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🔹 AI drug discovery, validated. Recursion recently announced the first clinical validation of its AI-enabled drug discovery platform – positive Phase 1b/2 data for the ongoing TUPELO study evaluating its AI-identified drug, REC-4881, in Familial Adenomatous Polyposis (FAP). This progressive rare disease affects more than 50,000 people in the...

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Recursion

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255,879 просмотров • 8 месяцев назад •via X (Twitter)

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🚀 How Recursion used AI to identify a novel approach for potentially treating the rare disease Familial Adenomatous Polyposis (FAP). A new video shares the story of Recursion’s investigational drug REC-4881 for the rare disease FAP which affects more than 50,000 people in the US and EU5 and has no approved medicines. Patients with this disease develop hundreds to thousands of polyps in their early teens to 20s that, if left untreated, have a nearly 100% risk of developing into colon cancer. ⬇️ Here’s how we did it: ▪️ Using the Recursion OS platform to analyze cell morphology, we tested thousands of compounds to find drugs that restored FAP-diseased cells to a healthy state. ▪️ This led us to REC-4881 — a drug that blocks a biological pathway called MEK1/2 – a new approach for treating FAP that had not been investigated clinically before. ▪️ Now, we’re using the full Recursion OS 2.0 platform — including our ClinTech real-world evidence – to fully understand disease burden and guide our clinical strategy, allowing us to expand patient eligibility for our trial. 💡 The takeaway: This program is the first clinical validation of the Recursion OS, and demonstrates our ability to translate unbiased phenotypic insights into potentially differentiated treatments for diseases with high unmet need. 👉 Read the full summary data in our December 8, 2025 press release here:
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🚀 How Recursion used AI to identify a novel approach for potentially treating the rare disease Familial Adenomatous Polyposis (FAP). A new video shares the story of Recursion’s investigational drug REC-4881 for the rare disease FAP which affects more than 50,000 people in the US and EU5 and has no approved medicines. Patients with this disease develop hundreds to thousands of polyps in their early teens to 20s that, if left untreated, have a nearly 100% risk of developing into colon cancer. ⬇️ Here’s how we did it: ▪️ Using the Recursion OS platform to analyze cell morphology, we tested thousands of compounds to find drugs that restored FAP-diseased cells to a healthy state. ▪️ This led us to REC-4881 — a drug that blocks a biological pathway called MEK1/2 – a new approach for treating FAP that had not been investigated clinically before. ▪️ Now, we’re using the full Recursion OS 2.0 platform — including our ClinTech real-world evidence – to fully understand disease burden and guide our clinical strategy, allowing us to expand patient eligibility for our trial. 💡 The takeaway: This program is the first clinical validation of the Recursion OS, and demonstrates our ability to translate unbiased phenotypic insights into potentially differentiated treatments for diseases with high unmet need. 👉 Read the full summary data in our December 8, 2025 press release here:

Recursion

11,944 просмотров • 6 месяцев назад

🚀 Reshaping drug discovery – and the future of medicine – with AI. Recursion CFO Ben Taylor joined the Opto Sessions podcast from CMC Aureon to share how Recursion uses AI, lab automation, biological data, and machine learning to improve the way new medicines are discovered and developed. 💠 They discuss: ▪️How pairing AI with lab automation across biology, chemistry, and clinical technology allows us to move beyond the ~10–12% of the genome currently addressed by approved or in-trial medicines and explore the vast realm of untapped biology. ▪️How Recursion is advancing REC-4881 for patients with familial adenomatous polyposis (FAP) — a progressive condition with no approved medical therapies — after achieving Phase 2 proof-of-concept and demonstrating meaningful reductions in polyp burden. ▪️How Recursion is compressing timelines and improving efficiency with high-quality, ML-ready data to design better molecules with ~90% fewer experimental chemistries and to run smarter, faster clinical trials. 👉 Check out the full conversation:
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🚀 Reshaping drug discovery – and the future of medicine – with AI. Recursion CFO Ben Taylor joined the Opto Sessions podcast from CMC Aureon to share how Recursion uses AI, lab automation, biological data, and machine learning to improve the way new medicines are discovered and developed. 💠 They discuss: ▪️How pairing AI with lab automation across biology, chemistry, and clinical technology allows us to move beyond the ~10–12% of the genome currently addressed by approved or in-trial medicines and explore the vast realm of untapped biology. ▪️How Recursion is advancing REC-4881 for patients with familial adenomatous polyposis (FAP) — a progressive condition with no approved medical therapies — after achieving Phase 2 proof-of-concept and demonstrating meaningful reductions in polyp burden. ▪️How Recursion is compressing timelines and improving efficiency with high-quality, ML-ready data to design better molecules with ~90% fewer experimental chemistries and to run smarter, faster clinical trials. 👉 Check out the full conversation:

