Weekly Tech+Bio Highlights #15
Weekly Tech+Bio Highlights #15
Roche Plays for Bigger Stakes in Digital Pathology; Fungal Mycelium Powers Robots; Gen-Bio Milestone by Google; Flexible Brain Probes; 3 startups building foundation AI for bio; and more...
Hi! I am Andrii Buvailo, and this is my weekly newsletter, ‘Where Tech Meets Bio,’ where I talk about technologies, breakthroughs, and great companies moving the biopharma and medtech industries forward.
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Now, let’s get to this week’s topics!
In Brief
🔬 Chai Discovery, backed by OpenAI and Thrive Capital, launched Chai-1, a new foundational AI model that predicts molecular structures and outperforms DeepMind's AlphaFold in certain benchmarks, accelerating drug discovery. The model is open-source for non-commercial use, allowing further research and development.
🔬 Gilead Sciences has inked a $35M deal with Genesis Therapeutics to use its GEMS AI platform for drug discovery on three undisclosed targets, with options to add more. Gilead gains development and commercialization rights, while Genesis is eligible for milestone payments and tiered royalties.
🔬 Insilico Medicine nominates ISM2196, a promising AI-developed WRN inhibitor targeting MSI-H tumors in metastatic cancers. Preclinical studies show potent anti-tumor efficacy, strong drug-like properties, and potential as a novel treatment for colorectal, endometrial, and gastric cancers.
💰 Cambridge University spinout CardiaTec has raised $6.5M in seed funding to use AI for developing cardiovascular disease treatments. By partnering with 65 hospitals, CardiaTec is building the largest multi-omics heart tissue dataset to accelerate drug discovery for the world’s leading cause of death.
💰 Superluminal Medicines raises $120M in Series A funding led by RA Capital Management, with participation from Insight Partners and others, to advance its AI-driven GPCR-targeted drug discovery platform that combines generative biology, machine learning, and proprietary data infrastructure.
💰 Human Longevity, Inc. raises $39.8M in an oversubscribed Series B round led by TVM Capital Healthcare to expand its Precision 100+ Longevity Care Program, which integrates AI-driven health risk assessments, whole-genome sequencing, advanced imaging, and personalized interventions to extend healthspan and reduce biological age.
🔬 Ginkgo Bioworks launches the Ginkgo Automation product line, offering flexible, modular tools like Reconfigurable Automation Carts (RACs) and Automation Control Software (ACS) to accelerate lab R&D with low-touch, data-rich solutions, now available globally
🔬 The Royal Marsden NHS Foundation Trust and Automata Technologies launch the UK’s first robotic genomic testing facility using Automata's LINQ platform, which automates sample processing with six robotic arms, doubling testing capacity for personalized cancer diagnostics.
💰 ThinkCyte secured $32M in Series C funding, bringing total funding to $91M to drive the global expansion of their AI-based VisionSort, a dual-mode fluorescence and morphometric cell sorting platform.
🔬 A 28-year-old man with acute pulmonary thromboembolism was successfully treated using an AI-guided device at Sir Gangaram Hospital, which precisely removes blood clots with minimal blood loss, avoiding risks associated with conventional treatments like severe bleeding and repeated interventions.
💰 CytoTronics secures $13.5M in Seed 2 funding led by LYFE Capital to accelerate the market entry of their Pixel™ live cell analysis platform, which offers single-cell resolution for various cell biology applications.
📉 Moderna faces investor backlash over its aggressive R&D spending of $4.5B annually amid declining Covid vaccine sales, with calls to trim its 45-product pipeline, especially in less promising areas like rare diseases and HIV.
💰 Segmed raises $10.4M Series A led by iGan Partners and Advocate Health to expand its AI-driven medical imaging platform, which de-identifies and standardizes diverse imaging data, enabling faster access for biopharma and AI research applications.
Spotlight
Fungal Mycelium Powers Robots in a Fusion of Nature and Technology
There is a groundbreaking advancement in biohybrid robotics, where researchers from Cornell University have integrated the mycelium of a king oyster mushroom (Pleurotus eryngii) into robots.
