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In this issue: a retired chemist designs a renin inhibitor with ChatGPT and Gemini — Astromech raises at $3.8B and Forus at $3B while BioXcel files Chapter 11 — Google maps the largest connectome yet — Claude runs a protein design campaign — and a stablecoin issuer quietly publishes three BCI papers.

Main story

Chemical & Engineering News (C&EN) recently published an interesting case study on using off-the-shelf AI chatbots to assist in drug design work.

As Bethany Halford explains in her article, at ACS Fall 2026 in Chicago, Guibai Liang, a veteran medicinal chemist who co-founded Sheo Pharmaceuticals after retiring from big pharma, presented a talk in the Division of Medicinal Chemistry about how his team designed a novel renin inhibitor for hypertension using ChatGPT and Gemini, off the shelf, with no proprietary model in the loop.

Renin is about as picked-over as targets get: every major pharma ran a program, and aliskiren (Novartis’s Tekturna/Rasilez) is the only one the FDA ever approved — a mediocre drug, in Liang’s words, mostly on bioavailability.

Sheo took Takeda’s TAK-272 as a starting point and put what Liang calls “critical and revealing questions” to the chatbots: what does the hydrogen bond donor on the piperidine do, is the morpholine oxygen needed for activity, is metabolic stability a concern.

The models answered by synthesizing decades of published structure-activity relationship (SAR) insights — “it’s impossible for human chemists to remember all those data to come up with a clear picture,” Liang said for C&EN. From those answers, the team designed a new chemical class, had a CRO make 200 compounds across several iterations, and landed on SHEO-054, which reportedly outperformed aliskiren in a monkey model of hypertension.

Caveats: The structure wasn’t disclosed, only a general scaffold, and the data are self-reported from a conference talk, no paper yet. Also, “outperformed aliskiren” in one primate model is a long way from a development candidate.

The AI here read the literature no human could hold in working memory and handed back a coherent SAR picture, and Liang himself flags that renin may be a special case precisely because so much was published before the field walked away. The enabling condition is a dense, abandoned literature, which describes a surprisingly large share of pharma’s dead targets, and points at a strategy that doesn’t need a $100M platform to run.

Liang’s takeaway was blunt: “If you are not using AI in your medicinal chemistry, start now.”

Read “This company designed a drug candidate with help from ChatGPT and Gemini“ by Bethany Halford.

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📅 I am excited to share that I’ll be a speaker at the upcoming TechBio Europe 2026 event in Paris, on November 30.

TechBio Europe 2026 brings Europe’s TechBio founders, investors, and pharma partners to Paris for one day on Nov 30 at Future4Care — financing, dealmaking, hiring at the bio/code intersection, plus a pitch contest for the continent’s newest startups.

TechBio Europe is organized by the TechBio Commission, co-led by WhiteLab Genomics and Scienta Lab, under the guidance of France Biotech, France Deeptech, Future4care, and proudly sponsored by In Extenso Innovation Croissance, ICOSA | European Intellectual Property Firm, BioLabs, McDermott Will & Schulte, BioTalent, and Wellcome Genome Campus.

TechBio Europe also kicks off Bioweek, a week of industry events in Paris.

Register for TechBio Europe and let’s meet: https://www.eventbrite.fr/e/techbio-europe-2026-tickets-1996434242562.

The Brief

🚀 New Launches

• Geodesic Intelligence launched out of the gate with two products, NovaDDE and NovaAtom-Lite-Preview, pitching an AI-native platform for protein therapeutics and describing its aim as "AGI for drug discovery" — no funding, team, validation data or partners disclosed in the announcement.

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💰 Follow the Money

• Astromech, the Colossal Biosciences spinout founded by Ben Lamm and George Church, raised $20M led by Bob Nelsen (PEAK6, NeoGenesis, Builders VC and CAZ participating) at a company-reported $3.8B valuation ($60M total) to expand its genomic training datasets and model infrastructure for longevity work on evolutionary outliers like bowhead whales and Brandt's bats, though no externally validated performance, revenue or deployment data was disclosed.

• Paul Allen's science fund is backing AI BioDesign, a Seattle effort, with ~$95M over five years — $46.1M to the Allen Institute, $43.8M to UW, $4.7M to Fred Hutch — for a loop where AI designs proteins and genetic switches that don't exist in nature, labs test them, and the results retrain the models.

• BioXcel Therapeutics, the AI-driven neuroscience biotech behind the approved agitation drug Igalmi, filed for Chapter 11 with Teva as stalking-horse bidder at $57.5M upfront plus $67.5M in milestones — a cautionary marker for AI-derived pipelines

• Forus raised a $150M Series C at a $3B valuation led by Bain Capital Ventures, taking total equity past $300M for its AI-agent network that cuts prior authorization from weeks to under 48 hours across physicians, pharmacies, payers and manufacturers.

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🤝 Deals & Alliances

• BenchSci and argenx partnered to apply BenchSci's agentic AI platform EMET to preclinical drug discovery, pitched by CEO Liran Belenzon and argenx's Tim Van Acker as an "always-on AI research partner."

• Owkin licensed K Pro, its agentic "AI scientist," plus multimodal patient data from its MOSAIC network to Servier for oncology research.

• Merck KGaA is paying PostEra "mid-double-digit millions" for its two fertility programs — oral small-molecule agonists of the FSH receptor and the LH/choriogonadotropin receptor that could replace the injection standard in IVF. Both are designed on PostEra's AI platform to solve the selectivity and half-life problems that sank earlier attempts, while PostEra keeps its PMOS program in-house with trials planned for early next year.

• Ginkgo Bioworks won a subcontract worth up to $17.5M under ARPA-H's GIVE program, joining Waterfall Scientific on ESCALATOR — a multi-organization effort to compress end-to-end RNA drug manufacturing onto a benchtop footprint.

  ---

🔬 Science to Watch

• Google Research and HHMI Janelia published in Cell the complete AI-reconstructed wiring diagram of the male fruit fly's brain and nerve cord — 166,000 neurons and 125 million synapses.

  

  It is the largest connectome to date, which, paired with the existing female map, lets researchers compare the sexes neuron by neuron and sets up the methods for mapping zebrafish and mouse brains next.

• AlphaGenome Atlas by Google DeepMind, scores all 9 billion possible mutations, coding and non-coding ones. They say that Gareth Hawkes, a Medical Research Council fellow at the University of Exeter, arguably found 22% more non-coding associations in the UK Biobank just by re-ranking with the Atlas.

• Anthropic reports Claude, orchestrating existing open-source design tools rather than modeling proteins itself, produced 354 wet-lab-confirmed binders from 1,320 designs across 15 targets — a 22.6–35.1% hit rate against the 10–15% industry norm. Though the work is unreviewed, has no human-expert control arm, and the best-performing models stay access-restricted on biosecurity grounds.

• Insilico Medicine Doses First Patient in GENESIS-IPF-3, the World's First Phase III Trial of a Generative AI-Driven Innovative Drug

  

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📋 From the Regulators

• The FDA is putting sponsors on notice that it will refuse clinical data it cannot inspect or verify — with extra scrutiny on foreign Phase 1 and non-IND/IDE studies in countries where site access is denied or informed consent is questionable — and is expanding foreign inspections and publicizing where access was blocked.

  ---

💁‍♀️ People 💁

• Hon Weng Chong, founder of Cortical Labs, the Melbourne company behind the DishBrain work and the CL1 biological computer, announced on LinkedIn that he has resigned after seven years, saying he's proud of what was built and no longer has access to his company email.

