Chris Moore

In 2022 when Illumina and GRAIL leadership where touting how many lives would be saved if only the antitrust authorities got out of the way, here's what was really happening. Please read carefully, shows how this "life saving" test doesn't work: "[With the] San Francisco firefighters, the test did not identify the statistically expected number of cancers, provided false positives for half the cancers it did identify, and missed at least three cases of confirmed cancer. In other words, the exercise showed Galleri to be a disaster." "In this example prominently featured on GRAIL’s website and in marketing materials as an example of how “multi-cancer early detection screening can be critical for those at elevated cancer risk”—GRAIL provided 1,786 screenings to a group of San Francisco firefighters and their family members, a “high risk” group for certain kinds of cancer. During that testing round, which was conducted on December 6, 2022—Galleri generated six false positives and identified only five instances of cancer." "...such a result is alarming in light of the fact that, based on statistical experience, cancer should have been detected in about 29 (not five) of the 1,786 tests. Moreover, of the five instances of where cancer signals were identified, two individuals already knew they had cancer to begin with. Further, all of the instances of detected cancer were at a “late stage,” and where the cancer was too advanced to make a difference for the patients. One firefighter was diagnosed with stage 3 pancreatic cancer and is now in hospice, while another firefighter was diagnosed with lung cancer, which has now metastasized to the brain." "Galleri also missed at least six instances in which the San Francisco firefighters actually had cancer—failing to identify instances of melanoma, prostate cancer, and lymphoma that were identified by private doctors just months after the Galleri test indicated “no cancer signal.” "Further, in one instance in the San Francisco firefighters screening where Galleri generated a false positive, the patient underwent a painful bone marrow biopsy procedure to determine whether the patient did have cancer. In that case, the patient’s bone marrow biopsy procedure, clinically shown to involve “unbearable pain” to the “worst possible pain” a patient can experience, revealed that there was, in fact, no cancer." "The screenings provided to the San Francisco firefighters were an expensive “disappointment.” According to Tony Stefani, the organization’s founder, the screenings demonstrated that the probability of catching an early-stage cancer through the Galleri test was “negligible,” and the screenings only caught “some serious cancers at a later stage,” which likely would have been detected within one or two months without the test. Given the test’s high costs and useless real-world performance, Stefani stated that Galleri was a total “disappointment” and that the foundation had been sold a “bill of goods.” 🤯🤯🤯

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Why Gilead Sciences and Arcellx Anito-cel's data shows Promise in Multiple Myeloma Therapy Innovative Safety Profile: Early clinical data suggest anito-cel offers a reassuring safety advantage, with no reported cases of Parkinsonism, cranial nerve palsies, or Guillain-Barré syndrome—potentially addressing critical quality-of-life concerns. Encouraging Efficacy: With a 97% response rate and a median progression-free survival of 30.2 months in Phase 1, anito-cel demonstrates strong potential as an effective treatment option in heavily pretreated multiple myeloma patients. Cutting-Edge Technology: Anito-cel’s unique D-Domain binding technology may contribute to its promising safety and efficacy, reflecting innovation in CAR-T therapy design. #CellTherapy #MultipleMyeloma #InnovationInHealthcare #CAR_T #advancedtherapies #biotech #biotechnology #lifesciences #Data #ASH2024 https://lnkd.in/eCmJ_vP3

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Why do folks in drug discovery talk about Eroom's law and the decrease in NMEs/$ (or per $B to make the numbers sane) and try to compare that to the progress in semiconductors as exemplified by Moore's "law"? The proper comparison would be NMEs/$ to Chip fabs/$, not transistors /IC (or transistors/$ as Moore's law is often misdescribed). I'd say we're actually doing pretty well as an industry. Admittedly we're comparing apples to pineapples here but I don't think the ~30x cost to bring a drug to market over 5 decades is something people should be freaking out about and using to indict the industry for its supposed lack of productivity. Drug development is hard, slow, and expensive, and much of the low hanging fruit has been picked (apples, not pineapples, I guess) but it's still one of the most amazing and important endeavors undertaken by us humans. (Thanks ChatGPT for the handy table - take with a grain of salt)