These robots respond to environmental stimuli, particularly light, by harnessing the electrical signals produced by the mushroom’s mycelium. The mycelium is used as a biological controller to activate the robots' movements, demonstrating a significant step in merging biological systems with artificial technology.
This field aims to combine biological materials (like fungi, cells, or insects) with synthetic components. The goal is to create systems that take advantage of biological computing, sensing, and response mechanisms, which are often more efficient than artificial systems.
The king oyster mushroom's mycelium produces small electrical signals that control the robot’s movements. The researchers successfully cultivated the mycelium in the robot's hardware, allowing it to respond to light stimuli. This concept is still in the early stages but has the potential for future applications, such as in agriculture or environmental monitoring.
The research hints at the potential for fungal-controlled robots to monitor soil chemistry in agriculture or assess the health of ecosystems. It also highlights the ongoing development of fungal computing devices, including self-healing robot skins that react to light and touch.
In the TROPION-Lung01 Phase III trial, the novel computational pathology-based TROP2 biomarker, using Quantitative Continuous Scoring (QCS), was shown to be predictive of clinical outcomes for patients with non-small cell lung cancer (NSCLC) treated with datopotamab deruxtecan.
This QCS platform, developed by AstraZeneca, accurately quantifies TROP2 expression within tumor cells using digitized images, addressing the limitations of traditional immunohistochemistry (IHC) methods.
Patients whose tumors exhibited high TROP2-QCS biomarker positivity (≥75% of cells showing a membrane ratio ≤0.56) demonstrated significantly improved progression-free survival (PFS) with datopotamab deruxtecan compared to docetaxel.
Notably, in nonsquamous NSCLC patients without actionable genomic alterations, datopotamab deruxtecan reduced the risk of disease progression or death by 48%, underscoring the biomarker's clinical utility.
New Generative Biology Milestone by Google
Google has just introduced AlphaProteo, a novel AI system developed to design high-strength protein binders for biological and health research.
Key Achievements:
AlphaProteo can generate protein binders for a wide range of critical biological targets, including cancer-related and inflammatory proteins such as VEGF-A, IL-17A, and the SARS-CoV-2 spike protein receptor-binding domain (SC2RBD). Its ability to create a binder for VEGF-A, an important protein involved in angiogenesis and diabetic complications, marks a breakthrough, as no previous AI system had successfully designed such a binder.
AlphaProteo outperforms existing methods, achieving up to 300 times better binding affinity than traditional design techniques. It shows an 88% success rate in experimental validation for the viral protein BHRF1, and its binders for proteins like TrkA are even stronger than those developed through multiple rounds of experimental optimization by other methods.
AlphaProteo’s algorithm rapidly generates candidate protein binders that, when tested experimentally, demonstrate strong and specific binding to target proteins. It has successfully designed binders for proteins involved in viral infections (e.g., SARS-CoV-2) and diseases like cancer and autoimmunity. These binders were validated in collaboration with experimental teams, such as those at the Francis Crick Institute.
AlphaProteo reduces the number of candidate designs required for testing by generating binders with higher success rates in initial rounds. Its in-silico predictions closely match experimental results, allowing researchers to bypass some of the laborious rounds of experimental optimization typical in traditional protein binder development.
However great, AlphaProteo is a work in progress with certain limitations and challenges still to be resolved.
For instance, AlphaProteo encountered challenges when attempting to design binders for certain highly complex targets, such as TNFɑ (tumor necrosis factor-alpha), which is associated with autoimmune diseases like rheumatoid arthritis. This failure highlights limitations in tackling difficult targets, which require further algorithmic refinement.
While AlphaProteo excels in generating high-affinity binders, achieving strong binding is only the first step toward developing practical applications. Challenges remain in downstream processes, such as ensuring stability, scalability, and functional integration into real-world systems for therapeutic or diagnostic use.
As protein design technology evolves, it is essential to consider the ethical and biosecurity implications. AlphaProteo is developed with a responsible framework in collaboration with external experts, including the Nuclear Threat Initiative (NTI), to ensure the safe use of this technology and mitigate potential risks.