  

Leave a comment

A Thing To Know This Week

Addressing one of the biggest challenges in brain-computer interfaces

Tether Evo, a frontier tech division of Tether, the stablecoin issuer, had three brain-computer interface (BCI) papers accepted at the Journal of Neural Engineering, Imaging Neuroscience, and Neural Networks, two of them with the University of Rome Tor Vergata.

All three attack the same bottleneck: every brain produces different signals, so decoders have historically been rebuilt from scratch for each patient, and the calibration burden is a real barrier to fielding these devices. The common move across the papers is an alignment step that maps different people’s neural data into a shared space, so one model can be trained across subjects and then fine-tuned for a new one.

The speech paper (preprint on bioRxiv) deals with a cross-subject neural-to-phoneme decoder trained on invasive recordings from multiple participants implanted in different cortical regions, which is the hard version of the problem. Tether reports it matches or beats single-patient systems while adapting to a new person in minutes to hours rather than the usual long calibration, and describes it as the first of its kind. The vision paper reconstructs what macaques were looking at from 200 ms of spiking data, identifying the exact image out of thousands 70% of the time.

The music paper ran fMRI on five people hearing 540 songs across 10 genres, hitting 61% genre identification against 10% chance and ~25% song identification among 60 candidates against under 2% — classical and jazz the most distinctive, metal and disco the most confusable.

It is notable that Tether took a $200M majority stake in Blackrock Neurotech in April 2024, the company behind a large share of the world's chronic human intracortical implants.

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Strategic Signals…

👉 AI labs are pivoting hard into health care to fix the notorious public perception trend. Anthropic is talking up biology ahead of its IPO, Nvidia and Lilly are building a $1B drug discovery lab, etc., partly because curing disease is a far better public story than data centers hogging power and driving up utility bills, and partly because life sciences diversify revenue as these companies head to public markets.

The catch is timing: real breakthroughs are years off, AI is making some health care more expensive, and biosecurity risk is real enough that Anthropic added safeguards to Claude Fable 5 after finding it could give a bad actor "significant uplift," so the goodwill is being spent well before the results arrive…

👉 AI-enabled drug repurposing is one of the most straightforward value propositions of the modern AI drug discovery movement. THPharm ran its Phase 3 metabolic drug THP-001 through Insilico Medicine’s PandaOmics (including 16 public datasets, 269 samples of post-treatment gene expression) to find new indications it might also treat, surfacing heart failure, fatty liver disease, and obesity as candidates.

The logic is capital efficiency: the drug already has human safety data, so each new indication starts with a big chunk of risk removed. Though these are computational hypotheses that still have to clear cell models, animals, and human trials.

👉 Aaron Blotnick argued on LinkedIn that OpenAI's integration of ChatGPT directly into Epic (the EHR holding records for 325M+ patients) just wiped out an entire startup category. Clinicians can now summarize labs, pull patient history, prep for visits and build clinical timelines without leaving the chart, plus a new plug-in pulling live data from ClinicalTrials.gov, CMS, RxNorm, DailyMed and PubMed. His conclusion: "If your startup's moat was 'AI + EHR integration,' it's time to find a new moat."

That was last week, and have a great one ahead!

Forward this to a colleague who should be tracking this space,

Cheers!

— Andrii and WTMB Team!

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Read also:

What Do the New AI Model Releases by Anthropic and OpenAI Change for Life Sciences?

The Bigger Point Beyond Insilico’s Aging Research Milestone

Into the Dark: Finding Novel Drug Targets Within the Depths of Our Proteome

If this newsletter is in your inbox, it’s because you subscribed, or someone thought you might enjoy it. In either case, you can subscribe directly by clicking this button:

In this issue: The FDA opens the question of how you evaluate a clinical AI that answers differently every time — Network Bio, Harell Data and Lilly's TuneLab each try a different way of getting proprietary data into AI models without the owner handing it over — Claude runs a 48-hour protein design campaign on its own and publishes the prompt — Insilico Medicine sketches an ageing virtual cell — Cell publishes fifteen grand challenges for generative AI in biology — Nature Reviews Neurology asks what happens after a clinical model works — Faro raises $37M to speed up trials — GSK starts paying NOETIK to rent its models — An emerging market for DNA as a programmable building material. And more…

The FDA writes down how it might grade a clinical AI, and opens the docket

This month, the FDA’s Center for Devices and Radiological Health (CDRH) published Considerations for the Regulation of Generative AI-Enabled Medical Devices. It’s a discussion paper, not guidance, and comments close October 19.

It is a conceptual matter, with potentially far-reaching legal and business implications for anyone building AI-driven medical devices… so make sure to read it.

In a nutshell, the old approach to assessing medical devices is to assume a device answers the same way every time, i.e., like “good old software”. Generative AI does not behave this way, it can answer differently tomorrow, and the foundation model underneath it can be updated by a third party, influencing the device behavior in unpredictable ways. So, trying to evaluate the behavior of AI-driven devices precisely might be unrealistic. The paper’s own words: “… it may be unreasonable to evaluate every conceivable input…”

So how do you evaluate something like that?

The FDA’s tentative answer is that you do it roughly the way you evaluate a doctor. Exams to check they know the material and won’t do anything dangerous, then supervised practice, then keeping an eye on them once they are working alone.

In practice, for a device that could become benchmarking, then clinical confirmation — which might be retrospective review, or shadow deployment, or a full prospective trial depending on the stakes. Then monitoring for as long as it is in use. In other words, where there is no single right answer, the device gets compared to a panel of clinicians or a median clinician rather than to an ideal correct response.

How much scrutiny you get depends on what the software actually does (just informs, directs, or acts on its own), and how badly things can go if it is wrong. A simple diagram explains the framework:

An important food for thought for businesses building AI-driven medtech devices — about risk management. A lot of health apps give someone specific advice and then add "talk to your doctor" underneath, on the assumption that the disclaimer keeps them in the safe, informational category. The FDA is signalling that it probably does not… What does is what the output actually tells the person to do.

⚡ This issue is brought to you by SYDRA

The Brief

💰 Follow the Money

• In a historical milestone, Moderna and Merck's intismeran, an mRNA vaccine built individually for each patient using data of their own tumour's mutations, kept melanoma from returning for longer when added to Keytruda, in a Phase 3 of about 1,100 people whose tumours had been surgically removed. It's the first randomised Phase 3 win for a personalised cancer vaccine, though the actual numbers come later at a conference, and each dose still takes months to manufacture. Anyway, Moderna’s stock jumped 177% on the news, its biggest one-day move ever, with Merck also gaining substantial upward movement.

• Network Bio, a biobank-focused biotech startup, launched out of stealth with $50M and what it calls the world’s largest patient-tissue training dataset — 500,000+ patients with paired tissue, blood and longitudinal outcomes from Mass General Brigham, Penn, Duke and CU Anschutz on a common data model. Investors include Section 32, Thiel Bio, Founders Fund and Breyer Capital, which led the company’s first institutional round in 2022.

  Interestingly, Network Bio and NVIDIA are also building what they describe as the first foundation model trained on cell-free RNA, using Parabricks and BioNeMo Recipes. The company also disclosed a $30M+ co-development and licensing agreement with a Fortune 100 healthcare company to identify and interpret novel disease signatures using Network’s proprietary AI architecture.

• Harell Data, founded by Adaptive Biotechnologies co-founder Harlan Robins, raised $15M from Fuse and Cercano Management to let data owners host proprietary training sets and take a cut of the compute revenue from models trained on them, rather than waiting on downstream royalties.

