Saloni
@salonium • 35,519 subscribers
Co-founder & editor @WorksInProgMag. Writer, Scientific Discovery. Podcaster, Hard Drugs. Advisor, @coeff_giving. // Prev @OurWorldInData. 🏳️🌈
Videos
New episode of HARD DRUGS! Should everyone be taking statins? Statins have revolutionised heart disease and they're one of many reasons for the long-term decline in cardiovascular mortality. Clinical trials suggest that the more you reduce LDL cholesterol levels, the more you reduce the risks of heart attacks and strokes, with no signal of harms even at the lowest levels. So scientists recommend the earlier they're taken the better, and the lower LDL levels you reach the better. Statins do have some rare side effects – such as muscle weakness and muscle loss in rare cases – but meta-analyses of RCTs find that most other side effects listed on statins' labels do not actually have higher risks than placebo. In this episode, Jacob Trefethen and I chat about all this and much more! - Why it took almost a century for scientists to come to consensus on "the lipid hypothesis": that higher levels of plasma LDL cholesterol causes higher risks of heart disease and that reducing it saves lives. - Drugs that effectively reduce cholesterol levels beyond the effect of statins, such as PCSK9 inhibitors, most of which are monoclonal antibodies, as well as newer siRNA drugs and the macrocyclic peptide enlicitide, which recently completed phase 3 trials, and Lp(a) drugs. - What the future holds for cholesterol drugs Timestamps: 0:00:00 Introduction 13:35 The decline in heart disease mortality 31:02 Surprising cholesterol trivia 55:40 The lipid hypothesis: 7 lines of evidence for the harms of LDL cholesterol 1:22:15 How cholesterol works 1:30:40 The discovery of statins 1:48:44 Should everyone be on statins? 1:57:10 PCSK9 drugs and beyond 2:22:56 Summary: how we got here and the future of cholesterol drugs Watch, listen, or read wherever you get your podcasts. Spotify: Apple: YouTube: Transcript:
Saloni734,864 views • 3 months ago
Guys, how do you invent a vaccine? Or wilder, how do you invent a vaccine during your PhD?! In a new episode of Hard Drugs, we talked to someone who did just that: Katharine Collins! A single malaria parasite that reaches your liver is enough to cause an infection. Worse, malaria has a complicated lifecycle with multiple stages, during which it changes shape and switches its surface proteins. And it’s co-evolved with humans for thousands of years, learning to evade and misdirect our immune system. That’s why it’s been so much harder to develop vaccines against than viruses or bacteria. But not impossible! In this episode, Jacob Trefethen and I are joined by Katharine Collins, who co-invented the second malaria vaccine, R21, during her PhD at the Jenner Institute in Oxford! After reading the expired patent of the first malaria vaccine (RTS,S), she stripped out the excess Hepatitis B surface antigen that RTS,S, leaving a particle with a much higher proportion of malaria antigen, used many newer processes, and paired it with a cheaper, more scalable adjuvant. The result is a vaccine that’s around a third of the price, easier to manufacture at scale, and may be more durable as well. It also means a vaccine that can reach far more children and save far more lives. Efficiency and scale matter enormously in the real world. It’s probably our coolest episode ever. You will learn lots of secret, behind the scenes information about how innovation really works. We chat about all this and much more! Timestamps: 00:00 Introduction 05:08 Our favourite parasites 10:12 How to invent a vaccine during your PhD 34:18 Why is it called the R21 vaccine? 37:32 Moving from the bench to hundreds of millions of doses 41:43 The vicious life cycle of malaria parasites 46:15 Malaria research IN MICE 53:03 The murderer in malaria research 55:51 Would you volunteer to get infected by malaria? 1:08:21 Why did the first malaria vaccine take so long? 1:18:26 Could we have had the vaccine sooner? 1:40:48 Vaccine versus vaccine: which one’s better? 1:46:53 If we did this again today, could we make better vaccines? 2:04:55 Conclusion and our reasons for pessimism and optimism
Saloni74,938 views • 18 days ago
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
Saloni109,256 views • 1 month ago
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
Saloni221,350 views • 7 months ago
LAUNCH DAY 🚀 Today I’m launching a new podcast, Hard Drugs, with Jacob Trefethen (Jacob Trefethen) Our first episode is about lenacapavir — a new HIV drug that blocks infections with an efficacy rate of nearly 100%, and which could completely change the fight against HIV worldwide. It’s also about the scientific journey leading up to this point, and how HIV was transformed from a lethal to a manageable condition. 3:52 How was HIV discovered? Where did it come from, and how does it attack the body and cause AIDS? 38:10 Antiretrovirals: How did scientists develop breakthrough HIV drugs — from azidothymidine to protease inhibitors to PrEP? 1:51:35 How does prevention and treatment work today? 2:19:03 HIV’s capsid and the breakthrough of lenacapavir, the first-approved HIV capsid inhibitor 2:50:36 How to develop long-lasting treatments 3:14:45 Lenacapavir’s near 100% efficacy in clinical trials 3:48:40 The impact of global programs against HIV, and can we now end HIV?