Recursion

98,839 просмотров • 1 месяц назад

Early data suggests Recursion (RXRX)’s AI-native platform is dramatically reducing the time and cost of drug discovery. Will it turn the stock around? Shea Wihlborg shares her thoughts in our March webinar.
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Early data suggests Recursion (RXRX)’s AI-native platform is dramatically reducing the time and cost of drug discovery. Will it turn the stock around? Shea Wihlborg shares her thoughts in our March webinar.

ARK Funds

30,583 просмотров • 4 месяцев назад

Today, Recursion reported our 4Q/FY25 business updates and financial results. As CEO and President Najat Khan said, “Recursion has reached an inflection point: moving from proving that AI can participate in drug discovery to demonstrating that an AI-native operating system can generate clinical proof and durable value.” This includes: 🚀 A 5th milestone with Sanofi for a first-in-class Sanofi-partnered oncology program against a historically difficult and novel biological space. This brings the total upfront and progress-based milestones to $134 million to date. Five Recursion discovery program packages have been accepted to date, establishing a growing joint portfolio of novel AI-driven small molecules for immunology and oncology. 🚀 The first AI-enabled clinical validation of the Recursion OS platform in our FAP program, demonstrating translation from AI-driven biological insight to meaningful patient outcomes. We also have multiple clinical and preclinical programs advancing with defined milestones. 🚀 New preclinical efficacy data for REC-7735, a potential best-in-class PI3K⍺ H1047R inhibitor, precision designed with 242 compounds synthesized from first novel hit to REC-7735 in 10 months using the Recursion OS platform. REC-7735 demonstrates >100-fold selectivity for the H1074R mutation over WT PI3K⍺ suggesting potential improved tolerability over current inhibitors and is currently in IND-enabling studies. 🚀 $754 million of cash and cash equivalents. We exceeded our original cost savings guidance and now expect runway into early 2028, without additional financing. 👉 Read the full business updates here: 🔹 Tune in to our Earnings Call today, February 25, 2026 at 8:00 am ET / 6:00 am MT / 1:00 pm GMT, here on X, or on: ▪️ YouTube: ▪️ LinkedIn:
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Today, Recursion reported our 4Q/FY25 business updates and financial results. As CEO and President Najat Khan said, “Recursion has reached an inflection point: moving from proving that AI can participate in drug discovery to demonstrating that an AI-native operating system can generate clinical proof and durable value.” This includes: 🚀 A 5th milestone with Sanofi for a first-in-class Sanofi-partnered oncology program against a historically difficult and novel biological space. This brings the total upfront and progress-based milestones to $134 million to date. Five Recursion discovery program packages have been accepted to date, establishing a growing joint portfolio of novel AI-driven small molecules for immunology and oncology. 🚀 The first AI-enabled clinical validation of the Recursion OS platform in our FAP program, demonstrating translation from AI-driven biological insight to meaningful patient outcomes. We also have multiple clinical and preclinical programs advancing with defined milestones. 🚀 New preclinical efficacy data for REC-7735, a potential best-in-class PI3K⍺ H1047R inhibitor, precision designed with 242 compounds synthesized from first novel hit to REC-7735 in 10 months using the Recursion OS platform. REC-7735 demonstrates >100-fold selectivity for the H1074R mutation over WT PI3K⍺ suggesting potential improved tolerability over current inhibitors and is currently in IND-enabling studies. 🚀 $754 million of cash and cash equivalents. We exceeded our original cost savings guidance and now expect runway into early 2028, without additional financing. 👉 Read the full business updates here: 🔹 Tune in to our Earnings Call today, February 25, 2026 at 8:00 am ET / 6:00 am MT / 1:00 pm GMT, here on X, or on: ▪️ YouTube: ▪️ LinkedIn:

Recursion

11,878 просмотров • 6 месяцев назад

Demis Hassabis explains Isomorphic Labs' 10x drug discovery engine, and why solving disease could also create a multi-hundred-billion-dollar business "If you ask me, the number one thing AI can do for humanity, it would be to solve hundreds of terrible diseases. I can't imagine a better use case for AI." "If you could revolutionize the drug discovery process, make it 10x faster and more efficient, and more likely to pass through the clinical trials because you can predict the properties better, that has to also be of enormous commercial value too." "I hope to do both with Isomorphic." "Build a multi-hundred-billion-dollar business. I think it has that potential, as well as being incredibly beneficial for society and humanity." The real health AI prize is not another chatbot. It is compressing the path from a predicted drug property to a clinical trial, then turning that speed into treatments for hundreds of diseases. Demis is making a rare dual bet: the most valuable AI company in biology may also deliver AI's most valuable public benefit. - Demis Hassabis (Demis Hassabis), CEO of Google DeepMind and founder of Isomorphic Labs, on Bloomberg TV
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Demis Hassabis explains Isomorphic Labs' 10x drug discovery engine, and why solving disease could also create a multi-hundred-billion-dollar business "If you ask me, the number one thing AI can do for humanity, it would be to solve hundreds of terrible diseases. I can't imagine a better use case for AI." "If you could revolutionize the drug discovery process, make it 10x faster and more efficient, and more likely to pass through the clinical trials because you can predict the properties better, that has to also be of enormous commercial value too." "I hope to do both with Isomorphic." "Build a multi-hundred-billion-dollar business. I think it has that potential, as well as being incredibly beneficial for society and humanity." The real health AI prize is not another chatbot. It is compressing the path from a predicted drug property to a clinical trial, then turning that speed into treatments for hundreds of diseases. Demis is making a rare dual bet: the most valuable AI company in biology may also deliver AI's most valuable public benefit. - Demis Hassabis (Demis Hassabis), CEO of Google DeepMind and founder of Isomorphic Labs, on Bloomberg TV

Karl Mehta

46,918 просмотров • 1 месяц назад

Demis Hassabis: If you know the structure of a protein, the real question becomes—where will your drug bind, and what will it actually do? That’s where the next wave of AI comes in. Not just predicting structures, but modeling interactions, outcomes, and real biological impact. At Isomorphic Labs, this is already happening—with 17 active drug programs and partnerships with giants like Eli Lilly and Novartis. The goal? Scale that to 100. This is a fundamental shift in how medicine gets built. Instead of slow, expensive trial-and-error in wet labs, AI allows researchers to run thousands of hypotheses in silico—hundreds to thousands of times more efficiently. The wet lab becomes validation, not exploration. Drug discovery is turning into a computational problem. Faster cycles, smarter predictions, and potentially massive breakthroughs in human health.
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Demis Hassabis: If you know the structure of a protein, the real question becomes—where will your drug bind, and what will it actually do? That’s where the next wave of AI comes in. Not just predicting structures, but modeling interactions, outcomes, and real biological impact. At Isomorphic Labs, this is already happening—with 17 active drug programs and partnerships with giants like Eli Lilly and Novartis. The goal? Scale that to 100. This is a fundamental shift in how medicine gets built. Instead of slow, expensive trial-and-error in wet labs, AI allows researchers to run thousands of hypotheses in silico—hundreds to thousands of times more efficiently. The wet lab becomes validation, not exploration. Drug discovery is turning into a computational problem. Faster cycles, smarter predictions, and potentially massive breakthroughs in human health.

Ian Miles Cheong

93,975 просмотров • 5 месяцев назад

Hello, my dear friends, we have some breaking news today! Our rentosertib (novel target that has never been in clinical trials before we picked it using AI, novel molecule designed using true generative AI with the published broadly used AI platform, targeting fibrosis and biology of aging) entered Phase III trial in Idiopathic Pulmonary Fibrosis. This is a major milestone in the life of every drug hunter - the ultimate test. To my knowledge, in our AI drug discovery industry, no other company took the drug for a novel target with a novel molecule into Phase III before so it is a big milestone for the entire industry. Of course, at Insilico we are no longer dependent on this program - we have 13 INDs and many clinical trials are ongoing internally and run by partners. But this program is very special as we also demonstrated the senomorphic properties of this drug and built the entire pipeline of molecules around this target. Please Wish Me Luck! Luck is the most important superpower in everything we do and if we succeed with this program and also demonstrate the antiaging effect - all of us will benefit. Godspeed, my friends and thanks to everyone who helped us along the way!
2:26

