How New Discoveries Influence Drug Development

𝐀𝐈 𝐢𝐬 𝐜𝐨𝐥𝐥𝐚𝐩𝐬𝐢𝐧𝐠 𝐭𝐢𝐦𝐞𝐥𝐢𝐧𝐞𝐬 𝐢𝐧 𝐦𝐞𝐝𝐢𝐜𝐢𝐧𝐞, 𝐚𝐧𝐝 𝐫𝐞𝐰𝐫𝐢𝐭𝐢𝐧𝐠 𝐭𝐡𝐞 𝐞𝐜𝐨𝐧𝐨𝐦𝐢𝐜𝐬 𝐨𝐟 𝐜𝐮𝐫𝐞𝐬 A few years ago, predicting a protein’s structure took months or even years. Then came AlphaFold, DeepMind’s Nobel-winning breakthrough, unlocking the ability to understand proteins and disease mechanisms at scale and speed. This led to Alphabet’s spin-off, 𝐈𝐬𝐨𝐦𝐨𝐫𝐩𝐡𝐢𝐜 𝐋𝐚𝐛𝐬, now using AI to design therapies with $600M in funding and its first human trials underway for cancer and immune disorders. ▫️ The speed of progress: Every six months, AI advances like a full human year. What once took decades now unfolds in quarters. ▫️ The cost collapse: AI is driving the cost of drug discovery and testing so low that researchers can now explore thousands of drug candidates and disease targets at once, including treatments that would have been dismissed as too niche or unprofitable just a few years ago. ▫️ The scale of exploration: AI has already helped identify or repurpose over 3,000 drugs currently in clinical trials. We’re already seeing the results. Northwestern researchers, for example, used AI-enhanced screening to repurpose 𝐩𝐢𝐩𝐞𝐫𝐚𝐜𝐢𝐥𝐥𝐢𝐧, a decades-old FDA-approved antibiotic, for Lyme disease. In mouse studies, it cured infection at one-hundredth the dose of standard treatment without harming gut microbiota. That breakthrough emerged in days, not years, at a fraction of traditional costs. 🔺 𝐖𝐞 𝐚𝐫𝐞 𝐰𝐢𝐭𝐧𝐞𝐬𝐬𝐢𝐧𝐠 𝐚 𝐟𝐮𝐧𝐝𝐚𝐦𝐞𝐧𝐭𝐚𝐥 𝐬𝐡𝐢𝐟𝐭. AI isn’t just speeding up medicine, it’s enabling exploration and validation at scales and price points previously unthinkable. And every six months, the landscape tilts even further. This is what leaders need to see: the weight of the opportunity, and the urgency to align their thinking to the pace of this change. #AI #Healthcare #DrugDiscovery #GenAI #FutureOfWork #mindsetchange Forbes Technology Council Gartner Peer Experiences InsightJam.com PEX Network Theia Institute VOCAL Council IgniteGTM IA FORUM 𝗡𝗼𝘁𝗶𝗰𝗲: The views within any of my posts, or newsletters are not those of my employer or the employers of any contributing experts. 𝗟𝗶𝗸𝗲 👍 this? feel free to reshare, repost, and join the conversation!

This weekend, I took some time to analyze the biggest #blockbusters in the #pharmaceutical industry in 2024. My goal? I was curious to determine what percentage of these top-earning drugs were developed internally by #pharma or originated elsewhere, via #biotech #acquisitions or #academic #licensing deals. I created a chart mapping the top blockbusters, their origin, #revenue, and #patent expiration dates. While the numbers are approximates from multiple sources the trend was clear: The vast majority of top-selling drugs were not discovered in-house by pharma companies. Even drugs set to lose exclusivity soon, meaning they were developed ~20 years ago, when pharma’s internal R&D spending was at its peak, mostly stemmed from academic #research or biotech M&A. Why does this matter now? When we think of #drug development, we think of pharmaceutical companies. But today, global pharma’s primary role is in clinical development and commercialization, not drug #discovery. This shift is evident; historically, large pharma followed a strategy closer to 70% internal development-30% acquisition. Now, ~28% of new drugs originate in-house, a dramatic reversal. If blockbusters still largely come from sourcing, even during high pharma R&D spending eras, what happens next as internal R&D budgets shrink and academic funding faces uncertainty? In a time where we are experiencing the dismantle of #academia either through the mass exodus of #scientists or through the pausing/reduction of #NIH #funding, it is important to remember that it is basic science that paved the way for the research and discovery of those drugs. Drug commercialization cannot exist unless basic research takes place. Till now, as shown in my analysis, the most financially and clinically successful drugs weren’t discovered with a specific application in mind. Instead, they emerged from fundamental scientific inquiry. The role of #GLP-1 as an #incretin hormone was uncovered in academia. Later, academic researchers studying venom peptides in the Gila monster found exendin-4, which led to #Byetta, the first long-acting GLP-1 agonist. Novo Nordisk, leveraging its expertise in #insulin analogs, then optimized it into #Ozempic (#semaglutide), now a major blockbuster in #diabetes and #obesity. #Keytruda, today’s #1 blockbuster, was an 'accident'. Biotech scientists were trying to stimulate, not block, PD1 in patients with #autoimmune disease (corrected*). Even after its #cancer potential was realized, the program nearly died, fighting for funding across 2 mergers before ending up at Merck. We are witnessing a transformation in biotech #leadership, with technical #PhD-trained #scientists transitioning into #CEO, #BD, and #VC roles, which I am personally excited about. But, this evolving landscape underscores the urgent need to sustain and modernize basic research while fostering biotech #entrepreneurship. Because without fundamental science, how will the next generation of novel therapies emerge?