Saloni287,213 views • 1 year ago
NEW EPISODE of HARD DRUGS! AlphaFold, ProteinMPNN and other AI tools are transforming biology and drug design. But how do they work? What can’t they do (yet)? And can we use them to make a vaccine against Strep A for the very first time? In this episode, Jacob and I talk about hacking proteins with AI. 03:21 What makes proteins cool 05:29 Can we make a vaccine against Strep A? 10:12 Proteins in nature aren’t enough, and we can improve them with AI 21:12 ProteinMPNN and AlphaFold are transforming drug design 29:52 A short history of protein structure prediction from physical models to deep learning 34:48 Using AI to design a Strep A vaccine 39:27 Validating an AI-designed Strep A vaccine 43:09 Recent AI protein breakthroughs 49:30 Why we don’t already have a Strep A vaccine and the future of optimizing protein drugs
Saloni72,457 views • 8 months ago
Imagine you lived in the 18th century. Smallpox kills 1 in 3 cases. Yet you can’t culture pathogens, don’t know germ theory, and have no idea what a virus is. How would you invent a vaccine? In a new episode of HARD DRUGS, we trace the history of vaccines! Let’s start with your first clue: survivors rarely get smallpox again. But you don’t know why. The only technology available is variolation – infect your child with a controlled dose of smallpox, but risk a 1-in-50 chance they die from the procedure. Edward Jenner finds a clue from folk wisdom about milkmaids who remain protected from smallpox after being infected by cowpox – a mild disease whose pustules are easy to spot. It turns out the virus gives cross-protection. Jenner documents dozens of cases and an experiment, but his manuscript is rejected by the Royal Society. He self-publishes his research as a monograph, and continues experimenting. It works. The idea spreads widely, but early smallpox vaccines are fragile. They have to be kept alive by arm-to-arm transfer. Lineages keep going extinct; communities pay families to keep their children infected just so local lineages don’t die out. The arm-to-arm vaccine is also prone to spreading other pathogens along with it, like syphilis and hepatitis B. For almost 90 years, no one can replicate Jenner’s success for any other disease. It’s difficult to find another analogue to cowpox: few other mild, visible pathogens grant cross-immunity as conveniently as cowpox for smallpox. It’s only in the late 19th century that Pasteur and other scientists start developing methods to make new vaccines, like attenuation and inactivation. They conduct hundreds of experiments to develop new vaccines, and run live demonstrations to show their effects. Even so, Pasteur doesn’t know what a virus is. Without electron microscopy, he can’t see one. Without cell culture methods, his rabies vaccine relies on drilling holes in rabbits’ skulls and passaging the virus through their brains. Over the following decades, vaccinology advances – with the development of germ theory, Koch’s postulates, cell culture, and new microscopy. Eventually, scientists can identify microbes, distinguish strains, and mass-produce safe vaccines for diseases like tuberculosis, polio, and measles. They transform the world. In this episode, we walk through the centuries-long struggle to turn vaccines from lucky accidents into a science. Plus: we talk about the limits of Koch’s postulates, Pasteur’s secret notebooks, the chance events that saved millions of lives, and the bizarre experiments that made early vaccines possible. If you want to know why early vaccinology was so hard, listen to our episode! Timestamps: 0:08:12 Why variolation kinda sucked 0:22:51 Why early smallpox vaccines kept going extinct 0:34:23 The 90 year gap until the second vaccine 0:44:23 How to disprove spontaneous generation 0:56:10 Is Louis Pasteur overrated? 1:16:30 Koch’s postulates and why Robert Koch broke them 1:27:13 Petri dishes, agar, and the cultivation of microbes 1:44:56 Seeing viruses for the first time 1:50:35 The career change that led to polio and measles vaccines
