Daniel Dacey

The Silent Tutors: How LLMs Are Bridging Knowledge Gaps in Cell Therapy Manufacturing

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Grant revenue is not revenue. I'm all for non-dilutive funding, if and only if the SBIR/STTR-funded project accelerates your strategy with development of a product that will be sold (so science fair projects). However... *grant revenue is not validation dollars on the top line.* What you have shown is an ability to write grants, the basis for the research that led to the startup (in the case of academic spin-outs) in the first place. What you have not shown is 💸 the desire for customers to purchase your product(s) ➡ an ability to articulate a go-to-market plan, or 🔐 a down in the trenches understanding of product-market fit. Startups are a game of the quick and dead -- take the time and money you would spend writing these grants early in the life of your company and hit the road, listen to your customers, learn where and how your products fit, and start selling. Your P&L shows me your real progress there -- As an investor and advisor, I would rather see $10,000 in revenue from sales from a handful of customers in 2 quarters than a X% chance you get a $1M grant in 9 months.

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I'm delighted to share news of this new AI-focused drug discovery company, Xaira, built on foundational technology from David Baker and led by Marc Tessier-Lavigne. The company applies AI tools to unlock opportunities in drug discovery for aspects of biology that we believe are not readily obtainable with conventional methods. I co-led NEA's investment in Xaira, along with my NEA partner and the firm’s co-CEO Mohamad Makhzoumi, and I will be joining Xaira’s board. Our enthusiasm for Xaira based on our belief that applying AI and large language models to biology holds immense promise and is still in its early stages. Currently, the application of AI to biology relies on model architectures borrowed from the tech world. A lack of well-curated clinical training sets hinders building and training these models effectively. This limits the advancement of AI in solving biological problems compared to other fields. Xaira aims to bridge this gap. We now have a clear understanding of the data needed to answer fundamental biological questions. By iterating on leading life science models and using focused perturbation data generated through our large-scale experiments, Xaira will generate data specifically suited to building advanced model architectures that are optimized for biology -- and hopefully, unlock therapeutic opportunities for patients who are not being optimally served by existing therapeutics.

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🤖 𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 (𝐀𝐈) 𝐢𝐬 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐢𝐧𝐠 𝐛𝐢𝐨/𝐩𝐡𝐚𝐫𝐦𝐚 𝐭𝐞𝐜𝐡 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 What percent of bio/pharma companies are emphasizing AI in their tech development? We attempted to answer this question using our biopharmIQ corporate intelligence platform* 𝐑𝐞𝐬𝐮𝐥𝐭𝐬 ~9% of the biopharmIQ companies on our platform emphasize AI in their tech 𝐂𝐨𝐦𝐩𝐚𝐧𝐲 𝐓𝐲𝐩𝐞 ⇒ Not surprising, computational biology (~39%) companies lead the way ⇒ Diagnostics companies (~25%) are beating bio/pharma and their vendors 𝐂𝐨𝐮𝐧𝐭𝐫𝐲 ⇒ From a country perspective, Israel (17%) is leading the way followed by the UK (~12%) 𝐒𝐭𝐚𝐭𝐞 ⇒ From a state perspective in the U.S., Washington leads the way (~13%) ⇒ On the other end, only ~5% of the bio/pharma companies from New Jersey emphasize AI for their tech 𝐌𝐞𝐭𝐡𝐨𝐝 *We used AI to upgrade the technology descriptions in our biopharmIQ database, and then analyzed those descriptions to determine the current prevalence of AI in bio/pharma tech 𝐋𝐞𝐚𝐫𝐧 𝐌𝐨𝐫𝐞 🤔 Want to learn more? Want to search our ~10,000 bio/pharma tech descriptions yourself? https://lnkd.in/g_QTJQQK

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There is no one right path for financing a biotech startup. Venture capital isn’t right for everyone. Even if that’s what all your peers are doing. I talk about this a lot because I want people to go into these fundamental decisions about their businesses with eyes wide open. VC is a very specific tool with a very specific purpose. It’s important to make sure you’re aligned with the best application of that tool. Just like you wouldn’t use a backhoe to plant a tulip or a spoon to dig a well… It’s important to understand who you are in that equation, so you pick the right tool for the job. VC is a great tool for the right founders. And it’s amazing for biotech founders to have the option, where just 20 years ago, it was almost unheard of to raise venture funds for life sciences. But I’d love to see more conversation about when, how, and for whom that is. Any thoughts? #biotechnology #lifesciences #finance

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'Asked how they want to see AI implemented in medical education, respondents provided a road map for how technology may help busy HCPs stay up to date. The most commonly requested use of AI, picked by 23% of respondents, was in the creation of summaries of educational videos. AI-generated summaries of journal articles were a popular choice, too, ranking third on the list of most requested features.' This is great idea, however we are getting in to the deep waters of advertising and the contents that could be seen as inappropriate direct advertising from the industry to healthcare professionals. We should not forget that AI will not be able to distinguish which content is appropriate and which is not. #AI #education #healthcare