  Why it matters: it prices scientific data as a rentable asset instead of an equity bet. Early datasets come from A-Alpha Bio and Adaptive.

• Faro, an AI startup working on clinical trials, raised $37M from Merck's venture fund and S32. CEO Scott Chetham says the startups chasing faster trial documents "just made tiny bits a little bit faster."

🤝 Deals & Alliances

• Eli Lilly’s TuneLab models are now in Benchling. AbLab predicts antibody developability (thermal stability, aggregation, viscosity, solubility, etc) from heavy and light chain sequences. ChemLab returns ADME/Tox predictions from a SMILES string.

  The catch: access is reciprocal. Participating biotechs contribute their own assay data through TuneLab’s federated learning platform, which never exposes the raw data, and admins install the models from Benchling’s Model Hub registry.

• NOETIK hit the first payment milestone in its five-year GSK deal by getting its OCTO-VC cancer foundation models running inside GSK's own systems, which matters mainly because GSK is paying subscription-style fees to rent the models rather than buying drug candidates, and that arrangement is now producing money.

🔬 Science to Watch

• Claude ran a 48-hour autonomous protein design campaign and published the prompt. Anthropic reports de novo binders against 14 of 15 targets — 1,320 designs, 354 of which bound in wet-lab testing at Adaptyv Bio and Twist Bioscience, with hit rates of 22.6–26.7% across the multi-target run and 35% in a single-target 24-hour campaign, against a stated field baseline of 10–15%.

  The caveat, per Endpoints News, noted by Andrew Dunn: Claude generated nothing much itself, it orchestrated open-source tools (PXDesign, RFdiffusion, BoltzGen) and reasoned over the results, and hit rate moves with target difficulty. Maltose-binding protein defeated all 90 designs against it. Designs, raw data, a 29-page technical report and the full ~16,000-word prompt are public.

• Insilico Medicine sketched out what an ageing virtual cell would look like. Most virtual cell models are trained on cells caught at a single moment, so they can tell you what a cell looks like but not how it got there or where it's going. Insilico wants to go further, making age the thing the model is operating on. Instead of one big gen network, they plan on using a group of AI agents that each handle a different level — molecules, cells, tissues, organs, the whole organism, communicating between each other.

  The idea is you could ask what happens if you, say, knock out a gene, and get the answer accounting for time and space. I think it is more of a concept for now, as I did not find specific data or paper yet. They say more thinking is coming at Aging Research and Drug Discovery Meeting in Boston soon.

  

• In a new article in Cell, authors published fifteen grand challenges for generative AI in cell biology, sorted into four categories: molecular interactions, molecular functions, cellular and systems function, and translation.

  Why it matters: the authors argue current systems are benchmarked on retrospective statistical tasks rather than decisions, and call for CASP-style held-out evaluation.

• A Nature Reviews Neurology perspective maps what happens after a clinical model works: outcome benefit, workflow fit, reimbursement and drift, arguing deployment is not the end of model development but another stage of evaluation.

📋 From the Regulators

• The FDA approved Revolution Medicines' daraxonrasib for previously treated metastatic pancreatic cancer — the first drug ever cleared that hits RAS broadly, the cancer gene that defeated the industry for forty years and drives nearly every pancreatic tumour. It doubled median survival to 13.2 months against 6.7 on chemotherapy in the Phase 3 RASolute 302 trial, and the FDA cleared it six and a half months ahead of schedule.

Leave a comment

⚡In partnership with SYDRA

Most AI drug-discovery companies begin with a target and use algorithms to optimize molecules around an assumption. SYDRA reverses the logic.

Our proprietary AI selects and generates novel chemistry; whole-organism lifespan assays reveal which molecules actually affect aging biology; and only the phenotype-proven winners advance into aged-mouse studies, human-cell target deconvolution and disease positioning.

Starting from six million molecules, we identified five significant lifespan hits, advanced two lead programs into late-life mouse pilots, and created a new generative chemistry cohort now entering synthesis. We don’t use AI to decorate a target hypothesis. We let living biology decide which AI discoveries deserve to become medicines.

SYDRA is now raising a CHF 2.6 million pre-seed round to accelerate lead programs toward pharma partnering.

A Thing To Know This Week

Beyond the Code: How DNA Nanotechnology is Moving from Molecular Origami to Programmable Biotech Platforms

In a new article published on BiopharmaTrend, Louise von Stechow and Marco Lolaico make the case that DNA is worth treating not only as a carrier of genetic information but as a construction material, one whose predictable base pairing lets researchers fold structures that place proteins, ligands or fluorophores exactly where they want them, and even build devices that switch shape or release cargo on cue.

In the article, Louise and Marco walk through where that has actually reached the market, which so far is mostly research tools and diagnostics:

- GATTAquant already sells DNA origami calibration standards for microscopy,

- Amplifold raised €5 million to make rapid tests roughly a hundred times more sensitive without changing the familiar lateral-flow format,

- Nanogami and MediQuant are building biochips and point-of-care drug monitoring, respectively… and so on.

Therapeutics are further back:

Plectonic's logic-gated T-cell engagers, DoriNano's cancer vaccine now working with Memorial Sloan Kettering and Daiichi Sankyo, DNA Nanobots' non-viral gene delivery — all still preclinical.

The authors are clear-eyed about why: the structures are expensive to make, they fall apart in the body, and nobody has settled how to prove batch-to-batch consistency.

Cost is improving fast, though (a 2026 study got to about $6 per milligram in the lab), but they argue cheaper DNA alone won't do it, and that the field needs standardized manufacturing, better stabilization, quantitative quality control, and regulators willing to say which structural attributes actually matter.

Read the article here.

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Strategic Signals…

Bart De Witte, co-founder and CEO of Isaree, argues that Apple's new M5 Ultra chip, 512GB of unified memory under a desk, kills the assumption that hospital AI has to mean shipping patient data to someone else's data centre, because a frontier-scale medical model now fits in local memory. His company is building for that world: an open platform where clinicians build their own agents, running on-device or on-prem, with GDPR compliance built into the architecture (especially relevant for EU-based facilities).

That was last week, and have a great one ahead!

Forward this to a colleague who should be tracking this space,

Cheers!

— Andrii and WTMB Team!

Share

Read also:

Could Anthropic Disrupt the Techbio Companies Built for Pharma/Biotech? (Also: The Numbers Behind China’s Biopharma Ascent)

Is the Future of AI Drug Discovery Hybrid?

Three Big Ideas in Aging Research That Could Shift the Therapeutic Landscape

Cancer as a Data Problem: What AI Is Doing in Oncology

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Cheers!

— Andrii and the Team of Where Tech Meets Bio newsletter, from sunny Spain🌴.

In this issue: a decade-in report card on AI drug discovery; frontier AI labs moving into the CRO and clinical-trial layer; agentic co-scientists landing inside pharma research organizations; what is happening in in vivo cell therapies; the FDA piloting a faster route into first-in-human trials, and other key signals.

After a decade of AI drug discovery, two of its vocal voices assess progress

Perhaps unusually for August, there is a lot of business and research activity in the artificial intelligence (AI) drug discovery space. Notably, within four days of each other, two respectable voices in the AI drug discovery space independently published two important pieces about the state of progress, where industry stands about all sorts of AI tools as of 2026, and where it may take us from here.

On August 3, insitro founder and celebrity in the field of artificial intelligence published “Drug Discovery Has No Magic Wands.” She splits development broadly into three stages:

… and argues AI effort has concentrated overwhelmingly on the middle one, molecular design, while more than 90% of clinical failures come from picking the wrong mechanism. Even total success on the AI-addressable portion, she notes, touches only about half a development timeline; the cell atlases needed to attack stage one are “orders of magnitude too small.”

