Jon Chee

In case you missed it, last week our friends Aclid posted this article about the use of red teams in biosecurity. As my followers might know, at Messaginglab, we have extensive experience working with DNA/RNA synthesis providers, the backbone of the bioeconomy. The ability to ensure safety when ordering and delivering genes is key, and redteaming is one of the ways the industry will stay safe. This article is well worth the read.

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NY Illumina/GRAIL lawsuit Final Part: Summation "Illumina’s directors and officers, foreseeing their non-exculpated liability in connection with their decision to close the Acquisition in spite of the EC standstill order, determined to shift that liability to Illumina at a cost of up to $100 million, made that shifting of liability a requirement before they would approve the Acquisition, and at the same time abandoned $150 million in insurance for the Company that could have funded Illumina’s financial losses associated with the Acquisition. At no time was this approved by a disinterested vote of disinterested directors or stockholders, and it was not fair to Illumina. By stripping away the Company’s insurance down to $0, while at the same doubling their D&O insurance to $300 million, Illumina’s directors and officers abandoned their fiduciary duties to the Company and left Illumina unprotected while protecting themselves at Illumina’s expense." "Prior to acquiring GRAIL, Illumina’s financials were strong, and its share price reflected that as it traded at its closing peak of $524.24 on August 16,2021. However, after the Acquisition, GRAIL acted like a millstone around Illumina’s neck, costing billions of dollars to maintain while providing no benefit to Illumina." "The Acquisition ultimately dropped Illumina’s cash balance to zero. On March 8, 2021, Illumina entered into a credit agreement with Bank of America for $750 million to help pay for the Acquisition and to keep the lights on at Illumina. When that proved insufficient to carry Illumina’s rising costs, on March 16, 2021, Illumina issued another $1 billion in notes. Less than two years later, on January 5, 2023, Illumina deemed it necessary to sign another $750 million credit agreement to “finance the working capital needs, and for general corporate or other lawful purposes, of Illumina and its subsidiaries.” Illumina has been unable to seek any other potential acquisition opportunities as it spent the bulk of its cash balances on the Acquisition." "By reason of the Director Defendants’ positions with the Company as directors, they owed the Company and its stockholders a duty of loyalty. deSouza and Dadswell owed the same duty of loyalty as Illumina officers. By closing the Acquisition, Defendants breached that duty in three ways: causing a knowing violation of the law, consciously disregarding their fiduciary duties and engaging in self-dealing."

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Cloud lab update! open source methods for DNA manipulation in the cloud have just dropped 🧬🤖👩‍🔬 https://lnkd.in/eHFmjX2Y Align to Innovate The 2022 Bioautomation Challenge has come to a close. The overall conclusion? Cloud labs are promising, but there is a long way to go before they become embedded in scientific practice! The Challenge was the world’s first cloud lab grant program, which offered academic labs and individual contributors access to Emerald Cloud Lab. In 2021, Align awarded 9 teams with access to the cloud We quickly realized it was going to be a wild ride Giving someone access to a cloud lab is like giving someone in 1970 access to a 2024 H100 cluster — mesmerizing, but perhaps premature. Without the cloud lab equivalent of C, C++, Python, CUDA, PyTorch, etc, there's not much infrastructure to support doing useful things! New users struggled to do cloud science. The barrier to entry is enormous. There was virtually no example code to look at. Users needed a lot of training to become proficient, but training materials didn’t exist. To accelerate training and develop example code, we created an internal team at Align to support academic users. Unlike Challenge participants, this team included folks who were already over the learning curve with years of experience programming in Emerald language Props to Align’s internal team - TJ Brunette Waseem Vali Amanda Kohler, PhD Michael Crone Dana Cortade - who got the first body of cloud lab code on github, including a complete set of methods for manipulating DNA! Check it out here: https://lnkd.in/eHFmjX2Y Ever wondered what Emerald Cloud Lab code is like? Now you can see if for yourself through our github https://lnkd.in/eXZDi6Z2 and screencast walkthroughs of primary methods like this one: https://lnkd.in/e85HBCGv Meanwhile, the quest for reproducible, sharable and scalable life science research continues! Align to Innovate has expanded and now works with many more automation facilities like the DAMP Lab, National Institute of Standards and Technology (NIST), and Ginkgo Bioworks, Inc.! More details on some of these exciting collaborations coming soon! 🫶 🤗 We're also doubling down on programs like the Protein Engineering Tournament. If you're interested in working with Align to Innovate we're recruiting for several roles in data, protein engineering, and machine learning! https://lnkd.in/eqNJMv2V Props to everyone who participated in the Challenge! Shout-out to Chase Armer and William Greenleaf for some exciting science that I hope to see published soon! And thanks to Emerald Cloud Lab for working with us 🙏