R&D Corner
ETH Zurich’s Flexible Brain Probes: A Safer Twist in the BCI Journey
In the pursuit of safer, more adaptive solutions, researchers at ETH Zurich have introduced Ultra-Flexible Tentacle Electrodes (UFTEs), designed to dramatically minimize these inherent risks, offering a gentler and more responsive interface between mind and machine.
The UFTEs developed by ETH Zurich are designed to record brain activity with minimal tissue damage and high signal fidelity. These electrodes consist of ultra-thin fibers made from gold and polymers, with each fiber being just 7 µm wide and 2.4 µm thick. The UFTEs are mechanically coupled to a tungsten shuttle for precise insertion into the brain, and the use of a biodegradable silk coating temporarily holds the fibers together during the insertion process. This approach allows the UFTEs to be inserted into deep brain regions without causing detectable damage to surrounding tissues, even after several months of implantation. Read futher »
Princeton researchers have created a new method to physically manipulate DNA within living cells using light-activated droplets, the article published in Cell.
These droplets, which are liquid-like and formed inside the nucleus, can be precisely controlled by shining specific wavelengths of light. By attaching these droplets to specific locations on the DNA using modified CRISPR proteins, the researchers can move different regions of the genome closer together. The movement is driven by capillary forces—similar to how liquid droplets behave on surfaces—allowing fast and precise repositioning of genes.
This method doesn’t alter the DNA sequence but can affect gene expression by changing the physical arrangement of the chromosomes. Chromosomes, which are made of coiled DNA and proteins, have both elastic and fluid-like properties, and this method allows researchers to explore these properties in real time.
Unlike CRISPR, which cuts DNA, this tool focuses on the spatial organization of genes and could have therapeutic implications, particularly for diseases like cancer, where gene regulation is often disrupted.
What is Space Omics and Medical Atlas (SOMA)?
The Space Omics and Medical Atlas (SOMA) is a major project aimed at understanding how spaceflight affects astronauts at the molecular, cellular, and physiological levels. It collects and analyzes data from astronauts across various missions, like NASA’s Twins Study, the Japanese JAXA CFE study, and SpaceX’s Inspiration4 crew. SOMA significantly increases the amount of publicly available data on human biology in space, which is essential for tackling the unique health challenges astronauts face during and after space travel.
Spaceflight can cause several health issues, including weakened bones and muscles, eye problems, and a disrupted immune system. However, because there are few astronauts, it's hard to study these problems in detail. SOMA collects biological samples—like blood, skin, and urine—from astronauts before, during, and after missions. These samples are analyzed using cutting-edge techniques like genomics (studying DNA), transcriptomics (studying RNA), proteomics (studying proteins), and metabolomics (studying metabolites) to get a complete picture of how space affects the body.
Some key findings include changes in immune responses, increased cytokines (immune system signals), telomere (chromosome ends) lengthening, and shifts in gene activity. The study also compares short missions, like the Inspiration4 mission (just 3 days), with longer ones to see how quickly the body responds to space.
SOMA provides this data through publicly accessible platforms and a biobank at Cornell for future research. The goal is to use this information to improve health monitoring and develop countermeasures for longer space missions, like those planned for the Moon or Mars. This data also helps us understand how space-related stress on the body mirrors certain diseases and aging on Earth, which could lead to new treatments for those conditions.
Quick Hits:
BioAge Labs Files for IPO with Focus on Anti-Aging Therapeutics and Metabolic Diseases
Unearthing New GEMS: Genesis and Gilead Collaborate to Tackle Tough Drug Targets
MGI Introduces New CycloneSEQ Nanopore Sequencing Products
BIOCAPTIVA Secures US Patent for Advanced DNA-Extraction Technology in Cancer Management
Proscia Expands Concentriq Platform with Real-World Data to Enhance Precision Medicine
Companies to Watch
We have just updated our running list “19 Companies Pioneering AI Foundation Models in Pharma and Biotech” with three new arrivals:
Chai Discovery
It is, a six-month-old AI biology startup based in San Francisco, just announced the release of its first open-source model, Chai-1. The model is designed to predict the structure of biochemical molecules, a key capability in drug discovery.