On August 7, Dr. Andreas Bender, Professor for Machine Learning in Medicine at Khalifa University, and a large co-author group published “Artificial intelligence in drug discovery — what it is, where we stand and the path forward” in Nature Reviews Drug Discovery.

Its verdict on a decade of work: clinically relevant impact has been “disappointingly limited,” caused by too little focus on clinical translation, the context-dependent nature of real biological data, and problem definitions too vague for the decisions the models are meant to inform. The prescription is a shift from “technology push” to “science pull,” and benchmarks that measure improved decision-making rather than model performance.

Neither piece cites the other, the convergence seems to be independent, which likely signals a growing industry consensus about the current state of AI in drug research.

⚡ This issue is brought to you by SYDRA

The Brief

💰 Follow the Money

🔹Dash Bio raised a $30M Series A led by Oak HC/FT for its robotics-run bioanalysis lab — preclinical assays back in days, not months.

🔹Pathos AI in-licensed two oncology assets in one day for a reported ~$2.2B — a TROP2/HER3 bispecific ADC from Alphamab and an ERα PROTAC from AstraZeneca.

🔹Bruker took a majority stake in organ-on-a-chip maker MIMETAS (terms undisclosed). Why it matters: positions Bruker in the New Approach Methodologies push to replace animal testing.

🔹Anthropic opened rare disease research grants — up to $50,000 in Claude credits over six months, two tracks, applications closed August 2.

🤝 Deals & Alliances

🔹Bristol Myers Squibb is deploying Schrödinger’s agentic AI co-scientist Bunsen across its research organization, plus the RetroSynth synthesis planner. Terms undisclosed.

🔹Genentech exercised its first option on a Recursion-discovered neuroscience target, moving it into joint small-molecule discovery. The numbers: first target out of a collaboration that has paid Recursion $216M to date, with up to 40 possible programs. — link

🔹ICON, a top-five CRO, signed a multi-year deal to embed Claude across the clinical trial lifecycle. Backstory: caps a year of Anthropic life-science moves — Claude for Life Sciences, the ~$400M Coefficient Bio buy, Claude Science, and hiring AlphaFold’s John Jumper.

🔹J&J paid $785M for an option to acquire in vivo CAR-T player Sail Biomedicines for a further $2.58B. Backstory: the fifth major in vivo CAR-T deal since Lilly’s $7B Kelonia buy in April thinned the field of outright targets.

🔹Merck KGaA adopted Evinova‘s AI-native clinical development platform — AstraZeneca’s in-house health-tech business selling to a competitor.

🔹Pharma is pooling its structural data. C&EN on the Federated OpenFold3 Initiative, where AbbVie, J&J, BMS, Takeda and Astex train a shared open model without exposing IP.

🔬 Science to Watch

🔹A CRISPR enzyme that kills cancer cells by shredding their own DNA. Doudna’s group published Cas12a2 in Nature: triggered by a mutant RNA transcript, it destroys the host cell’s chromatin, killing p53-mutant cells while sparing healthy ones. Preclinical.

🔹An AI biologist called XunZi flagged CHK2 as a Parkinson’s target; inhibiting it rescued dopaminergic neuron loss and motor deficits in mice. Nature Biomedical Engineering, Aug 4.

🔹Open-weights models that read chemistry out of patents. Edison Scientific’s MarkushGlyph parses Markush structures at 58–64% accuracy; single-structure recognition hits 93.8%. What it is: Markush diagrams are how one patent claims millions of compounds.

🔹Clinical AI’s dangerous failure is omission, not fabrication. The NOHARM benchmark found errors of omission account for >80% of severe errors across 20 LLMs and 4 RAG clinical tools. Preprint.

🔹Recursion says protocol simulation against real patient data expanded trial eligibility 10–40%, with enrollment 30–60% above historical projections. Vendor-reported.

🔹Insilico launched a benchmark for AI drug discovery models — 300+ evaluations across two suites, live since July 30. No leaderboard scores published yet.

🔹~50 authors proposed an L0–L5 autonomy ladder for laboratories, arguing self-driving labs need a shared “world model” layer. Leskovec, Uhler, Ioannidis and Shah among the signatories.

📋 From the Regulators

🔹FDA is piloting a faster route into first-in-human trials. The Expedited IND Pilot would let sponsors use pre-vetted research institutions and file INDs on a rolling basis; the agency estimates clarified Phase 1 CMC expectations alone could save 6–12 months.

🔹A companion draft guidance shifts first-in-human dose selection toward QSP modelling and away from animal toxicology.

Leave a comment

⚡In partnership with SYDRA

Most AI drug-discovery companies begin with a target and use algorithms to optimize molecules around an assumption. SYDRA reverses the logic.

Our proprietary AI selects and generates novel chemistry; whole-organism lifespan assays reveal which molecules actually affect aging biology; and only the phenotype-proven winners advance into aged-mouse studies, human-cell target deconvolution and disease positioning.

Starting from six million molecules, we identified five significant lifespan hits, advanced two lead programs into late-life mouse pilots, and created a new generative chemistry cohort now entering synthesis. We don’t use AI to decorate a target hypothesis. We let living biology decide which AI discoveries deserve to become medicines.

SYDRA is now raising a CHF 2.6 million pre-seed round to accelerate lead programs toward pharma partnering.

A Thing To Know This Week

In vivo CAR-T, and why everyone is buying it

Conventional CAR-T extracts a patient’s T cells, engineers them in a facility to recognise a cancer target, and infuses them back. It works, at several hundred thousand dollars per patient, over weeks, at specialist centres only.

In contrast, in vivo CAR-T does the engineering inside the body: inject a lipid nanoparticle or targeted viral vector that reprograms T cells where they already live. No extraction and no manufacturing slot, a body becomes, basically, its own bioreactor for the therapy. If it works, a cell therapy becomes almost as simple as a regular injection.

That “if” is what eighteen months of dealmaking has been pricing on the level of strategic M&As.

AstraZeneca, AbbVie and Gilead each bought a platform in 2025; Lilly bought two in early 2026, for up to $7B in total. Most of these assets are preclinical — pharma is arguably buying delivery technology on the bet that the biology is proven and only the logistics are mostly unsolved.

Interestingly, Johnson & Johnson appears to be more cautious. J&J’s $3.5 billion deal with Sail Biomedicines serves as the textbook definition of an "option-first" structural hedge. Rather than acquiring an in vivo company outright at a massive premium like others, J&J structured a massive option agreement that secures early platform access while shifting early clinical risk entirely onto the partner.

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Strategic Signals…

🔗 DeepMind dismantled the AlphaFold team — and it matters less than the headline suggests.

🔗 Ramy Farid called AI in drug discovery “nonsense” in 2024. In July he shipped an AI co-scientist and said he couldn’t be more pleased to be wrong about AI in the past.

🔗 “Forward deployed scientist” is becoming a real job title — Chai, Phylo, Lila and Periodic Labs are all hiring it.

🔗 China's share of first drug launches rose from 3.3% to 28.7% between 2004 and 2024 while Europe's fell from 26.7% to 8.5%, per a new analysis. US is still leading the pack but declined from 45.9% to 37.8%.

That was last week, and have a great one ahead!

Forward this to a colleague who should be tracking this space,

Cheers!

— Andrii

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Read also:

Could Anthropic Disrupt the Techbio Companies Built for Pharma/Biotech? (Also: The Numbers Behind China’s Biopharma Ascent)

Is the Future of AI Drug Discovery Hybrid?

