover every aspect of the markets so you can analyze the market before you trade. Try any of our great newsletters risk-free with our 30-day money-back guarantee. Just visit the Newsletters tab on the front page of TFNN.com. TFNN, educating investors. Don't forget, you can listen to TFNN live on your mobile device 24 hours per day. Go to TFNN.com. And hit Watch Tiger TV. That's TFNN.com. And hit Watch Tiger TV. Welcome back, folks. We got Google shares up by 1.2% right now. And yeah, the other story going on with Google, which is probably why it was underperforming at least to start off the session with that sell-off, is the news that they're pausing Gemini. Right, are they just pausing the image generation tool? I think it is. Yes, they're pausing the Gemini AI image generator after it created inaccurate historical pictures. This story having to do with race, that always gets things fired up on both sides, of course, but not where you wanna be. And man, we are the frontier of the Wild Wild West of AI. Yeah, and it looks like they're generating pictures of historical figures like the US founding fathers as people of color, calling them inaccurate. I mean, quite the story, man. Gonna be interesting to see how we go forward. This image, now it's interesting. I was just talking about the image generators, okay? The image generators are so cool, man. I was just using them for Tommy IV, right? Putting some of those images out there earlier in the week.  We're so at the forefront, folks. It's gonna be interesting to see how companies tackle this stuff. And you can see that Google's a little bit nervous being on the wrong side of that. They just released this at the beginning of February. They've now stopped that image generator. I mean, videos are coming right down the line. They're already almost here. They're just gonna ramp up. And keep in mind that it is very difficult to understand and grasp exponential growth. And that's what we are in right now, okay? It's very difficult to understand how fast things can move when they're moving exponentially because of how fast growth can occur when it's growing at an exponential rate. And that is what is happening right now. To the likes of Nvidia earnings growing almost 500% over the last year, right? Less than the last year. Nonetheless, Google, they're taking it on the channel a little bit. I say that they're still up by 1.2% right now but actually underperforming the NASDAQ which is up 2.1% even after they give back some of that gain this morning. And why not? We gotta finish it off with Nvidia, right? Nvidia shares up by 92 bucks, 13.6% to the upside biggest market capitalization gain ever right now in the history of the markets needs to be up about $80 by the close to take that title from Metta. Metta up by 3.5%, man. It's gonna be an interesting day folks. Stay tuned. We got a man, Basil Chapman. He's coming up next with the Tiger Technician Sour. Have a great day folks. Look forward to seeing you tomorrow at 9 a.m. as well. Stay tuned for Basil. We'll be right back folks. Have a great one.


You'd better stay over there Not too smart. Are you not too smart? Are you keep your distance misty us looking for trouble? Are you?


Describe how that picture took place. What happened is that was it when they I was on the air broadcasting the president's death when the White House travel office person grabbed me by the car my suit caller from behind and said I had to go with him And I said I'm on the air. I'm not leaving.  I'm on the air. Well, we need a pool I said we'll get pure points this term. I mean we're arguing over the biggest story in a century And I'm not wanting to go to cover. It's crazy Yeah, I couldn't believe when I look back and thought of my fighting over but I did go and we picked up Merriman Smith and Chuck Roberts of Newsweek and Went down through the hospital and they had a unmarked police car waiting for us And they said we're going out the Air Force one We took off and we were doing 60 to 70 miles an hour through Dallas over Curbs Driveways lawns no matter how this police officer could get through backyards trying to get to Air Force one before it took off back to Washington Got to the airport and Mrs. Kennedy had just arrived with the casket And they were putting the casket onto the aircraft and they had to carry it up the incline of the rear stairs This was a 707 big airplane and majestic airplane because Mrs. Kennedy and Jack Kennedy had helped design the colors the colors You see on Air Force one today and the lettering were designed by Mrs. Kennedy and Jack Kennedy It was a brand new airplane that came to his administration. So they had a piece of it in this history as well They had to knock four handles off the casket because it kept the handles were sticking out They couldn't get it into the airplane So the Secret Service got an axe somewhere and they knocked off the handles and were able to manage to get the thing in there I went around the side of the airplane with the Merriman Smith and Chuck Roberts and climbed in from another side of the aircraft coming from the front end of the plane Went back to the midships and I saw Mrs. Johnson I saw President Johnson in there with a group of people and President Johnson