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Can Polaris become the guiding star in this drug development #rAIvolution? A recent correspondence in Nature Portfolio's Nature Machine Intelligence, led by Cas Wognum of Valence Labs and Recursion, calls for "an industry-led initiative to critically assess machine learning for real-world drug discovery." This collaborative effort is both timely and crucial for our industry's future. Here's a free copy of the paper: https://lnkd.in/eNkQGqUR The authors, representing a consortium of ten leading biotech and pharmaceutical companies, argue that despite exciting innovations, we lack standardized benchmarks and evaluation methods tailored to drug discovery's unique challenges. This gap is delaying ML adoption and potentially misleading us about real-world impact. The fact that competitors are joining forces underscores the importance of this initiative. These include: Johnson & Johnson, Bayer, Novartis, Blueprint Medicines, Merck, Nimbus Therapeutics, Pfizer, AstraZeneca, and Relay Therapeutics. Why does this matter? The challenges we face in implementing ML for drug discovery are multifaceted: • Inconsistent datasets hinder progress • High-stakes decisions require reliable methods • Current benchmarks may not reflect real-world conditions • Interdisciplinary expertise is crucial but often siloed The proposed solution is an open-science, cross-industry initiative focusing on curating representative benchmark datasets, developing rigorous evaluation guidelines, and creating open-source tools to implement best practices. We have a responsibility to bridge the gap between perceived progress and actual impact. This Polaris - Benchmarks for methods that matter effort could become our guiding star in the vast sea of data and methodologies. By pooling our expertise and resources, we can establish a fixed point of reference to guide ML innovation in our field. The question now is: how can we all contribute to this initiative? What unique insights could each of us bring to the table? Our collective expertise can chart a more accurate course for innovation, potentially revolutionizing how we approach drug development and ultimately benefiting patients worldwide. Let's start a conversation about shaping the future of ML in drug discovery. #MachineLearning #DrugDevelopment #Innovation #Biotech #Collaboration Illustration EMxMJ

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Among the multitude of single-cell technologies, which one should you choose? A comprehensive benchmarking study by a team from Genentech can help make that choice easier. They used PBMCs to evaluate the performance of droplet, microwell, and combinatorial indexing-based methods from various companies that offer single-cell technology. 07/08/24 edit: please see response from Singleron by Harlin Jhyont below in the comment. “This study provides a comprehensive and systematic evaluation of commercially-available scRNA-seq kits and sheds light on the various strengths and weaknesses of each kit with respect to time, cost, functionality and performance, which should facilitate an optimal and informed selection of a suitable scRNA-seq approach.” “Overall, 10x FRP exhibited the highest ranked performance followed by 10x 3’, BD, Scale, 10x 5’ / Parse (tie), Fluent, Honeycomb, and Scipio. Singleron was excluded from the cell-based comparisons due to unreliable cell calling; however, based on UMI count, gene count, and usable mapped reads, it exhibited the lowest performance at the library level.” https://lnkd.in/eTk3jYVQ #singlecell #science

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"The 12-month results from the PARADIGM Phase IIb study are encouraging, showing slowing disease progression and improved survival outcomes. This new analysis highlights PrimeC's ability to regulate the iron panel in people living with ALS, underscoring the drug's target engagement. These findings strongly support the proceeding to phase 3 testing of PrimeC in ALS." #NeuroSenseTherapeutics #ALSTreatment #ClinicalTrial #BiomarkerData #HealthcareInnovation #ProudMoment NeuroSense Therapeutics https://lnkd.in/dzVT6QWJ

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Great scoop by Jared Whitlock on the Illumina/GRAIL SEC investigation. As I noted yesterday, increasing numbers of people being questioned by the SEC and obviously Jared has had some good chats. Jared is an actual journalist and gets everything verified from at least two sources, so you can be sure this is the real deal: "Endpoints News has learned that agency investigators have asked about whether forecasts by Grail’s management were too aggressive, and have asked about objections from inside the company by some employees who had reservations about the numbers." [more hints that the wall of silence is weakening, remember the GC just got his own counsel different to the board's? 🤔 ] This also reflects with the rumors I've heard (eek maybe they came from me 🤪) that the SEC is also looking into GRAIL's publicly stated FDA timelines which may be in direct contradiction with what the FDA had actually told GRAIL confidentially (something to the effect of "you guys are crazy if you think this shitty NHS data is going to get you IVD this decade"): "Last year, Grail reported $93.1 million in revenue, or about one-fifth of the conservative scenario in that same 2020 forecast. Grail had originally anticipated full FDA approval by now, a key milestone to set up insurance and government coverage for its pioneering cancer test." 👮👮👮

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What does computation-driven drug discovery look like from a peptide's perspective? Hint: unnatural amino acids are necessary. A 2 nm view of Atommap.