Our industry is special. The long drug discovery and development cycles even accelerated using AI are incredibly long. Do you remember in 2019 we published a paper in Nature Biotechnology on Generative Tensorial Reinforcement Learning (GENTRL)? The study was actually done in 2018 and we set a speed record from generation of the molecule to PK in mice - it took almost a year in peer review. For generative AI in drug discovery it was a big milestone since most AI papers lacked experimental validation. It was covered by the the wonderful Alex Knapp of Forbes and but came under heavy criticism from some of the members the traditional drug discovery community working for competing companies. Back then, we made a decision to go after a novel target with a novel molecule for a disease with no cure. And today, Alex Knapp covered our big breakthrough - the release of topline data from the Phase IIa clinical trial in IPF where AI was used for every step of discovery and development. The trial was designed for safety but we observed unexpected efficacy. And not small efficacy - almost 100ml increase in force vital capacity (FVC) at high dose and now every pharma company suddenly got interested. Especially those that were previously skeptical about this target. Check out the link to the Forbes article in the comments and to the Nature Biotechnology paper describing the TNIK program until phase I complete.

The Biggest Winner in AI (So Far) Is… Imagine compressing six decades of scientific work into mere months. That’s exactly what AlphaFold has done for biology and drug discovery. There’s an old saying👉 There are decades where nothing happens, and then there are weeks where decades happen. This is one of those moments. The numbers blew my mind: 🔹 200 MILLION+ protein structures predicted—free and accessible to scientists worldwide. Until this happened ~100 were synthesized. 🔹 A process that once took 5+ years per protein can now be done in SECONDS 🔹 One-third of all human disease-related proteins have now been mapped with AlphaFold 🔹 $2.6 billion—the average cost of developing a new drug—could be slashed dramatically 🔹 In just one year, AlphaFold contributed to nearly 1,000 new research projects 🔬 What just happened? For decades, PhD-level researchers spent entire careers trying to determine the structure of a single protein—a fundamental step in designing new drugs, understanding diseases, and engineering life-saving treatments. Then came AlphaFold 3—the latest breakthrough from Google DeepMind and Isomorphic Labs. Unlike previous versions, AlphaFold 3 doesn’t just predict proteins. It now models protein-DNA, protein-RNA, and even small molecule interactions, unlocking new possibilities for drug discovery, synthetic biology, and precision medicine. 💡 What does this mean for businesses? ✅ Drug discovery time shrinks—what once took years can now happen in days ✅ R&D costs plummet—fewer failed experiments, faster breakthroughs ✅ New industries emerge—AI-powered biotech, personalized medicine, and beyond 🏭 Which industries are being transformed? Biotech & Pharmaceuticals – Faster cures, custom-designed proteins for medicine Healthcare – AI-powered precision treatments, next-gen vaccines Agriculture – Disease-resistant crops, sustainable food production Environmental Science – Biodegradable materials, pollution-cleaning enzymes 🚀 What does this mean for humanity? 🔹 Longevity unlocked—AlphaFold is paving the way for anti-aging breakthroughs 🔹 Curing the incurable—Diseases we once thought untreatable may soon have answers 🔹 A new medical revolution—tailor-made medicines for individuals, designed in days 💡 Did You Know? 🔬 AlphaFold’s impact was so revolutionary that Demis Hassabis and John Jumper of DeepMind won the 2024 Nobel Prize in Chemistry for their work on protein structure prediction. This isn’t just a scientific milestone. This is a new era for LIFE itself.