was talking to Marie Famer and Secretary. I Talked to Marie for a little bit and they said they were trying to assemble and get a judge from downtown To come out and do the swearing in Mrs. Kennedy was in the rear with the casket at Marie told me when the president arrived on on the aircraft Which was maybe 15 minutes no more than that than Mrs. Kennedy's arrival The first thing he did was he asked for a hot cup of vegetable soup and he said to Marie I've lived a year since this morning and Then he told her and I overheard him tell her that I would like you to go Ask Mrs. Kennedy if she will stand with us at the swearing in and Marie went back and talked to Mrs. Kennedy or sent a message back and the message came back. Yes I will I'll come but I want a few minutes to come compose myself and so we waited by this time the room was Stifling hot probably 120 degrees have been sitting in under a hot sun and only one engine had been running to keep the electronics going and It was probably a few minutes before Mrs. Kennedy appeared in in the doorway And that's when the sobbing this the quiet sobbing by the young Kennedy staff You have to understand these were young people in their 20s and 30s who had made the long march to the campaign and made him president And they got to the Bay of Pigs and and the Cuban Missile crisis and he was their hero and now he was gone And this the sadness of this scene was evident on the faces of these young women with the mascaras streaking their cheeks as As they were waiting for Mrs. Kennedy and once she came into the room The crime became almost unbearable among everyone. They the sobbing was just Unbelievable and was Mrs. Kennedy crying. This is no was Jackie Kennedy crying No, she was not she was standing in the doorway and we could see her President Johnson left his place in the middle of the room walked over to her took her by both hands and He walked backward Sort of backward and took her and placed her to his left in the center of the room and he pulled Mrs. Johnson over to his right and He asked for a glass of ice water and Marie brought him some ice water and then he took looked at the judge and he said proceed now As a pool reporter, it's my responsibility to write everything. I saw I'm just a rinky-dink reporter who covered the police station in Ohio And now I'm covering one of the biggest stories in the world and I have to tell you that I was Worry that I wouldn't get everything I had to get it was this it was such an important story But I examined her pretty carefully. I saw blood on her legs It had congealed on both legs I saw blood and speckles of brain matter on her on her skirt and on her blouse. I Notice that she was unblinking She was in grief, but she had her wits about her. She knew was exactly what was going on She understood everything was going on. I don't think she would have come forward had she not known what was going on I think in the annals of history, this was one of the most courageous things I've ever seen between the president and a another first lady where she I believe having to Suffering the probably the worst thing that could happen to a married couple losing your husband in a murderous situation Leaving the casket to come up front To attend the ceremony took a lot of courage And I think Mrs. Kennedy felt it was important for her to be in that room at the same time I thought that gesture by president Johnson Asking her to come forward if she she said if she would like to stand with us because he knew the circumstances. I think that the The opportunities for greatness were there in that in that setting for the so brief a time the compassion shown by president Johnson I've never I've never seen it since he he invited all the members of Kennedy staff on the airplane That wanted to to come into the compartment. They jam the compartment as close as they could he asked people to get closer together So that more of the Kennedy people could could attend the thing In his behavior, I never saw anybody who was more resolved than what he had to do He had he had his wits totally about him He went over as soon as the oath was finished. He kissed Mrs. Johnson. Then he kissed Mrs. Kennedy on the cheek Then he went over to president Kennedy's secretary Evelyn Lincoln He shook her hand and held her tightly and said he was sorry that this had happened And then he fended off almost any effort on the part of anyone to come up and congratulate him Which was another sign of I think his greatness and he did not want this to turn into a celebration And if the somber mood that existed from the Benny was there at the end my friend Chuck Roberts one of the other poolers who was standing near me but went over to president Johnson and He shook his hand he looked up at the president's he was president six three or six four And he looked up the president he told me later He said I looked up at him and now he's different and he said I didn't know what to say So I said Godspeed mr. President And I thought I wish I could have said something like that is it was so appropriate for him to have said that and Then president Johnson's first order was let's get airborne that they knew they had to move that airplane Out of Dallas and get back to Washington