Hello, my dear friends, we have some breaking news today! Our rentosertib (novel target that has never been in clinical trials before we picked it using AI, novel molecule designed using true generative AI with the published broadly used AI platform, targeting fibrosis and biology of aging) entered Phase III trial in Idiopathic Pulmonary Fibrosis. This is a major milestone in the life of every drug hunter - the ultimate test. To my knowledge, in our AI drug discovery industry, no other company took the drug for a novel target with a novel molecule into Phase III before so it is a big milestone for the entire industry. Of course, at Insilico we are no longer dependent on this program - we have 13 INDs and many clinical trials are ongoing internally and run by partners. But this program is very special as we also demonstrated the senomorphic properties of this drug and built the entire pipeline of molecules around this target. Please Wish Me Luck! Luck is the most important superpower in everything we do and if we succeed with this program and also demonstrate the antiaging effect - all of us will benefit. Godspeed, my friends and thanks to everyone who helped us along the way!

Alex Zhavoronkov, PhD (aka Aleksandrs Zavoronkovs)

49,544 просмотров • 1 месяц назад

NEW episode! Drug development has never been more expensive, in terms of output per dollar spent. This trend, called Eroom’s law, is surprising, considering the incredible technological advances in drug discovery, from genome sequencing to engineering to microscopy. On a new episode of the Works in Progress podcast, Ben Southwood and I talk to Ruxandra Teslo 🧬 about why this has happened and what can be done about it. We discuss how: • AI isn’t a magic bullet for drug discovery. Predictive models lack the physical human data, like individual variation and rare side effects, that can only be generated by actually running real-world clinical trials. • As scientists invent more effective drugs, it becomes harder to discover new treatments that can surpass past successes. This is known as the "Better than the Beatles" problem. • Biotech companies are increasingly moving their "first-in-human" trials to Australia because its simpler regulations allow researchers to test drug safety faster and cheaper than in the US. • Clinical trials can be made more efficient with various reforms including: embracing platform trials, allowing researchers to select from independent ethics boards, expanding the funding and validation of surrogate endpoints, increasing transparency by releasing regulatory correspondence from failed companies, and much more. Timestamps: 00:00:00 Eroom’s law and the paradox of drug development 00:08:03 How clinical trials actually work 00:10:23 The power and controversy of surrogate endpoints 00:14:01 How historical patent laws influenced trial timelines 00:22:46 The Australia advantage and regulatory drag 00:29:08 Institutional review boards (IRBs) and bureaucratic drag 00:32:21 Open science and successful reforms 00:41:49 Our wishlist for clinical trial reforms, and which reforms we *don’t* like 00:53:48 Why AI isn’t a magic bullet for drug discovery
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NEW episode! Drug development has never been more expensive, in terms of output per dollar spent. This trend, called Eroom’s law, is surprising, considering the incredible technological advances in drug discovery, from genome sequencing to engineering to microscopy. On a new episode of the Works in Progress podcast, Ben Southwood and I talk to Ruxandra Teslo 🧬 about why this has happened and what can be done about it. We discuss how: • AI isn’t a magic bullet for drug discovery. Predictive models lack the physical human data, like individual variation and rare side effects, that can only be generated by actually running real-world clinical trials. • As scientists invent more effective drugs, it becomes harder to discover new treatments that can surpass past successes. This is known as the "Better than the Beatles" problem. • Biotech companies are increasingly moving their "first-in-human" trials to Australia because its simpler regulations allow researchers to test drug safety faster and cheaper than in the US. • Clinical trials can be made more efficient with various reforms including: embracing platform trials, allowing researchers to select from independent ethics boards, expanding the funding and validation of surrogate endpoints, increasing transparency by releasing regulatory correspondence from failed companies, and much more. Timestamps: 00:00:00 Eroom’s law and the paradox of drug development 00:08:03 How clinical trials actually work 00:10:23 The power and controversy of surrogate endpoints 00:14:01 How historical patent laws influenced trial timelines 00:22:46 The Australia advantage and regulatory drag 00:29:08 Institutional review boards (IRBs) and bureaucratic drag 00:32:21 Open science and successful reforms 00:41:49 Our wishlist for clinical trial reforms, and which reforms we *don’t* like 00:53:48 Why AI isn’t a magic bullet for drug discovery