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This article is inspired by comments from Tony Czarnik and Stefan Ivanov regarding my previous blog post at https://lnkd.in/gFPhXfKW. To clarify, I am writing from the perspective of an AI believer or practitioner, acknowledging the critical viewpoint that claims "AI has shown little in terms of actual achievements in drug discovery." However, I believe it is not sufficient to simply agree with this stance without further elaboration. My focus here is on higher-level concepts rather than practical examples. Here are three key takeaways: 1. Need for Comprehensive Data Collection The current drug discovery process often lacks systematic documentation and relies heavily on subjective reporting, which hampers the ability to draw robust conclusions. Scientists, particularly chemists and biologists, should prioritize automating the collection of extensive data and metadata to create a rich database for analysis. 2. Integration of Mathematical and Data-Driven Approaches Two primary strategies for improving drug discovery are mathematical modeling and data-driven methods. While traditional scientific practices lean toward mathematical modeling for deeper understanding, advancements in AI highlight the effectiveness of data-driven approaches in addressing existing challenges and providing new insights into complex biological processes. 3. Transformative Role of AI AI technology plays a crucial role in drug discovery by enabling scientists to embrace data-driven methodologies that enhance problem-solving capabilities and deepen understanding of biological systems. Combining both mathematical models and data-driven techniques can lead to a more robust framework for innovation in the pharmaceutical industry.

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If you're a gene synthesis provider, screening is a competitive advantage. Aclid just highlighted recent legislation that mandates screening certifications for any vendor working with US govt agencies, academic institutes, and federal contractors. At the recent Ginkgo Bioworks, Inc. Ferment conference, several panelists mentioned the White House is more concerned about genetic data than any other kind. To gain the competitive advantage, take a read...

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Enhanced3D Genomics has a proprietary GenLink3D platform which uses capture Hi-C technologies to profile this 3D genome folding at high resolution for all genes and their enhancers simultaneously. The platform thus links gene enhancers and non-coding genetic variants to their target genes, unlocking their potential for therapeutic discovery Genetically supported drug targets are twice as likely to succeed in clinical trials (Sanseau et al., 2015). https://lnkd.in/ebuV89cE #enhanced3Dgenomics

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Using 'representation vectors' instead of prompt engineering to modulate #LLM responses enables tuneable behavior: https://lnkd.in/eUwSUvtg

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[NEW] Database of Globally Approved Regenerative Medicine Products - https://lnkd.in/gXk9x2NA Today in 2024, there are a total of 78 globally approved regenerative medicine (RM) products that include: 1. Cell-Based Immunotherapies (14 approvals) 2. Gene Therapies (17 approvals) 3. Cell Therapies (21 approvals) 4. Cord Blood Products (8 approvals) 5. Tissue-Engineered Products (18 approvals) This product reveals the identities of these products, breaking them down into the five product categories shown above. At this time, tissue-engineered products and gene therapies are leading the regenerative medicine (RM) field with the greatest number of product approvals at 18 and 17 approvals, respectively. If you’re working within the regenerative medicine (RM) field, then you need to be in the know about these historic product approvals. It is critical that you are aware of the specific products that have achieved regulatory approval for use in human patients, as well as the identities of the companies who are commercializing them, the date of their approvals, the regulatory bodies who have approved them for use in human patients, and the conditions they can treat. #regenerativemedicine #immunotherapies  #celltherapies #genetherapies #cordblood #tissueengineering

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How does the BioInnovation Institute measure impact? When I started 5 years ago we quickly decided that measuring how much financing our companies raised was the right KPI - it was hard, measurable, and integrated all the effects we wanted to make on the start-ups. (Plus we had a board meeting the next day and we could pull it together pretty quick). We’ve had 5 years to reflect on that choice , and it seems insufficient now. Our startups are advancing to clinical trials (Breye Therapeutics ApS Kariya Pharmaceuticals ) and selling sustainable products (Matr Foods EvodiaBio Sundew ). Most recently BOOST Pharma announced positive clinical data in Brittle Bone Disease showing reduced bone fractures in children. Financing raised showed what our programs could do for start-ups, but its the products they create that are going to improve Human and Planetary Health - and that is the impact we wish to measure and report. The Danish newspaper Børsen featured our CEO Jens Nielsen today and our thoughts on how BII thinks about impact 5 years into this unprecedented experiment. Maybe one day we can tell you about approved medicines, lives saved , and CO2 and water spared ? Stay tuned…. Link below