The company, founded by former OpenAI and Meta researchers, recently raised nearly $30 million in a seed funding round led by Thrive Capital and OpenAI, valuing the company at $150 million. Chai Discovery is focused on using AI foundation models to transform biology from a science into an engineering discipline, with a particular emphasis on predicting and reprogramming molecular interactions.
Chai-1 is an advanced AI model that predicts the structures of various biochemical entities, such as proteins, small molecules, DNA, RNA, and even complex chemical modifications. What sets Chai-1 apart from other tools, like Google DeepMind’s AlphaFold, is its ability to achieve higher accuracy in predicting these structures, with improvements of 10% to 20% in success rates on key tasks related to drug discovery.
Helical
Helical, founded in 2023 and based in Luxembourg, raised €2.2 million in seed funding in June 2024 to build the first open-source platform dedicated to bio foundation models for DNA and RNA data. Led by co-founders Rick Schneider, Mathieu Klop, and Maxime Allard, the company aims to democratize access to advanced AI tools for pharmaceutical and biotech companies, helping them integrate these models into drug discovery processes without the need for specialized AI teams.
Helical focuses on creating a user-friendly interface for biologists and data scientists, allowing them to leverage complex genomic models through simple API calls. The platform includes a library of Bio AI Agents—pre-built applications tailored for tasks such as biomarker discovery and target prediction.
Unlike other platforms, Helical specifically integrates DNA and RNA foundation models, offering researchers the ability to work directly with nucleotide data rather than general AI inputs like text or images.
Helical’s model library and tools are open-source, facilitating collaboration and continuous improvement from the scientific community.
NOETIK
Noetik, founded in 2022 by Jacob Rinaldi and Ron Alfa in the San Francisco Bay Area, is using AI to tackle one of the toughest challenges in cancer treatment: finding the right targets and understanding how drugs will work on different patients. They just secured $40 million in Series A funding to expand their work, including growing one of the world’s largest cancer biology datasets and enhancing their in vivo CRISPR Perturb-Map platform, which helps them test and refine potential therapies.
At the heart of Noetik’s tech is OCTO, a powerful AI model that acts like a virtual lab for cancer research. While many AI models focus on predicting molecular structures, OCTO goes further—it predicts how different cancer treatments might play out in real patients. This model can be thought of as a simulator that can test “what if” scenarios, helping scientists see which treatments could work best for which patients, without the long trial-and-error of traditional methods.
OCTO is trained on a large mix of data from thousands of tumor samples, including gene expression, protein data, and images of the cancer cells. By learning from these varied inputs, OCTO can predict how tweaking a single gene could change protein levels across a tumor.
Takeaway of the Week
Roche Plays for Bigger Stakes in Digital Pathology
Roche has announced an expansion of its Digital Pathology Open Environment, integrating over 20 artificial intelligence (AI) algorithms from eight new collaborators into its navify® Digital Pathology platform.
Deep Bio Inc.: Algorithms for prostate cancer detection, grading, and tumor quantification.
DiaDeep: Tools for breast cancer biomarker quantification.
Lunit: Tumor proportion score (TPS) analysis for non-small cell lung cancer (NSCLC).
Mindpeak GmbH: Algorithms for breast cancer biomarkers and PD-L1 analysis for lung, gastric, esophageal, bladder, and breast cancers.
Owkin: Detection of microsatellite stability (MSS) in colorectal cancer.
Qritive: Screening and grading of prostate cancer, analysis of lymph nodes for metastasis, and colon cancer screening.
Sonrai Analytics: Detection of microsatellite instability (MSI) status in colorectal cancer.
Stratipath: Risk profiling for invasive breast cancer.
As per analysis by Katie Maloney from DeciBio, Roche's strategy relies on owning the upstream hardware (slide scanner) and platform (IMS) and using this leverage to create an open environment for downstream image analysis tools. According to Katie, this is somewhat similar to the App Store business model.