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Immune-Onc Therapeutics shared Phase 1b trial data on IO-202, our first-in-class anti-LILRB4 antibody, in combination with azacitidine for #CMML. The results are incredibly promising: ✔️ 50% Complete Remission (CR) Rate ✔️ 66.7% Overall Response Rate (ORR) ✔️ Exceptional outcomes in high LILRB4 expression patients (CR: 83%; ORR: 100%) As the CEO of Immune-Onc Therapeutics, I am inspired by the dedication of our team and grateful for the patients and clinical investigators who made this progress possible. These results bring us closer to transforming care for #CMML patients.

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Very excited to announce that in the upcoming ECI Single Use Technologies VII: Revolutionizing Manufacturing for Tomorrow’s Medicines conference, I will be chairing a session with Veronique Chotteau and Patrick Evrard. We will be chairing a session on Future Perspectives and Emerging Trends. The future perspectives and emerging trends of SUT in biologics manufacturing are poised to redefine efficiency, flexibility, and innovation in the industry. Looking ahead, advancements in materials will drive the development of novel SUT with enhanced performance, durability and biocompatibility including reduced leachable, as well as broader chemical resistance and better sustainability. - Advancements in SUT will support key challenges in the manufacturing of new modalities such as cell and gene therapy. - Novel concepts and designs as well as integration of monitoring, automation, robotics, data processing and digitalisation into SUT workflows will streamline manufacturing processes, optimize resources and enable process analytical technology (PAT) for greater process reliability and product quality.  - Integrated SUT platforms combining multiple operations will enhance productivity and reduce complexity. - Modular, customizable and scalable SUT platforms will facilitate agile development and manufacturing strategies, support seamless transition from small-scale to proof-of-concept and then commercial scale as well as ensure cost-effectiveness across different process sizes. The future of SUT in biologics manufacturing holds immense promise, driving industry-wide transformations towards more efficient, scalable, and adaptable manufacturing paradigms. We are inviting abstract submissions for the ECI Single Use Technologies VII: Revolutionizing Manufacturing for Tomorrow’s Medicines. Oral abstracts deadline: September 20th 2024 Poster abstracts deadline: December 17th 2024 Let us hear more about the advances in single-use technology that you are involved in. You can find out more about the conference here: https://lnkd.in/edHswRCU Don’t forget to sign up for the mailing list to receive the most up to date information. Andrea CME Rayat Nelly Montenay Alex DiBenedetto

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🚨 New Paper Alert! 📃 The teams at the University of North Carolina at Charlotte's CIPHER Center and ​Tuple, The Cloud Genomics Company just released a new preprint: "Large-Scale Computational Modeling of H5 Influenza Variants Against HA1-Neutralizing Antibodies". We computationally modeled 1,804 experiments containing various influenza H5N1 variants against existing HA1-neutralizing antibodies. Spoiler alert: Things are getting worse with more recent variants of the flu. 😲🦠 However, using scalable computational frameworks like what we've employed here, we can better model new variants to inform public health decisions *before* flu season hits. Give it a read: https://lnkd.in/eAhPxPYk Thank you to The GISAID Initiative for hosting the 18k isolates we used in this massive study! #computationalbiology #bioinformatics #genomics #genetics #datascience #ai #biophysics #influenza #antibody #virology 🐖

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FDA issues new guidance on how to use circulating tumor DNA (ctDNA) as a biomarker in cancer treatment. MRD mentioned 29 times 🚀🚀🚀

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🩸 Working with blood? Looking at peripheral responses? Join Andrew Warkman, Martin Karam, Ph.D. and me on April 30th to delve deeper into the utilization of blood fixation for single-cell analysis. "Unlocking the potential of whole blood for downstream single cell analysis: A guide to multiplexing and fixation." https://lnkd.in/g28CYHzs

