Cancer Clinical Trials: The $500B Scandal Exposed

Imagine you're told we've got a breakthrough. It's a clinical trial that might save your life. You're scared, you're hopeful. But here's what nobody's saying out loud. You might be randomized to a single experiment drug aimed to one marker or you might end up in the control arm getting a standard chemo or sometimes a placebo like add on that's layered on top of your already existing care. Meanwhile, your cancer is using dozens of pathways, what we call the hallmarks of cancer, to survive, grow, resist treatment. And you're trying to find the one hit wonder. That's like sending out a firefighter fighter with one hose to put out a fire that's burning on 20 different levels. It's not linked to your specific unique biology or the heterogeneicity or the uniqueness of your cancer. You have two patients with the same type of cancer and stage and yet when you run the deep mapping, the deep multiomics, they have different targets. So today I want to talk about the good, the bad and the ugly with clinical trials in cancer and why I believe the future is precision oncology. It's a multi pronged approach using n of 1. What does that mean? It's custom built to you. The markers are discovered on you and their custom built medicines and treatments for you. So n of 1 oncology is built for your body, your biology. The more the matches, the better. I'm Dr. Dino Prado. For the last 25 years, my team and I we've helped thousands of patients who have failed the top cancer hospitals across the country. But we've done that because of multi omics deep testing. Oftentimes what I hear is people are just going into a clinical trial and what happens is you become kind of like a guinea pig for an experiment instead of getting the right targets for you. And what I mean by right targets is not one drug that helps you live a little bit longer. Which most of the drugs on the market today are cytostatic, which means they slow down cancer, they don't necessarily kill cancer, which is cytotoxic. What I'm talking about is making sure that when you build a plan that's for you, not a one size fits all, but a blueprint for you that it's designed for your immune system and for long term responses. Now nobody can guarantee long term responses, but when you build a plan on you and you're using multiple pathways and targets, your success rates go way up. Instead of waiting on a one hit wonder drug, that's Where I think the real breakthrough is going to be is when we start moving away from the double blind placebo clinical trial which has been the gold standard for years, and start moving to n of one where we can custom build based on data telling us what that cancer needs. It's based on detailed testing. So let me explain that. First of all, most patients don't get testing. Less than 30% of patients even get basic DNA testing. And that's very basic. Several hundred markers, I'm talking about over a thousand plus markers. And the bioinformatics or the planning that's done on those algorithms then utilizes the best in data pulling, which is where artificial intelligence comes in to custom build the algorithms for your cancer so that your cold tumors that are hiding become hot and visible to your immune system. So that your toxicities can be virtually little to none. And you have a good response and long term response to cancer. Because you're not just relying on on label drugs which are maybe only a few. See, big pharma owns the current model and their goal is to develop a drug for a target and then mass produce it in one vial, self make thousands of them every 10 minutes and ship them and sell them across the world. There's nothing wrong with that. There's a place for that. But when we can custom build on the patient, with today's deep learning, we such a huge advantage because we have a full picture. We're looking at all the hallmarks of cancer, all the pathways in which cancer grows and spread. So I want to walk you through this idea together. How clinical trials are supposed to work on paper. They test a new drug to see if they're safe and effective. That's the phase one part of the study. Then you compare that to options against the standard of care or placebo. Most are not better than older copies of drugs, meaning sometimes they're just modifications because that pharmaceutical company wants to get market share, you see. And they do that by having a similar drug. And the key in my experience to have good long term outcomes is to go with an immune centric care, meaning getting the cold tumors that aren't visible to be hot. So your immune system will do the job. Now remember, the immune system is the first and last defense against cancer. I don't care which clinical trial you're using, if even if it's an immunotherapy drug. If you look at the PD1 inhibitors which have exploded on the market, multi billion dollar sales worldwide, the average time it works is about, you know, 10 months and maybe 19% of the patients have long term durability with it. So that means it's not doing the job. It's only one aspect of the immune where n of 1 targeting comes in because we can then find the breaks in the immune system, support those to help people respond longer with better quality of life. So the evidence for these new drugs comes from the FDA model, which I think is antiquated because the double blind placebo clinical trial though it is the gold standard. A lot of times new drugs aren't better than others and they'll get accelerated quickly. It's called fast track. So if you have a cancer drug that can get on fast track, you can get it approved quickly. And oftentimes those drugs do not prove to lengthen people's lives. So this is why the system's a little bit fuzzy. It's not as clean as you think. And not everybody has the same response because they might be testing one drug on one marker, but they're not looking at all your markers, which would have already told us likely that drug isn't going to work. And so that's the key. And so in the world of pharmacology, the goal is to find one RNA or DNA marker and find a mutation and find one drug to treat that. But these slow cancer down, they don't cure it. And