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I wonder if change in a mature industry is incremental or disruptive? I've been on the road last 3 weeks. One of my realizations is that the pharma/biotech industry is having a really hard time understanding the implications of AI in their field. Our conversations are centered on how AI might augment our old way of doing things. Not so much on will there be newer ways of doing things? Biological research is a complex balancing exercise between mechanistic understanding and pure empirical evidence. We are progressively better at understanding - or as I would say hypothesising - about how a disease works and how a drug might 'cure' it. A submission of mechanism of action is more common in IND applications but as far as I can tell it's not a pre-requisite. The greatest successes of drug research are solid empirical hit and trial. Very successful ones at that. The hay day of drug research in the 60s to 90s was because big pharma figured out how to do compound screening effectively at scale. Will AI give us new ways to do this in more effective ways? I would be extremely surprised if that were not to be the case. a. we have never had access to the kind of data that we do now b. we have never had a tool like modern AI to sift through data Those two combined will give us ways to do empirical research like we haven't done before. That much is clear. Will it change the relative dearth of blockbuster drugs? That is a whole another subject. What is clear to me is that the current focus on mechanistic understanding will have to evolve to incorporate AI. We will have newer ways to do things - not necessarily change or abandon old ways. Link to a blog that Abhishek wrote in 2020 which seems truer than ever before.

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What are Psychedelics Investors Concerned About? In general, psychedelics investors are very concerned about regulatory and legislative barriers, with respondents of all stripes expressing this as the most salient area of uncertainty for the field. Retail investors continue to demonstrate sensitivity to company-level catalysts and drug development milestones, with trial timelines and outcomes a key concern among this cohort. Larger investors, meanwhile, were more likely to express concerns over longer-term considerations like post-approval commercialisation factors or general commercial viability, as well as issues pertaining to safety and ethics. That latter category, safety and ethics, is a new one that emerged in this latest series of the survey. Here, we take a deeper dive into these themes…

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Over the past several years, I have worked with and written extensively about #CRO, #CDMO, and CMO companies. As a result, I have been giving a lot of thought to the relationship between contract organizations and the #biotech, #pharma, and device companies that hire them. There's a lot to consider when hiring an external partner for research or critical steps toward commercialization, especially with newer technologies and modalities like cell and gene therapies. Given that, it can be especially tough for early-stage and small life science companies to navigate the myriad factors that influence contract partner selection. Nevertheless, the decision is of paramount importance to success. Fortunately, I recently came across a fantastic paper written by authors from several leading CDMOs. The paper specifically addresses critical considerations in contract organization selection for cell and gene therapy development programs. In this nicely written and tightly organized piece, the authors provide an excellent overview of the many details to consider during exploratory conversations and decision-making. Aside from the authors' consolidated list, it's clear that sophisticated soft and organizational skills are essential for developing a rock-solid RFP and drawing out critical information. Simply put, the better internally aligned and prepared your early-stage company is, the more likely the process will result in a good choice. Though focused on C&GT, many of these thoughts are transferable to other modalities, so it's worth reading even if you are not developing cell or gene therapies. Give it a read, and let me know what you think! (link in comments)

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You can’t “math your way through” people problems. For some, that’s probably one of the really disappointing truths about running a life sciences startup. Even though STEM can feel incredibly objective/quantitative, the common thread with every founder and executive I’ve spoken with on The Biotech Startups Podcast is that people problems are the hardest as they are subjective/qualitative. For some, that was a bit of a shock, because the expectation is that science is objective, scientists are rational, and we should just be able to focus on the work. Something Quin Wills at Ochre Bio said about this sticks out to me – I’m paraphrasing, but essentially: “There are people behind the science making decisions, and people are susceptible to fads and trends.” Even scientists. The implication being that you need to control for that by: – Getting the right people on the bus – Being crystal clear in your purpose and objectives – Intentionally setting culture – Doubling down on your EQ and soft skills That’s not always the stuff that comes naturally when you’ve risen the ranks relying primarily on your IQ and scientific brain. But if you can understand the importance of leadership skills and begin cultivating your competence there early on – you’re already ahead of the curve. Don’t sleep on those soft skills. #biotechnology #lifesciences #thebiotechstartupspodcast

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🚀 Bioinformatics Bootcamp Alert 🚀 Join us this week as we kick off our online bootcamp series on single cell data analysis! https://lnkd.in/giNWTtG8 In this 3-session learning series, you will: ✨ Get an overview of common ways to analyze single cell data ✨ Learn how to run differential gene expression analysis using Loupe Browser ✨ Find out how to use pipelines with a point-and-click interface via the 10x Genomics Cloud ✨ Learn from experts #Bioinformatics #SingleCell #Bootcamp #DataAnalysis #Science #LearningOpportunity