Three Big Ideas in Aging Research That Could Shift the Therapeutic Landscape

Cancer as a Data Problem: What AI Is Doing in Oncology

Also, if you're at Drug Discovery Chemistry in San Diego this week, our board advisor Andrew Lozoniuk is there and happy to chat!

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If this newsletter is in your inbox, it’s because you subscribed, or someone thought you might enjoy it. In either case, you can subscribe directly by clicking this button:

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🤖 AI x Bio

(AI applications in drug discovery, biotech, and healthcare)

Agents at AACR 2026

At the American Association for Cancer Research annual meeting this year, out of ~9,600 accepted abstracts roughly 1,100 (1 in 8) are AI-related, according to Miraei AI.

Within that: ~160 reference LLMs, generative AI, or agentic systems; ~140 involve multimodal approaches; ~225 cite clinical validation or deployment language. The Miraei team’s read is that AI in oncology is shifting from isolated proof-of-concept models to embedded operational systems.

The leaderboard of AI-related abstract affiliations at AACR 2026, split by academic institutions and commercial entities:

AACR will host a dedicated “Agentic AI in Cancer” session: MD Anderson will debut “Charles,” a self-critical agentic drug discovery analyst built around traceability and hallucination mitigation. MSK will show an MCP-enabled AI agent in its genomics platform automating multimodal analysis at petabyte scale. AstraZeneca (the most-represented biopharma in the AI corpus with ~10 abstracts) will present an agentic system for RNA-seq pipeline automation. (AstraZeneca also acquired Modella AI earlier this year, whose Judith agent automates pathology image analysis workflows.)

Foundation models were still a distinct thread. Bioptimus delivered three abstracts including two late-breakers—H-optimus-1 for computational histopathology and M-Optimus-1, a multimodal model unifying H&E imaging, bulk and single-cell RNA-seq, and spatial transcriptomics. A UCSD–Lunit collaboration will present a foundation model of cancer genotype predicting therapeutic response.

Around 225 abstracts use language suggesting clinical validation or deployment, not just modeling accuracy on retrospective data. A separate “LLMs in the Clinic” session (~30 abstracts) will focus on infrastructure problems: trial matching, EHR extraction, pathology report abstraction, patient message triage. A University of New Mexico study reported cancer patients preferred ChatGPT-generated responses to physician-authored ones in a blinded comparison.

This tracks with what we covered in our February deep dive on AI in oncology. The pattern across ~1,100 abstracts is the same shift we described there: from standalone models to infrastructure. Agentic systems managing multi-step workflows, LLMs doing clinical plumbing, foundation models trained at institutional scale. Commercial actors (Tempus, Labcorp, Lunit, Natera, BMS) are showing operational work; the startup layer (Bioptimus, Data4Cure, Keiji AI, EXoPERT) is landing late-breaking slots.

AI in the pipeline

A few recent moves worth noting across clinical trials, pharma infrastructure, and AI-designed therapeutics.

🔹 Massive Bio published a prospective study in ESMO Real World Data and Digital Oncology with 3,804 cancer patients, 17,000+ oncologist-confirmed trial matches, 4x faster matching (120-to-30 min per patient), outperforming zero-shot GPT-4 and GPT-4o.

🔹 CRO Fortrea released an AI suite for clinical workflow automation, part of CEO Anshul Thakral’s turnaround strategy since taking over in June 2025.

🔹 Tempus expanded its collaboration with Gilead with enterprise-wide access to Tempus’s Lens platform for oncology R&D, continuing the pattern of large pharma outsourcing data infrastructure to specialized AI/RWE companies.

🔹 Astellas exercised its option to license a Dyno Therapeutics AAV capsid for skeletal muscle gene delivery, following Roche’s CNS capsid license in January 2025 — making Dyno the first company to license AI-designed capsids for both CNS and muscle ($15M fee plus milestones and royalties).

💰 Money Flows

(Funding rounds, IPOs, and M&A for startups and smaller companies)

Daniel Strasser, Executive Director and Therapeutic Area Biomarker Head at Novartis, posted a tracking that puts the pace of AI-pharma deals in perspective.

• Between 2021 and 2024, top pharma signed 2–4 AI deals per year. In 2025: 15. At Q1 2026 pace, the year is tracking toward ~24 — a 12x increase in five years.

• Combined potential deal value across 34 tracked partnerships exceeds $78B. All top-10 pharma like Lilly, Novartis, Roche, Sanofi, AstraZeneca, J&J, Novo Nordisk, Takeda are in with billion-dollar-scale commitments.

• NVIDIA alone ($4.4T) is worth more than all top-10 pharma combined ($3.5T), and the AI-pharma pure-plays signing these deals collectively sit around $23B.

Strasser's question is—which side captures the value?

Cell reprogramming in the clinic

Longevity raise—Life Biosciences (co-founded by David Sinclair) closed an $80M Series D for the Phase 1 trial of ER-100—reportedly the first partial epigenetic reprogramming therapy to receive FDA IND clearance. ER-100 uses AAV2 vectors encoding three Yamanaka factors and an oral small-molecule switch, targeting retinal ganglion cells in optic neuropathies.

Q1 2026 was strong for longevity biotech overall ($3.7B across 49 deals, 56% YoY). Competitors remain largely preclinical:

• Altos Labs (~$3B, stealthy)

• Retro Biosciences ($1B+ Series A, clinic-stage autophagy)

• NewLimit ($130M Series B, liver)

Life Bio’s differentiator is clinical specificity, though the sector still has to prove it can produce credible aging biomarkers alongside disease-specific efficacy.

Europe’s largest independent biopharma raise?

Jeito Capital closed Fund II at over €1B (~$1.2B). The fund targets 15-to-20 clinical-stage investments at up to €150M each, addressing a funding tier where European options have historically been thin.Fund I (€534M, 2021) exits include Eyebiotech (Merck, $1.3B) and Hi-Bio (Biogen, $1.15B).

For context: between 2015 and mid-2025, EU biotech startups attracted €25B in venture capital versus €219B in the US, and 66 of the 67 EU companies that went public over the past six years listed on foreign exchanges.

🔹 A digital therapeutic for negative symptoms of schizophrenia—Click Therapeutics got $50M Series D from Boehringer Ingelheim plus full commercial rights for CT-155. Phase III CONVOKE met its primary endpoint (p=0.0003, Cohen’s d = –0.36). Click already has two authorized digital therapeutics (migraine, major depressive disorder) and 63 patents.

🔹 The OpenAI Foundation announced $100M+ in initial grants for Alzheimer's research across five tracks: causal mapping (Arc Institute), AI-assisted drug design (Institute for Protein Design + Mass General Brigham), open datasets (EvE Bio), biomarkers (UCSF), and off-patent treatment testing (Harvard). David Baker's IPD team says they've already engineered molecules that engage and degrade Alzheimer's-relevant targets using AI protein design models. The off-patent track is notable: lithium orotate and the shingles vaccine both have suggestive evidence but no private-sector incentive to fund proper trials. More grants expected through 2026.

🏛️ Bioeconomy & Society

(News on centers, regulatory updates, and broader biotech ecosystem developments)

🔹 Neuropacs received De Novo classification from the FDA for a diffusion MRI–based AI diagnostic aid that helps differentiate atypical Parkinsonian syndromes (MSAp, PSP) from Parkinson’s disease. The classification establishes a new regulatory category “Parkinsonian syndrome diagnostic aid,” and is the first device in it. The tool draws on 15+ years of research and a prospective multicenter study across 21 centers (Parkinson Study Group, published in JAMA Neurology). Neuropacs recently closed a $1M seed round.