Saloni

109,256 просмотров • 4 месяцев назад

Demis Hassabis, the Nobel Prize winner who runs Google DeepMind just described the most consequential project on earth, and most people have no idea it exists. The project is called Isomorphic Labs and the goal is to end the way drugs have been developed for the last century. Here is the problem it is trying to solve. Developing a single drug today takes an average of 10 years, costs billions of dollars, and fails 90 percent of the time before it ever reaches a patient. Of every 10 drugs that enter clinical trials, only one makes it through. The other nine years of work, the other billions of dollars, the other scientific careers, gone. Hassabis believes AI can collapse that entire process from identifying a disease target to designing a compound that binds to it, predicts how it behaves in the body, and minimizes side effects , end to end, on a computer, before a single experiment is run. The foundation is AlphaFold, the AI system that solved one of biology's hardest problems predicting the 3D structure of every protein in the human body and won him the Nobel Prize in Chemistry in 2024. But knowing a protein's shape is only one part of designing a drug. Isomorphic is building what Hassabis describes as adjacent systems , AlphaFold 3, AlphaFold 4, and now a unified model called IsoDDE , that take the next steps. From designing the actual chemical compound that binds to the protein, predicting its binding strength, identifying new pockets to target that no one has ever found before. IsoDDE more than doubles the accuracy of AlphaFold 3 on the hardest protein-ligand prediction benchmarks that exist. Isomorphic is already running 18 to 19 live drug programs, cardiovascular disease, cancer, immunology in partnership with Eli Lilly, Novartis, and Johnson and Johnson. The first human clinical trial of a fully AI-designed drug is expected by the end of 2026. If that trial succeeds, it will be the first time in history that a drug put into a human body was designed not by a team of chemists working for a decade but by an AI working for months. Hassabis's long-term vision is even more direct, one day you describe a disease, click a button, and a drug blueprint comes out the other side. AI will solve almost all diseases within 10 years.
1:20

Demis Hassabis, the Nobel Prize winner who runs Google DeepMind just described the most consequential project on earth, and most people have no idea it exists. The project is called Isomorphic Labs and the goal is to end the way drugs have been developed for the last century. Here is the problem it is trying to solve. Developing a single drug today takes an average of 10 years, costs billions of dollars, and fails 90 percent of the time before it ever reaches a patient. Of every 10 drugs that enter clinical trials, only one makes it through. The other nine years of work, the other billions of dollars, the other scientific careers, gone. Hassabis believes AI can collapse that entire process from identifying a disease target to designing a compound that binds to it, predicts how it behaves in the body, and minimizes side effects , end to end, on a computer, before a single experiment is run. The foundation is AlphaFold, the AI system that solved one of biology's hardest problems predicting the 3D structure of every protein in the human body and won him the Nobel Prize in Chemistry in 2024. But knowing a protein's shape is only one part of designing a drug. Isomorphic is building what Hassabis describes as adjacent systems , AlphaFold 3, AlphaFold 4, and now a unified model called IsoDDE , that take the next steps. From designing the actual chemical compound that binds to the protein, predicting its binding strength, identifying new pockets to target that no one has ever found before. IsoDDE more than doubles the accuracy of AlphaFold 3 on the hardest protein-ligand prediction benchmarks that exist. Isomorphic is already running 18 to 19 live drug programs, cardiovascular disease, cancer, immunology in partnership with Eli Lilly, Novartis, and Johnson and Johnson. The first human clinical trial of a fully AI-designed drug is expected by the end of 2026. If that trial succeeds, it will be the first time in history that a drug put into a human body was designed not by a team of chemists working for a decade but by an AI working for months. Hassabis's long-term vision is even more direct, one day you describe a disease, click a button, and a drug blueprint comes out the other side. AI will solve almost all diseases within 10 years.