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A recent publication by Merck demonstrates the effectiveness of benchtop quantitative NMR (qNMR) as a reliable and user-friendly method for quantifying nonionic surfactant excipients (NISEs) in biologics formulations. Traditionally, NMR has been a complex characterization technique, challenging to transfer to a QC lab. However, benchtop instruments are changing the game, paving the way for routine use of NMR in specific applications. 🎯 𝐊𝐞𝐲 𝐭𝐚𝐤𝐞-𝐚𝐰𝐚𝐲𝐬: • 🧪 Benchtop qNMR can accurately quantify polysorbates 20 and 80 (PS20, PS80) and poloxamer 188 (P188) in biologics formulations. • 🌿 This method is non-destructive and generates no solvent waste, making it a greener alternative to traditional liquid chromatography (LC) methods. • 🔍 The technique proves effective under biopharmaceutically relevant conditions, even at low concentrations (≤0.025% w/v). • 🏭 It offers potential for routine integration within analytical and QC laboratories across the pharmaceutical development and manufacturing sectors. For a deeper dive into this work, check out the full publication here: 🔗 https://lnkd.in/eHyFQfKE #biopharmaceuticals #NMR #analyticalchemistry #drugdevelopment #pharma #sustainability Ciarán C. Lynch, Ph.D., Gennady Khirich and Ryan T. Lee

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Global Database of Regenerative Medicine (RM) Industry Investors, 2024 – Featuring 300+ Investors - https://lnkd.in/g8f6k5h #Regenerative medicine companies are defined as those involved with cell therapies, gene therapies, exosomes, tissue engineering, biomaterials, or other related therapies involving the use of human cells and tissues. Gene therapy is the use of genetic material to change the expression of a gene product, while cell therapies utilize living cells to create either direct or indirect effects within the human body. Exosomes exert their effects by carrying cell-specific cargos which are selectively taken up by recipient cells. Likewise, there are technologies that can impact the health of tissues and organs through the use of biomaterials, tissue engineering, and 3D bioprinting. To develop these novel regenerative medicine (RM) and advanced therapy (AT) products, investor capital is often needed to support these therapeutics from preclinical development through clinical trials, and ultimately, to commercialization. Regenerative medicine industry investors include: Venture Capital Groups Private Equity Firms Hedge Funds Investment Firms Endowment Funds Angel Investors (for example, Tony Robbins, Bill Maris, Peter Diamandis and others) This database reveals the identity and investment behavior of over 300 investors who specialize in companies commercializing regenerative medicine (RM) and advanced therapy (AT) products. If you are in the process of raising investor capital or are considering it, this database is an essential resource.

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Interesting technical overview from Ginkgo Bioworks, Inc. that might be the first signs of a strategic pivot: they trained an LLM on 2 billion proteomics sequences. "Here we introduce AA-0, a 650M parameter model following the ESM-2 architecture, trained on public data combined with proprietary sequences from the UMDB. We compare the performance of AA-0 to ESM-2 on popular benchmarks as well as a collection of internal benchmarks relevant to our commercial work in the Ginkgo Bioworks foundry." "AI can make biology easier to engineer. This is the first of many intended releases from the Ginkgo AI team. We are excited to begin peeling back the curtain and enabling bioengineers across the world to access our technologies. As we scale up our training efforts (we are currently training models 10x larger than these and more!), we will be eager to share our findings and plan to make the resultant models available to the community."

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Neutralising antibodies play a vital role in the fight against SARS-CoV-2, the virus responsible for COVID-19. These antibodies are produced by the immune system in response to infection or vaccination and are crucial for preventing the virus from entering cells and replicating. One of the primary values of neutralising antibodies lies in their ability to directly inhibit viral infections. By binding to the virus's spike protein, these antibodies block its interaction with the ACE2 receptor on human cells, thereby preventing the virus from gaining entry and reducing the likelihood of infection. This mechanism not only protects individuals from severe disease but also contributes to reducing viral transmission within communities. Furthermore, neutralising antibodies have significant implications for vaccine development and efficacy. Vaccines that elicit a robust neutralising antibody response are generally more effective in preventing infection and disease. Monitoring the levels and potency of these antibodies in vaccinated individuals aids researchers in understanding vaccine durability and the need for booster shots. In therapeutic contexts, neutralising antibodies can be engineered and administered as treatments for COVID-19, providing immediate protection for high-risk populations. Overall, the identification and enhancement of neutralising antibodies remain essential in combating the pandemic and developing long-term strategies for managing SARS-CoV-2. Learn more at https://lnkd.in/gDcpyyVe