⚙️ Other Tech

(Innovations across quantum computing, BCIs, gene editing, and more)

🔹 Can it deliver? Quantum computing is drawing pharma bets. Amgen has worked with Quantinuum since ~2018 and invested in its $300M round in 2023—building competence now for utility that’s 5–10 years out. Novo Holdings is assembling a portfolio (Sparrow Quantum, Phasecraft) and says it’s “confident tangible quantum advantage for defined applications will be achieved before 2030.” VC in quantum hit $3.8B in 2025. Merck KGaA’s Thomas Ehmer offered the counterpoint: the field is “always three years away,” and whether the killer app justifies advance competence-building remains open.

🔹 Miltenyi Biomedicine’s CAR-T drove three autoimmune diseases into simultaneous remission in a single patient—AIHA, antiphospholipid syndrome, and immune thrombocytopenia, all B-cell–driven and previously treatment-resistant. Eleven months out remission holds. Kyverna Therapeutics is likely first to an autoimmune CAR-T approval (stiff-person syndrome, FDA submission expected H1 2026).

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Cover: Antique celestial map illustration with mythological constellations and star charts from an early 19th century astronomy book, published in London in 1822. iStock

Read also:

Three Big Ideas in Aging Research That Could Shift the Therapeutic Landscape

Cancer as a Data Problem: What AI Is Doing in Oncology

Hi! This is BiopharmaTrend’s weekly newsletter, Where Tech Meets Bio, where we explore technologies, breakthroughs, and cutting-edge companies.

If this newsletter is in your inbox, it’s because you subscribed, or someone thought you might enjoy it. In either case, you can subscribe directly by clicking this button:

Subscribe now

🤖 AI x Bio

(AI applications in drug discovery, biotech, and healthcare)

The CRO intelligence layer remains unclaimed

Reuters reported that CRO stocks (IQVIA, Medpace, Charles River) have dropped sharply since Anthropic launched AI agents in February. The authors’ conclusion is that the market is overreacting, AI can’t replace patient recruitment and site execution, CROs will be broadly fine.

The near-term read is probably right, but the longer-term question is more interesting: what happens when the design layer of trials (protocol optimization, site selection, patient matching) gets commoditized by AI tools that sponsors can run themselves?

As noted recently, strategic CROs sell execution and intelligence bundled together. Unbundle that, and they’re competing on operations and logistics alone, which is a different kind of business that’s priced differently.

There’s also a second angle: if AI lowers the intelligence barrier, a new class of leaner, tech-native CROs could assemble trial infrastructure on demand, more platform than services firm. This structural shift is explored in our 2023 BiopharmaTrend analysis of the CRO industry, though agentic capabilities at the time were still early. Nobody is cutting CRO spend yet. But “no evidence of cuts” is not the same as “no shift in leverage.”

Agentic AI in drug discovery is already delivering value, but not where the hype points

📝 A new review in Drug Discovery Today covers eight case studies from companies including Coincidence Labs, Human Chemical, Potato, Kiin Bio, Augmented Nature, onepot, Plex Research, and Convexia. Some reported specific quantitative benchmarks, not just “we used AI and it was faster.”

The most convincing gains are in the tedious middle: literature synthesis, protocol generation, assay design. Potato’s Tater agent took the design cycle for a qPCR assay from literature review through to executable lab automation code from 1-4 months to under two hours. Empirical validation was still needed, but the bottleneck it removes is the coordination tax between “we know what assay we need” and “here’s a draft protocol ready for the bench.”

Note: there’s missing cost data: every case study reports time savings and none report what the agentic infrastructure costs to build and maintain.

The authors flag that current benchmarks capture whether an agent got the right answer but not whether the reasoning behind it was sound. And a separate evaluation from Princeton and Cornell suggests the problem runs deeper: across 14 AI agent systems, agents that could solve a task often failed on repeated attempts under identical conditions. Bigger models didn’t reliably fix this.

Our read: the real value right now is compressing the overhead between steps that already work on their own, not autonomous science (yet). We went deeper on where agents are actually being deployed, what’s breaking, and what alternatives look like in our recent deep dive:

➕ Fay Lin at GEN also surveyed agentic platforms now on the life sciences market after NVIDIA GTC: Kosmos, LabOS, Latent-Y, Dyno Psi-Phi, and others.

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Is AI replacing radiologists?

The “AI replacing radiologists” discourse resurfaced this week after NYC Health + Hospitals CEO Mitchell Katz said AI could replace a significant portion of radiology work—initial AI reads, radiologist review of flagged abnormals. The Stanford-Harvard NOHARM study showed top LLMs outperforming generalist physicians on clinical safety, and the numbers look good in controlled settings.

Bo Wang, Chief AI Scientist at University Health Network (Canada’s largest research hospital network), offered a corrective from deployment experience:

The accuracy is real in narrow tasks. But NOHARM found 76.6% of AI errors were omissions = things the model never flagged. In a hospital, a missed finding propagates: downstream physicians trust the AI read, the patient waits, the window closes. (One caveat: NOHARM evaluated LLMs on clinical recommendations across ten specialties, not imaging-specific AI, though the omission pattern maps to radiology concerns.)

No hospital system has solved the accountability question either. When an AI-assisted diagnosis causes harm, the liability chain between physician, hospital, and vendor has no established framework. Which is not regulatory delay but “...a fundamental gap in the infrastructure for AI-in-medicine.”

In Wang’s view, here’s what’s actually happening: AI is reshaping the radiology job, not eliminating it. Routine reads get faster, time shifts to complex cases and clinical correlation. The capability question is nearly answered, while the deployment question has barely been asked.

AI clinical operations partnerships keep stacking

🔹 Bristol Myers Squibb and Faro Health announced a multi-year deal to apply AI across clinical trial design and protocol workflows. Faro structures protocols into standardized, machine-readable formats and benchmarks them against datasets like Tufts CSDD, with work spanning both early- and late-phase programs. Faro is not an isolated instance—this year BMS also partnered with Evinova on trial design and with Microsoft on AI-based patient identification.

🔹Tempus reports that its ALERT trial, conducted with Medtronic across 35 U.S. hospitals, showed that AI-driven alerts embedded in EHRs improved detection and treatment of severe heart valve diseases. The study had 765 clinicians, over 2,000 echocardiograms, and used Tempus’s “Next” to analyze echo reports in real time with NLP and notify clinicians about high-risk findings.

🔹 OpenEvidence partnered with Tandem to connect real-time, AI-driven clinical decision support with automated prescription and insurance workflows. OpenEvidence functions as a clinical search layer, synthesizing evidence from sources like NEJM, JAMA, and Cochrane to support treatment decisions at the point of care, with over 1 million daily consultations in the U.S. Described as “ChatGPT for doctors,” OE raised $250M at a $12B valuation this year and generates over $100M in annual revenue, in part through ads embedded in the platform.

AI in the pipeline layer

🔹 Boehringer Ingelheim is expanding its collaboration with OpenProtein.AI to push AI-driven antibody discovery further into its development workflows. The pairing of a large pharma with deep therapeutic pipelines and a startup built around protein foundation models is becoming a common pattern.

🔹 Infinimmune and Merck entered a discovery and development partnership—Infinimmune will use its platform to screen human immune cells and identify naturally occurring antibodies, then refine them with AI tools. Merck gains exclusive commercialization rights, with potential milestone payments up to $838M across multiple undisclosed disease targets.