Milk Road AI

36,062 просмотров • 4 месяцев назад

Artificial Intelligence is far more than a productivity tool. Its greatest potential lies in advancing knowledge and driving the next wave of scientific discovery. At the #RaisinaDialogue2026 in New Delhi, emphasised how AI can catalyse breakthroughs and drive innovations capable of transforming lives across the world.
2:52

Artificial Intelligence is far more than a productivity tool. Its greatest potential lies in advancing knowledge and driving the next wave of scientific discovery. At the #RaisinaDialogue2026 in New Delhi, emphasised how AI can catalyse breakthroughs and drive innovations capable of transforming lives across the world.

Smriti Z Irani

31,278 просмотров • 5 месяцев назад

"The future is really bright in AI and drug discovery." Sean McClain, Founder and CEO of Absci, shared the CES stage with healthcare leaders to illustrate how the compute partnership with AMD has scaled their ability to screen over a million drugs in one single day, resulting in better models for drug discovery.
1:14

"The future is really bright in AI and drug discovery." Sean McClain, Founder and CEO of Absci, shared the CES stage with healthcare leaders to illustrate how the compute partnership with AMD has scaled their ability to screen over a million drugs in one single day, resulting in better models for drug discovery.

AMD

15,346 просмотров • 6 месяцев назад

Joe Rogan Is Shocked To Learn How Drug Trials Really Work... Before the actual trial starts, the participants are put on the drug for 6 weeks & then EXCLUDED from the trial if they have side effects from the drug! It Is Legal To Remove People Who Are Harmed & Not Report It. Pre-Randomization Run In Period: Before the actual trial starts, the participants are put on the drug for 6 weeks & then EXCLUDED from the trial if they have side effects from the drug! Clinical trials are structured to minimise the harms of the drug & show ONLY the results they want, in order to get FDA approval. This skews the entire end result of the trial data to only include participants who experienced no negative side effects from the drug. The act of excluding a large group of people from clinical trials after they have taken the drug for several weeks is not only legal, but it is an accepted practice. This results in study data that grossly underestimates the actual rate of side effects associated with statins. This explains why the rate of side effects in statin trials is wildly different from the rate of side effects seen in real-world experiences by patients. In The Heart Protection Study, 36,000 participants were removed during the "pre-randomization run in period," before the actual clinical trial began. Participants took 40mg Simvastatin daily for 6 weeks & then were removed from participating in the actual clinical trial. Unacceptable side effects were experienced by these 36,000 individuals. It is legal to label this in the trial data as "participant non compliance." In a clinical trial, a participant is labeled "non-compliant" when they experience adverse drug side effects that they deem too harmful to their health. Since they cannot continue taking the trial drug, they are kicked out of the actual trial, for not continuing the drug dose protocol rules. 👇Statin Wars: Real Evidence Of Harm👇 👇Adverse Events Reported As Non Compliance👇 Speakers: Dr Aseem Malhotra Cardiologist Joe Rogan Joe Rogan Experience
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Joe Rogan Is Shocked To Learn How Drug Trials Really Work... Before the actual trial starts, the participants are put on the drug for 6 weeks & then EXCLUDED from the trial if they have side effects from the drug! It Is Legal To Remove People Who Are Harmed & Not Report It. Pre-Randomization Run In Period: Before the actual trial starts, the participants are put on the drug for 6 weeks & then EXCLUDED from the trial if they have side effects from the drug! Clinical trials are structured to minimise the harms of the drug & show ONLY the results they want, in order to get FDA approval. This skews the entire end result of the trial data to only include participants who experienced no negative side effects from the drug. The act of excluding a large group of people from clinical trials after they have taken the drug for several weeks is not only legal, but it is an accepted practice. This results in study data that grossly underestimates the actual rate of side effects associated with statins. This explains why the rate of side effects in statin trials is wildly different from the rate of side effects seen in real-world experiences by patients. In The Heart Protection Study, 36,000 participants were removed during the "pre-randomization run in period," before the actual clinical trial began. Participants took 40mg Simvastatin daily for 6 weeks & then were removed from participating in the actual clinical trial. Unacceptable side effects were experienced by these 36,000 individuals. It is legal to label this in the trial data as "participant non compliance." In a clinical trial, a participant is labeled "non-compliant" when they experience adverse drug side effects that they deem too harmful to their health. Since they cannot continue taking the trial drug, they are kicked out of the actual trial, for not continuing the drug dose protocol rules. 👇Statin Wars: Real Evidence Of Harm👇 👇Adverse Events Reported As Non Compliance👇 Speakers: Dr Aseem Malhotra Cardiologist Joe Rogan Joe Rogan Experience