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#data #biology #biotech #food #ai #ML This is such an important issue! First, if you’re not already subscribed to Jesse's newsletter Jesse Johnson, you should be! It’s packed with extremely interesting and educational content, especially for #data and #R&D managers and users (which means all of us, right?). This specific post is incredibly relevant in our company too, especially with teams that aren't traditionally data-oriented. Our solution in my data team along with Omer Genkin? We co-create internal tools with our users, leveraging ML and other cutting-edge techniques to solve real day-to-day challenges. Our mission is to make these tools as intuitive and accessible as possible – with the simplest UI/UX (if any at all) and ensuring the insights are crystal clear. It's all about #empowering everyone, not just the data scientists, to harness the power of #data and #AI.

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A new company called Xaira Therapeutics just launched with over $1B+ billion in funding for using AI in drug discovery R&D. To give you some context, the last time we saw this level of fundraising was for Roivant Sciences, which focuses on creating companies around abandoned drugs, and Altos Labs, with $3B for cellular rejuvenation programming. But this is the 1st for an AI drug discovery R&D company. Interestingly, a significant portion of the company's technology originates from the Institute for Protein Design at the University of Washington, which is renowned for its work in protein science. This institute is led by Prof. David Baker, the co-founder of this and also the Charm Therapeutics. Read the full story here: https://lnkd.in/eCZfV4CW #drugdiscovery #artificialintelligence

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Why did my co-founders and I choose ‘Intrepid Labs’ as the name of our startup? Intrepid means adventurous and fearless - and we believe the technology we’ve developed at Intrepid Labs, utilizing AI and robotics in drug formulation, truly embodies this spirit. Excited to share our story with Jim Wilson of the NGBIdeas podcast and why JLABS is a great home for Intrepid Labs, as we push boundaries in using AI and robotics to optimize drug formulation for safer and better medicines. #AI #robotics #drugformulation #drugdevelopment #IntrepidLabs

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Surprisingly informative interview with Scott Gottlieb about the genAI drug development company Xaira announced yesterday with $1B of funding. (Most important: "Xaira" is pronounced "Zyra" 🙄 ). "What's different now is the use of large language models in the drug discovery and drug development process. You're seeing the large drug makers try to get instances of large language models within their own ecosystems so they can now load their own data on it and use it in a proprietary fashion." "Right now we're using models that are borrowed from the consumer tech world so they're not, they haven't been built to solve for human biology and we don't really have good clinical data training sets and that's the goal of the startup that we're funding is to try to build models that are purpose built for biology." "A lot of the data that we have to train models on...hasn't necessarily been collected in a fashion where it's optimized for training an artificial intelligence model, a large language model. So you might have proteomic or genomic data but it's not correlated with phenotypes." "It's also going to allow us if we can truly understand biology of different systems and different disease states, it might allow us to identify targets that we wouldn't have perceived otherwise. Using this model you can try to design antibodies intelligently that bind to protein targets that you might not have been able to get antibodies to bind to before because antibodies are the sum of small interactions and sometimes it's very hard to figure out how to design antibody to bind to certain targets." "Instead of borrowing some of the models that have been developed for more of a technological application you can actually develop models that are based on human biology and based on training sets that were built for the purpose of designing new models and that's happening right now."

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Make data immediately and efficiently available so folks can focus on fighting diseases instead of battling outdated systems. It is not okay to hinder biotechs from obtaining their clinical trial data in cumulative CSV or Excel files.  Imagine this: Hours go by, and you're at your desk painstakingly sifting through individual data points. Why? Because someone decided that sending cumulative data was too much to ask. No one should have to: 🔹 Request data files from a vendor contact who may not reply in a timely manner. One client experienced this when their primary contact was out for a week, and they were unaware. 🔹 Have to wait for a weekly or monthly file because that's what the data transfer agreement dictates. Another client could only access their data via PDFs, leaving no room for immediate data exploration or analysis. 🔹 Pay an extra cost for more real-time data updates/transfers. Access to all data should be immediate. #ClinicalTrials #Medicine #RealTimeData #Innovation

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“Drugs are substances that alter abnormal biological processes to treat disease and restore health.” This is from an interesting primer on drug discovery from Chamath Palihapitiya It got me thinking about what it means to restore health. For many (good) reasons, as drug discoverers/developers, we focus on reducing/removing by symptoms, whether in terms of the biology behind the disease, or the development program to test the compound/target. Does this restore health though? As I posted last week, stories of people living with chronic autoimmune diseases don’t suggest it does, and that’s probably true for other conditions also. What would we need to do to get to the situation where we can truly say we are in the business of restoring health? Disruptive biology would need disruptive development, regulation and commercial thinking. What do you think? How do we get from treating disease to restoring health?

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