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This cool new animation on www.pirche.com pretty much summarizes what #PIRCHE is all about. Donor HLA-derived peptides bound in recipient HLA (i.e. PIRCHE) are presented to naive recipient's T cells. Activation of such T cells elicits Tfh cells that support B cells to form a sound antibody response towards the donor antigen. Consequently it appears beneficial to transplant donor-recipient pairs with lower PIRCHE scores. #TransplantDiagnostics #NewWebsite #bioinformatics

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Nice in depth (and nostalgic) analysis of the new Illumina SmolSeq (🐱) by Keith Robison. Some really insightful points, most importantly IMO that SmolSeq is artfully positioned to create some competitive turbulence for Element Biosciences: "But now, $50K gets a very highly performing machine and $100K one which will fit many startup aspirations.  AVITI is still a sweet instrument, but Illumina just made that conversation more complicated." Also, why so shy about a DX version - "But even more surprising is that Illumina didn't outline a clear roadmap for clinical versions of the MiSeq i100 [🐱] - the DX model of the MiSeq received FDA clearance. The simpler workflows and smaller footprint would seem to make MiSeq i100 [🐱] a natural to try to put into pathology labs. So why the hesitancy to outline a path to get these into lucrative clinical markets?"

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CicrRNA 2.0. This paper from the Wang lab @ MIT finds that a branched mRNA cap effectively initiates translation on linear or circular #mRNAs without IRES. Two types of chemical modification, locked nucleic acid (LNA) N7-methylguanosine modifications on the cap and LNA + 5 × 2′ O-methyl on the 5′ untranslated region, enhance RNA–eukaryotic translation initiation factor (eIF4E–eIF4G) binding and RNA stability against decapping in vitro. Through multidimensional chemotopological engineering of dual-capped mRNA and capped circular RNA, we enhanced mRNA protein production by up to tenfold in vivo, resulting in 17-fold and 3.7-fold higher antibody production after prime and boost doses in a severe acute respiratory syndrome coronavirus 2 vaccine setting, respectively.  https://lnkd.in/eX2a9Tn3

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The evidence of suzetrigine in clinical trials has invigorated the field of analgesic development targeting NaV1.8. The development of novel inhibitors like our own LTG-001 and LTG-305, with their unique mechanism of action and binding site, holds promise for improved pain management with potentially fewer side effects. After 16 years of little innovation in pain management, it is an exciting time to be leading research and clinical development to determine the full potential and optimal clinical use of LTG-001, LTG-305 and NaV1.8 agents in addressing the significant unmet need for effective pain relief.   Read the recent feature for Nature Reviews Drug Discovery about the Nav-targeted pain pipeline.    (This article is behind a paywall and requires subscription)   https://lnkd.in/gR2-_TSp

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#Bcells | #Immunity | #Infection | "To #Affinity & Beyond" | First Release study from Michel Nussenzweig & Co. now previewing at Science Magazine | MAJOR NEW UNDERSTANDING of how "Best" #Antibodies are generated | #vaccines | Increased antibody affinity over time after vaccination, known as affinity maturation, is a prototypical feature of immune responses. Recent studies have shown that a diverse collection of B cells, producing antibodies with a wide spectrum of different affinities, are selected into the plasma cell (PC) pathway. How affinity-permissive selection enables PC affinity maturation remains unknown. Here, Andrew MacLean, Lachlan Deimel, et al found that PC precursors (prePC) expressing high affinity antibodies received higher levels of T follicular helper (Tfh)-derived help and divided at higher rates than their lower affinity counterparts once they left the GC. These findings indicate that differential cell division by selected prePCs accounts for how diverse precursors develop into a PC compartment that mediates serological affinity maturation. (Open access preprint: https://lnkd.in/eCAkb3ru) Celentyx Ltd #Bcells #drugdiscovery #CRO https://lnkd.in/eutbvMe7 Professor Nicholas Barnes PhD, FBPhS Omar Qureshi Catherine Brady

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I am looking forward to serving on a panel at the BISNOW International Life Sciences & Biotech Conference. The panel will discuss: IDENTIFYING FUTURE BIOTECH INDUSTRY TRENDS: AI, TECH, SUSTAINABILITY Future-Proofing Life Sciences Assets Rutgers life sciences research portfolio is the fastest growing part of our research enterprise. Rutgers University Rutgers Research Rutgers Innovation

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