Valerie Anne Smith

311,309 просмотров • 1 год назад

“If the FDA had enforced the law, pharma would owe $19 billion.” The FDA under Marty Makary is officially forcing drug companies to post all clinical trial results, not just the ones that make drugs look effective. “In 2007, Congress passed a law if you run a drug study in America, you have to post the results, all of them, not just the ones that worked.” “If a company runs 10 studies and only 2 work… and they only show the good ones and suppress the bad ones, then the doctor and the patient are misled.” “The fine for breaking this law is about $13,000 a day per trial omitted.” “How many fines has the FDA collected in 19 years? Zero.” “3 weeks ago, Dr. Marty Makary put out an announcement that Pharma has been suppressing unfavorable clinical trial results.” “He sent messages to 2,200 companies saying, ‘We know you’re out of compliance and you have to fix it.’” Dr. Eric Berg DC
2:34

“If the FDA had enforced the law, pharma would owe $19 billion.” The FDA under Marty Makary is officially forcing drug companies to post all clinical trial results, not just the ones that make drugs look effective. “In 2007, Congress passed a law if you run a drug study in America, you have to post the results, all of them, not just the ones that worked.” “If a company runs 10 studies and only 2 work… and they only show the good ones and suppress the bad ones, then the doctor and the patient are misled.” “The fine for breaking this law is about $13,000 a day per trial omitted.” “How many fines has the FDA collected in 19 years? Zero.” “3 weeks ago, Dr. Marty Makary put out an announcement that Pharma has been suppressing unfavorable clinical trial results.” “He sent messages to 2,200 companies saying, ‘We know you’re out of compliance and you have to fix it.’” Dr. Eric Berg DC

MAHA Action

15,876 просмотров • 4 месяцев назад

Heart disease is the #1 cause of death in the world today, with heart failure alone costing over $30 billion a year in the U.S. Myocarditis is one pathway into that broader crisis—and no drug has ever been shown to work in it. Data from the ARCHER trial now suggest CardiolRx™ could be the first—and may have opened the door to the largest unmet need in cardiology: heart failure. (NASDAQ: CRDL | TSX: CRDL) 🎥 Watch the full ARCHER Phase II results video:
3:11

Heart disease is the #1 cause of death in the world today, with heart failure alone costing over $30 billion a year in the U.S. Myocarditis is one pathway into that broader crisis—and no drug has ever been shown to work in it. Data from the ARCHER trial now suggest CardiolRx™ could be the first—and may have opened the door to the largest unmet need in cardiology: heart failure. (NASDAQ: CRDL | TSX: CRDL) 🎥 Watch the full ARCHER Phase II results video:

Cardiol Therapeutics Inc.

10,322 просмотров • 8 месяцев назад

This year Demis Hassabis predicted AI could cure all disease in a decade. But Claus Wilke & Derek Lowe say biology is far more complex, or progress will be limited by clinical trials & economics. In a new 4hr episode of the Hard Drugs podcast, we answer: Will AI solve medicine and cure all diseases (within a decade)? We talk about drug discovery, virtual cells, the Human Genome Project, manufacturing, nanobots, innovative clinical trial design, and much more. AI is already being used in drug discovery, and there’s been a lot of progress predicting the structure of soluble proteins, tweaking proteins and designing new structures, as we’ve covered in previous episodes. But there’s still a huge gap in understanding protein dynamics and interactions, as there are many areas where measurement tools and data collection are limited, including events that happen in the span of milliseconds or microseconds, which is how fast many things occur in biological systems. And while computing has scaled exponentially with Moore’s Law, drug development has faced the opposite: Eroom’s Law, where innovation has gotten more complex and more expensive over time. Even with promising drug candidates, we talk about why human testing – not in animals or virtual cells – will continue to be vital, to test which ones are effective and safe, even though models will help earlier in the pipeline. Beyond that, large samples and long follow ups are needed to detect rare side effects, understand whether drugs cause long-term complications, and find ways to manage them. It’s hard to see AI getting around the desire for rigorous safety data in real humans. Another big challenge is the capital and expertise needed to produce and scale personalized medicines and complex biological products, surgeries, transplants, antibodies, and gene-editing tools, which have entirely different cost structures from small molecule drugs. Their manufacturing and delivery often require highly skilled staff and expensive, intensive, individualized procedures. Cost challenges are also severe for tropical and rare diseases, where the financial return to diagnose, do research, develop drugs, manufacture and deliver them at scale, is limited. Without philanthropic funding and economic growth, a lot of diseases are going to remain uncurable, and a lot of people are going to go untreated – whether that’s because of a lack of trust, poor economic and financial incentives, limited public health ambition, and policy. In one sense, we’re skeptical that AI can solve medicine on its own. But in another, there are many areas where we think AI can help. So the episode also functions as a roadmap to speed up medical progress and scale up the delivery of lifesaving medicines – with AI and other approaches to reform the pipeline. What are the economic incentives, innovative trial designs, and data collection efforts that can help drive further medical progress? And how does AI fit in? You’ll have to listen to find out! Timestamps: 0:04:34 Contrasting AI optimism and skepticism 0:32:44 The non-linear path between science and technology 1:01:30 The fundamental need for experiments 1:23:15 Animals, organoids, and virtual cells 1:50:47 The challenges of collecting drug efficacy data in humans 2:34:02 The long road to drug safety data 3:06:09 The cost problem of delivering biological drugs and personalized medicine at scale 3:45:35 The global skew in R&D and healthcare funding 4:01:48 Trust, ambition, and the final barriers to medical progress
4:34:47