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By leveraging AI with vast genetic databases, we are not only quickening the pace of identifying potential drug targets but also enhancing the accuracy of clinical diagnostics and predictive modeling. Recently, I had the privilege of discussing the profound impact AI is having on pharmaceutical R&D with Kim Branson, GSK's SVP Global Head of Artificial Intelligence and Machine Learning. Listen on Raising Health: https://lnkd.in/g6QUm2-A

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One of the most common misunderstandings in biotech today: the "best" AI automatically delivers the best results.  At DeepMirror, we've discovered a different truth. So many AI tools in biopharma, despite their potential, struggle to find their way into regular use.  Why?  Because having the most advanced algorithms or the largest training datasets doesn't mean that a software is usable. No matter how well a tool performs in theory, if it’s too complex for the end user, it’ll be abandoned.  Our focus is on building AI that scientists can actually use, day in and day out. We prioritize usability, speed, and intuitive design just as much as raw computational power.  As one of our customers put it, "This is the simplest AI software we use." And that's exactly the point. So, when you’re evaluating AI tools, instead of asking, “what makes your AI better than anyone else?”, we think a better question is: “what makes your AI easier to use?”. In the end most AI is like water: The same in every bottle. We've written a brief piece exploring this here: https://lnkd.in/ezkfu3cy Would love to hear your take on this! 

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InduPro raised an $85M series A (https://lnkd.in/gnPMq2pP). Exciting to see protein sequencing + AI enabling the discovery of new protein-led cancer treatments. "Our MInt platform leverages deep learning analysis of proprietary protein microenvironment and membrane proteomic data to define the architecture of the cell surface proteome across different target classes and indications. This allows us to identify novel targets and co-target pairs for selective tumor targeting (bispecifc ADCs and TCEs) or immunological synapse modulation (bi/multispecific mAbs)."

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Optimization of the solvent system used to prepare cannabis inflorescence samples for cannabidiol and Δ9-tetrahydrocannabinol quantification using D-optimal design. Innovative Solvent System Enhances Cannabis Analysis Researchers have developed a groundbreaking solvent system for preparing cannabis inflorescence samples, offering a safer and more efficient alternative to the commonly used methanol:chloroform mixture. This new method significantly improves the extraction of key cannabinoids, CBD and Δ9-THC, using a less toxic solvent mix of acetonitrile, methanol, and water. Key Findings: The optimal solvent ratio (acetonitrile:methanol:water) is 0.511:0.289:0.200. Enhanced extraction efficiency compared to traditional methods. Stability of cannabinoids in the new solvent is superior, maintaining integrity at 4 °C and −20 °C for 28 days. Impact: The study presents a solvent system that not only prioritizes health safety but also maximizes cannabinoid extraction from cannabis cultivars, Hang Kra Rog, Phu Phan and Charlotte’s Angel. This advancement could revolutionize the standard procedure for cannabinoid content analysis. Another great study out of Thailand made possible due to the Decriminalizing / Delisting of Cannabis. #Thailand #ThaiCultivars #CannabisResearch #CannabinoidExtraction #InnovationInScience #HealthSafety #AnalyticalChemistry

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The Role of AI in a Manufacturers Plan-Do-Check-Act (PDCA) Cycle. Found an interesting article that discusses the role of AI in the plan-do-check-act (PDCA) cycle The article discusses that: "Technology is a tool and can be beneficial, but it has to be used to solve problems and be part of the process instead of being the process. Technology, Whitley said, has to help turn the wheel faster in the plan-do-check-act (PDCA) cycle. Manufacturers need to improve visibility, retain workers, increase onboarding speed for workers and make sure the data gets to the right person at the right time. Artificial intelligence (AI) can help the connected worker because it can gather all the data and synthesize it into something workers can evaluate. Many forward-thinking manufacturers are already looking to using AI to improve their manufacturing and maintenance processes. Even with that optimism, it still comes down to using AI the right way and finding ways to improve throughput and performance." See: https://lnkd.in/gCTs_iZm #AI #artificialintelligence #qualitycontrol #PDCA #AIandManufacturing #connectedworker #digitalmanufacturing #manufacturingprocesses #manufacturing #manufacturingquality #manufacturingprocesses #manufacturingsystems #continuousimprovement

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