Saloni37,270 views • 6 months ago
Hepatitis B is a tiny virus with just 4 genes. But it kills hundreds of thousands globally per year, mostly by infecting babies and causing liver disease or cancer over the following decades. In a new episode of HARD DRUGS, we tell the story of the hepatitis B vaccine, which became the first of many milestones: It was the first viral protein subunit vaccine, the first recombinant vaccine, and the first vaccine to prevent a type of cancer. Key stats: • Hepatitis B virus has just 4 genes, some of which overlap, making it one of the most genetically compact human pathogens • It has a very unusual life cycle for a DNA virus: it forms a stable mini-chromosome (cccDNA) inside liver cell nuclei, and uses reverse transcription, similar to HIV. • It integrates itself into your liver cells' DNA, causes repeated cycles of cell damage and repair, and inflammation that eventually leads to cancer. • During infection, the virus produces ~500 quadrillion (5 x 10¹⁷) copies of its surface antigen in the bloodstream. They act as decoys, soaking up our antibodies and helping the virus evade immunity, and it sticks around in our cells for decades. Over time, it causes one of the most common and deadliest liver diseases and cancers: ~300 million people worldwide are living with chronic hepatitis B ~600,000 people die from hepatitis B each year, mostly from cirrhosis and liver cancer While only ~5–10% of adults infected develop chronic infection, ~90% of infants infected at birth do. Around a third of those infants will eventually die from liver failure, cirrhosis, or liver cancer. But there's a vaccine against it: the hepatitis B vaccine. It is so efficient that it helps us block the virus by training us to recognize just one protein (the surface antigen) quickly, before it can overwhelm us. The first hepatitis B vaccine (1981) was made from human plasma, through multiple brutal inactivation steps that kill other microbes and contaminants. The second hepatitis B vaccine (1986) was the first recombinant DNA vaccine ever, made by factories of yeast cells churning out the antigen in bulk. The payoff has been enormous. Large cluster randomized trials have shown that hepatitis B vaccination reduces liver cancer rates by 85% and deaths by 70%. Universal vaccination of newborns has led to massive drops in hepatitis cases, liver failure, and liver cancer in younger generations. As the first viral protein subunit vaccine, it was built upon a series of breakthroughs in immunology, advances in virology and vaccinology. In this episode, we trace the discovery of hepatitis, hepatitis B virus, the development of vaccines, and their impact. And we explore how we even got to subunit vaccines at all: the great battle of immunology, the discovery of antibodies and their incredible diversity, and how that understanding could be used to test, study, and build better vaccines. If you want to understand how modern vaccines actually came to be, or why hepatitis B vaccination still matters today, this one’s for you. Timestamps: 0:00:00 Introducing the hepatitis B vaccine 0:15:46 The mysterious trail of jaundice outbreaks and the search for an invisible liver pathogen 0:28:03 How a tiny virus causes cirrhosis and liver cancer, and the struggle to identify it 0:53:19 How Maurice Hilleman developed the safest, purest vaccine in history 1:17:36 Turning the hep B vaccine recombinant 1:29:14 The impact of hep B vaccination 1:39:27 How we got here: the 19th century battle for the soul of immunology 2:01:34 How the body builds an infinite library of defenses 2:19:25 Why scientists thought immunology was solved in the 1960s 2:30:57 How better immunology led to precise subunit vaccines 2:45:33 Conclusion
Saloni32,424 views • 5 months ago
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