This year Demis Hassabis predicted AI could cure all disease in a decade. But Claus Wilke & Derek Lowe say biology is far more complex, or progress will be limited by clinical trials & economics. In a new 4hr episode of the Hard Drugs podcast, we answer: Will AI solve medicine and cure all diseases (within a decade)? We talk about drug discovery, virtual cells, the Human Genome Project, manufacturing, nanobots, innovative clinical trial design, and much more. AI is already being used in drug discovery, and there’s been a lot of progress predicting the structure of soluble proteins, tweaking proteins and designing new structures, as we’ve covered in previous episodes. But there’s still a huge gap in understanding protein dynamics and interactions, as there are many areas where measurement tools and data collection are limited, including events that happen in the span of milliseconds or microseconds, which is how fast many things occur in biological systems. And while computing has scaled exponentially with Moore’s Law, drug development has faced the opposite: Eroom’s Law, where innovation has gotten more complex and more expensive over time. Even with promising drug candidates, we talk about why human testing – not in animals or virtual cells – will continue to be vital, to test which ones are effective and safe, even though models will help earlier in the pipeline. Beyond that, large samples and long follow ups are needed to detect rare side effects, understand whether drugs cause long-term complications, and find ways to manage them. It’s hard to see AI getting around the desire for rigorous safety data in real humans. Another big challenge is the capital and expertise needed to produce and scale personalized medicines and complex biological products, surgeries, transplants, antibodies, and gene-editing tools, which have entirely different cost structures from small molecule drugs. Their manufacturing and delivery often require highly skilled staff and expensive, intensive, individualized procedures. Cost challenges are also severe for tropical and rare diseases, where the financial return to diagnose, do research, develop drugs, manufacture and deliver them at scale, is limited. Without philanthropic funding and economic growth, a lot of diseases are going to remain uncurable, and a lot of people are going to go untreated – whether that’s because of a lack of trust, poor economic and financial incentives, limited public health ambition, and policy. In one sense, we’re skeptical that AI can solve medicine on its own. But in another, there are many areas where we think AI can help. So the episode also functions as a roadmap to speed up medical progress and scale up the delivery of lifesaving medicines – with AI and other approaches to reform the pipeline. What are the economic incentives, innovative trial designs, and data collection efforts that can help drive further medical progress? And how does AI fit in? You’ll have to listen to find out! Timestamps: 0:04:34 Contrasting AI optimism and skepticism 0:32:44 The non-linear path between science and technology 1:01:30 The fundamental need for experiments 1:23:15 Animals, organoids, and virtual cells 1:50:47 The challenges of collecting drug efficacy data in humans 2:34:02 The long road to drug safety data 3:06:09 The cost problem of delivering biological drugs and personalized medicine at scale 3:45:35 The global skew in R&D and healthcare funding 4:01:48 Trust, ambition, and the final barriers to medical progress

Saloni

221,350 просмотров • 10 месяцев назад