🔹 Evotec, Germany-based drug R&D company operating a large network of partnered R&D platforms, appointed Ashiq Khan as chief commercial officer, bringing AI and robotics experience from previous roles at Iktos and Schrödinger. The hire is part of Evotec’s broader transformation plan announced last month, which named commercial execution as a core focus.

🔹 Insilico Medicine partner TaiGen Biotechnology achieved first-subject enrollment and dosing in the Phase 1 trial of ISM4808—an oral HIF-PHD inhibitor for CKD-related anemia discovered with Insilico’s generative AI platform for small-molecule design in under three months (from the December 2025 in-licensing). Another data point for Insilico’s growing list of partnered programs moving into clinic.

🔹 Enveda Biosciences reported early clinical data from its Phase 1b trial of an oral candidate discovered via the company’s AI platform, in moderate-to-severe atopic dermatitis. Nine patients treated at 800 mg daily for 28 days showed rapid reductions in disease severity and itch within the first week, with improvements continuing after treatment cessation. No serious adverse events were reported. ENV-294 is a non-degrading stabilizer (LOCKTAC) showing biomarker activity across Th1, Th2, and Th17 pathways—broader modulation than single-cytokine-axis therapies. Enveda plans Phase 2a trials in atopic dermatitis and asthma, with Phase 2b targeted for mid-2026.

🔹 Lantern Pharma, the AI-driven oncology company known for its RADR AI platform, is joining the agentic wave with withZeta.ai—a multi-agentic co-scientist platform for rare cancer drug discovery that autonomously queries proprietary knowledge bases, clinical trial databases, and molecular libraries. The company will demo the platform at an investor briefing on April 9.

💰 Money Flows

(Funding rounds, IPOs, and M&A for startups and smaller companies)

Pharma went on a $25.5B, eight-day acquisition spree. Six pharmaceutical companies signed acquisitions worth up to $25.5B by the start of this month, including two $5B+ upfront deals disclosed on the same Tuesday morning: Eli Lilly’s $6.3B bet on Centessa Pharmaceuticals’ orexin drug candidates and Biogen’s acquisition of Apellis Pharmaceuticals. For context, there were a total of seven deals of that magnitude in all of 2025.

Across March, biopharmas lined up 10 acquisitions worth up to $31.5B, per Endpoints News. Eight included contingent value rights or milestone payments—a dealmaking structure that has become more common in recent years. Year-to-date, Endpoints has tracked up to $48B in total potential deal value across 19 biopharma acquisitions. Société Générale tallied $126B in total deal value for all of 2025, well above the 10-year average of $85B. If the current pace holds, 2026 could surpass it.

🔹 Blackstone has collected $6.3B for its latest life sciences fund, which has already committed roughly $2B over the past year for an ADC at Merck, an anti-TL1A at Teva-partnered Sanofi, and other deals. The fund invests in approximately 3% of deals it evaluates, targeting therapies with $1B+ peak sales potential.

🔹 Anthropic acquired Coefficient Bio, a stealth biotech AI startup, for $400M in stock. Coefficient Bio was founded eight months ago by Samuel Stanton and Nathan C. Frey, both from Genentech’s Prescient Design group. The team of roughly 10 people was using AI for drug discovery planning, clinical regulatory strategy, candidate identification, and biomolecule modeling. As Jonah Probell of Lexi Ventures noted, the deal generated a reported 38,513% paper IRR for Dimension, the VC firm that backed them. Anthropic’s head of Healthcare & Life Sciences Eric Kauderer-Abrams stated the goal rather plainly: Anthropic wants a meaningful share of all life science work globally to run on Claude. The team is expected to join Anthropic’s health and life sciences unit.

🔹 Generare, a Paris-based techbio building a discovery platform around microbial DNA, raised €20M in a Series A co-led by Alven and daphni. The company identifies biosynthetic gene clusters in microbial genomes, expresses them in lab systems, and characterizes the resulting molecules, reporting over 200 previously uncharacterized small molecules per cycle versus an industry baseline of ~45. Most screening libraries draw from an estimated 3% of accessible chemical space, and Generare is betting that new chemistry from unexplored microbial genomes, not just more data from known chemistry, is what AI-driven drug discovery needs. The team of ~25 aims to scale output to 2,000+ molecules by 2027. Founded in 2023 by Guillaume Vandenesch and Vincent Libis.

🔹 Scala Biodesign, an Israeli computational protein design platform, raised $16M in a Series A led by Grove Ventures, joined by TLV Partners, Deep Insight, and the Israel Innovation Authority. The platform combines LLMs, physics-based modeling, and evolutionary data to help clients design and optimize proteins for biologic, enzyme, antibody, and vaccine therapies. It aims to become routine infrastructure across the full development pipeline rather than a one-off discovery tool.

🏛️ Bioeconomy & Society

(News on centers, regulatory updates, and broader biotech ecosystem developments)

The EMA just issued its first draft qualification opinion for virtual control groups as a New Approach Methodology.

Context is rather narrow: rat dose-range finding studies, non-GLP, replacing concurrent control groups with virtual comparators built from historical data. It’s a narrow starting point, but it sets a regulatory blueprint for every study design where live controls exist by convention rather than scientific necessity.

Stefano Gaburro, who leads the Pistoia Alliance Minimal Metadata Set Working Group, noted that the real bottleneck was never the algorithm, but the metadata. Inconsistent annotation across institutions prevented historical control data from being pooled and reused. Pistoia’s MNMS framework, notes Stefano, was built specifically to create the data infrastructure that virtual control groups require. A cross-pharma proof of concept with five companies, using 24/7 home cage monitoring data, is ongoing. The EMA consultation runs until May 12, 2026.

Around the same time, Joseph Wu and colleagues published a review in Cell on NAMs for drug discovery, framing the evolution from 2D stem cell systems through 3D organoid models to AI-driven in silico platforms. The review argues that the next 30 years of drug development will see animal models transition from a central role to a supporting one, following the 3Rs principle, replaced increasingly by human-centric NAMs powered by multi-omics data and AI pipelines. Between the EMA opinion and this review, the regulatory and scientific architecture for reducing animal use in drug development is being built in parallel.

⚙️ Other Tech

(Innovations across quantum computing, BCIs, gene editing, and more)

🔹 Bone marrow by the moon—NASA’s Artemis II mission is carrying AVATAR (A Virtual Astronaut Tissue Analog Response)—organ-on-chip devices containing bone marrow cells from crew members to study how deep space radiation and microgravity affect human health during the approximately 10-day lunar journey.

Bone marrow was selected for its radiosensitivity and centrality to immune function. The battery-powered payload, developed by Space Tango, will maintain automated environmental controls during flight. Upon return, Emulate researchers will use single-cell RNA sequencing to compare flight samples against ground-based controls. NASA has extended organ-on-chip viability to six months minimum to enable observation of long-term disease and treatment responses.

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🚀 A New Kid on the Dura

Epia Neuro launched from San Francisco this week with a minimally invasive brain-computer interface designed to help stroke survivors regain motor function by reading what the brain intends to do and translating that into movement.

The founder is Michel Maharbiz, who previously built iota Biosciences, a bioelectronics startup later acquired by Astellas. The team spans engineering and neuroscience, with backgrounds in both research and clinical system development.

Stroke is the entry point: roughly 690,000 cases annually in the U.S., with around 60,000 patients potentially eligible for device-based intervention. Epia plans first-in-human demonstrations with the Department of Neurosurgery at Lenox Hill Hospital.