my goal here is to look at all the targets and then custom build on off label, repurpose phytotherapeutic cancer cell vaccines that we build. That's unique to the, that's what we're doing here with the cancer. We're looking at the fingerprint of the cancer and then it's designing for us the drug targets, the immunotherapy targets, the diet, the lifestyle integrative conventional with an unbiased approach, letting the data dry. The clinical trial model is to find one drug, get it approved and mass sell it. And that's not bad, but it doesn't solve what we're trying to solve, which is removing cancer altogether, getting people to heal. This is a different philosophy which is coming because of AI. If you look at the work we're doing in precision oncology, it's about a decade ahead, but that's going to quickly change because of all of the artificial intelligence and deep learning is going to accelerate the access for these medications for patients. And this is a wonderful day because then people will have safer, less toxic, better options to live. And that's the goal. So clinical trials are needed, but I think there's a better way to do It, I think you do it with N of 1. And not just a one hit wonder drug. But the goal of clinical trials today is really to help people find new drugs. But it also is a goal of pharma having royalties and mass profits. And there's nothing wrong with that. But the drugs often do not kill cancer. They basically manage it. And managing it makes more money than killing it. So cytostatic versus cytotoxic. I've done episodes on that. But here's where the story changes. When you step out of the brochure into the real clinical experience. Nobody explains that very few adults with cancer ever make it onto a trial. National estimates only 5 to 8% even get into a trial. And 20% to 40% of oncology patients fail to meet enrollment. Even some of these new drugs don't even get enough patients access to them. Wouldn't this be much better in an open database with n of one shotgun testing like I talk talk about a thousand plus markers and then let the AI data guide exactly which drugs each patient needs at the right doses for them? This would be a new way to introduce a powerful drug pipeline that's custom built for each patient. That's the ecosystem we've built. But in the conventional model with clinical trials, only 3.3 to 3.4% of oncology drug programs actually make it from phase one to approval. That's very low. So only 3% go all the way. Most drugs don't make it. Imagine how many of those drugs are actually fabulous targets that never made it. For some reason. Too high of a dose, not the right combinations. These are all the things that artificial intelligence can fix and is doing it right now. In fact, there are studies around the world, including in our facilities, to do this. Because these drugs are powerful in microdoses. That's true of plants, natural agents, dietary lifestyle changes. These are going to all be dialed in with the right information. Trials often move very slowly. Oncology programs run longer and generate far more data than most of the other fields of medicine. And they have years of complexity. So who makes the money in the trial ecosystem? No surprise here, big pharma, if it goes to market. But the ones that make out are the big hospitals, the titans, the big hospitals, you know them, they run the studies, they pay them lots of money, billions of dollars to run these studies. So it's kind of a backwards system. Instead of finding what a patient needs and giving it to them, we're trying to find a drug that we can patent, own, and have royalties on which Is okay, but it's not going to work for everybody. It's only going to work for a few people and sometimes not much better than other drugs. So this is why the hospitals make a lot of money. They pay for per patient visits, monitoring the patients complications, ICUs and they have entire buildings, administrations dedicated, they publish papers, they get money from National Institutes of Health and they get all kinds of benefits to push this industry of trials. And it's still not producing what we want it to produce. Billions and billions of dollars and people are still dying of cancer and our outcomes haven't really gotten that much better. If you look at what I've said before. Billions spent in the pharmaceutical world and what's the average lifespan that's increased? Maybe three and a half months. If we focused on N of 1 in immunotherapy, I think we'd be many times, times that many, many times. I know, because we've seen that. Because the data is getting better, because we custom build it for each patient. So patients may still face all kinds of problems on clinical trials. They got into the study, they can't do anything else. Something might exclude them from the study. So I think there's problems with it. But that doesn't mean clinical trials haven't saved people's lives. I know of stories with patients that they were the one in few who survived the trial and got benefit from it. And that's great, that's a blessing. But it doesn't work like that for everybody. So it's something you have to weigh as a patient. Do you want a full planning design on you or are you going to try one for your target? And I think that's a decision each patient needs to make. But they need to be knowledgeable, understand what the mechanism of the drug is, is it going to work in combination, what the toxicity of the drug is? And that information to me I think is critically important for quality of life. Because there are more than one way to help get a patient into remission. It's by having the right targets. So none of this means the trials are bad. It means that they have financial incentives. I shared that, the hospitals, the infrastructure. But when you're looking at them, you're only looking at one drug, one target, a study protocol, that's the clinical trial so that they can use it around the world, produce the ampoule, sell. That's Not n of 1. N of 1 is where it's the combinations that are customized for you. It's not looking at a one hit wonder, it's Looking at all the combined pathways that are going to give you an advantage. So most cancer trials are built around a main drug or a regimen aimed at a pathway or one biomarker. If you've got it, it works. The drug goes to market, you get matched for one target. And the difference is tumors are heterogeneous. They have many different markers that are different from one patient to the other. So they're not able to hit all that. Where the goal of N of 1 or precision oncology, is to hit more of those targets, giving better survival, better response rates with targeting, that's what we see. And so there's a huge barrier to just treating one mutation at a time, one drug at a time. But when you use n of 1, we can get there so much faster because we're opening up the tool belt. We have off label, on label, repurposed phytotherapeutic, cellular biologics, immunotherapies, all custom built at the right doses and combinations to help the patient. That's the key. So that's why I believe the n of one pathway is superior to clinical trials and my clinical experience. But you have to expl all these as a patient and make those decisions for yourself, you know, and look at these pieces and put them all together. So the sooner you bring in precision oncology, the sooner you are operating at a much more advanced level. I'm a big fan of the star wars movies, and it's almost like, you know, somebody lays in a bed and it scans their body and finds what's wrong and fixes it. Well, we're getting there. We can do that to some extent with blood work, where we're scanning all the right targets, but we're even getting there with other devices and tools. And the advancements are going to continue to grow in oncology. So I don't want to discourage you away from clinical trials, but I think there's a better way. But the question you want to ask, is this treatment cytostatic, Is it going to hold the cancer back for a short period of time, or is it going to actually kill the cancer? Because what we want is treatments that kill the cancer and make it go away so that we can get on with our lives. And we don't just want to slow things down. Now, sometimes we have to use combinations because we want to slow things down so we can kill them. But we need to make sure our algorithms are working correctly. For over the last two decades, 90% of the new oncology approvals have been targeted on biologics that are not cytotoxic. So most of them slow it down. The literature repeatedly describes targeted agents as primarily slowing the cancer down. Cytostatic. So lots of slowing, less direct tumor kill. And we don't want that unless you have immunotherapy, which is what I'm talking about, that everything is immune centric in the design. Now we can move to cytotoxic. We can take a cold tumor that's hiding, make it visible so it can be killed by your immune system. So that's important. That's what I believe FDA should be looking at. I believe that's the whole key of precision. Oncology is focusing on that so that it's immune centric and people can survive, have better progression free survival, better response rates and start to really see a change in the world of cancer. We don't have to be as scared and we can be treated correctly and move on with our life. So when a patient feels like they're donating their body to science, there's something wrong. We need to find a pattern of care that's custom for the individual, it's not randomized and it's specific to the person. I like to look at real world examples like the I predict studies or the winter studies, or the studies that we've published. These tell us that precision is the way of helping patients respond better to care. The real benefits of combinations and target, not just one size fits all. And yes, there's going to be some great drugs that come out of the double blind placebo model, but it may not work for everybody. And until we have all the markers and data working in our favor, we won't know. Instead of giving the immune system what it needs, Changing that tumor microenvironment, Finding the right drugs for that patient, or target's integrative natural combination so the immune system can turn on. That's where we need to be spending our time and energy Because I believe we already have most of the treatments we need. What we need is better matching. That's what I've Learned in my 25 years. That's very important. We need better matching. We've dedicated our team and our work to get these molecular matches happening. We can improve protocols, get more patients feeling better, Getting them the care they need, Reduce their side effects and give people more life and get them out of guesswork. So that's how we optimize care n of 1 and that's how the precision model works. So that's why I like precision oncology over clinical trials. But that doesn't mean in your specific case, clinical trial might be a bad selection. But it's important to note that there's a big difference between the two. And to make sure your treatment is immune focused, cytotoxic, killing the cancer. It's microdosed, it's the right fit for you, and all your combinations are working in your favor. These are the types of questions that you want to ask your doctor. How does this work in concert with the rest of my therapy? Is it going to stimulate my immune system? Is this going to help me go into long term remission or just slow things down? I want to know those things and I want to know what the potential toxicity profiles and maybe adjusted doses, things that they don't do in clinical trials. In clinical trials, they like to give maximum high doses and work their way down. And that's not the way we do it. In precision oncology, we go the other way. We try to give the smallest dose to get the best benefits so we keep the person healthy and responding better. And you'll find that oftentimes drugs are given in way too high doses and they're not given in the right combinations for that individual patient. So I hope this was helpful. I don't want to discourage you to clinical trials, but I think there's a big disadvantage on trying to find that one drug, one target, instead of a full ecosystem that's custom built for you and what we call n of one precision oncology. May the Lord bless you on your journey to healing. Sam.