The technical differentiator is a read/write architecture where the device can both record brain signals and deliver stimulation, spanning cortical and deeper brain regions. This contrasts with earlier generations of primarily read-only BCIs. The implant is placed within the skull without penetrating brain tissue via a roughly one-hour surgery. Onboard algorithms adapt to each patient’s activity patterns over time, with wireless recharging and upgradeable architecture built in for long-term use.

Early on, the device supports rehabilitation, reinforcing intended movements through feedback and stimulation while recovery is still possible. As recovery plateaus, it shifts into an assistive role, driving wearable external devices for tasks like gripping objects. The company is also signaling longer-term ambitions in cognitive decline, Parkinson’s, and neuropsychiatric conditions.

The BCI landscape we’re tracking is moving fast. Just last month we covered how Neuracle’s minimally invasive device became the first commercially approved BCI in China, followed by increased funding activity:

• StairMed raised $73M for its robot-inserted flexible electrode system;

• Gestala raised $21M for a non-invasive ultrasound-based platform.

• In the U.S., Merge Labs (backed by Sam Altman) raised $252M for ultrasound-based BCIs

• Nia Therapeutics received FDA breakthrough device designation for an AI-guided memory loss BCI

• Neuralink, the largest actor, plans high-volume production and a near-fully automated implantation workflow in 2026 following a $650M Series E.

We covered the broader neurotech landscape in our Neurotech Review here.

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Read also:

Three Big Ideas in Aging Research That Could Shift the Therapeutic Landscape

Cancer as a Data Problem: What AI Is Doing in Oncology

GTC 2026 brought that energy straight into life sciences. NVIDIA CEO Jensen Huang's keynote framed agentic AI as the next platform shift, and the life sciences sector seems to be taking that literally. Agents are now everywhere you look: IQVIA rolled out over 150 of them across clinical and commercial workflows, Insilico added one for translational biology, Latent Labs has one designing antibodies from text prompts, and NVIDIA itself unveiled a full surgical robotics stack.

We now have two top pharma companies in an apparent compute arms race within the same quarter. Roche's 3,500+ Blackwell GPUs positioned as the largest announced pharma GPU footprint, right on the heels of Lilly‘s LillyPod supercomputer and its $1B co-innovation lab with NVIDIA announced at JP Morgan.

Startups are matching the energy: Xaira, backed by nearly $1 billion in funding, has now launched a 4.9-billion-parameter virtual cell model, Earendil, since our note of their plans for a ~$500M IPO last week, pulled in $787 million for AI-driven biologics, and the overall VC wave into AI-native bio keeps building. Speaking broadly of AI-native startups, Huang pointed to ~$150 billion in venture funding flowing their way last year.

Hi! This is BiopharmaTrend’s weekly newsletter, Where Tech Meets Bio, where we explore technologies, breakthroughs, and cutting-edge companies.

If this newsletter is in your inbox, it’s because you subscribed, or someone thought you might enjoy it. In either case, you can subscribe directly by clicking this button:

Subscribe now

🤖 AI x Bio

(AI applications in drug discovery, biotech, and healthcare)

🔹 Roche launched its own large-scale AI computing hub for drug discovery, development, and manufacturing after buying 2,176 Blackwell GPUs (bringing its total capacity above 3,500), supporting model training, biological data analysis, lab-linked workflows, and factory digital twins.

🔹 AI reasons about protein function — Arc Institute researchers introduced a multimodal model for protein function prediction that combines protein embeddings with language-model reasoning, beating prior public benchmarks, generating step-by-step biological explanations, and earning preference over curated annotations in 79% of expert comparisons.

🔹 At GTC, UCL and partners unveiled what they describe as a first hybrid quantum-GPU biomolecular simulation pipeline, combining quantum hardware with 120 H100 GPUs to model a major drug-target protein class at realistic biological scale.

🔹 Xaira Therapeutics launched a 4.9-billion-parameter AI virtual cell model that predicts cellular responses to genetic perturbations, trained on 25.6 million perturbed single-cell transcriptomes and reported to generalize to unseen biological contexts.

🔹 Latent Labs, after coming out of stealth last February, launches an AI agent that designs therapeutic antibodies from text prompts in hours, reporting ~67% success with nanomolar affinities.

🔹 At GTC 2026, NVIDIA launched Proteina-Complexa, a generative protein binder design model accepted as an oral presentation at ICLR 2026, built on Proteina (ICLR 2025) and extending it to full atomistic binder design with inference-time compute scaling. Novo Nordisk, Manifold Bio, and Viva Biotech participated in wet lab validation. Other announcements also included a surgical robotics stack: Open-H (700+ hours of surgical video), Cosmos-H (synthetic surgical data generation), GR00T-H (vision-language-action model for clinical robotics), and Rheo (a simulation framework).

🔹 IQVIA launched a unified AI platform to support clinical, commercial, and real-world decision-making, with 150+ deployed agents, and 100+ AI patent filings.

🔹 Open-source protein design workflow — Dyno Therapeutics unveiled an open-source agentic AI suite for protein binder design that pairs generative models with lab-trained predictive filters.

🔹 Agentic AI for translational biology — Insilico Medicine launched a new AI analysis agent for therapeutic discovery that lets biologists use natural language to run multi-omics and bioinformatics workflows, drawing on 140+ scientific skills and 1,000+ tools.

🔹 To fix the experimental bottleneck, Arctoris opened a new biophysics center in the UK to generate drug-binding and protein-quality data at scale for AI-driven discovery.

🔹 Building a massive genomics dataset for AI — Basecamp Research launched a Trillion Gene Atlas to collect genomic data from 100+ million species, aiming to expand biological training data for AI models through global sampling.

🔹 AlphaFold adds protein interaction context — the AF database now includes 1.7 million predicted homodimer structures from an initial 30 million candidates, expanding beyond single proteins to capture how identical protein pairs may interact across 20 key organisms.

🔹 Automated labels can quietly worsen medical AI bias — Researchers found breast tumor segmentation models do far worse on younger patients for qualitative reasons, and training on machine-generated labels can amplify the disparity by ~40% while biased benchmarks can hide the damage.

💰 Money Flows

(Funding rounds, IPOs, and M&A for startups and smaller companies)

🔹 Earendil Labs raised $787 million to scale AI-driven antibody and biologics discovery, expand teams, and advance a pipeline of 40+ programs, including one program ready for Phase 2 and multiple regulatory filings planned for 2026-2027.

🔹 Neuracle‘s commercial approval triggered a funding wave across China’s BCI sector: Shanghai-based StairMed raised ~$73M for its robot-inserted flexible electrode approach, while Gestala pulled in $21M just two months after launch for a non-invasive ultrasound BCI platform.

🔹 Verily raised $300M and became an independent company, using the capital to expand its AI-ready health data platform, consumer health tools, and clinical/research agents while Alphabet shifts to a minority stake.

🔹 Rivia raised €13M to build AI agents for clinical trial operations, expanding its unified data platform to proactively flag risks, anomalies, and enrollment issues in regulated trial workflows.

⚙️ Other Tech

(Innovations across quantum computing, BCIs, gene editing, and more)

🔹 Nia Therapeutics received FDA Breakthrough Device status for an AI-guided brain implant aimed at memory loss after traumatic brain injury, with a study reporting a 19% recall improvement by detecting poor memory-encoding states and delivering targeted stimulation.

🔹 Brain activity restored after deep freezing — researchers in Germany revived key functions in vitrified mouse brain slices after storage at up to −150 °C for as long as seven days, preserving neuronal firing, metabolism, and synaptic plasticity.

🔹 ReVision Implant won FDA Breakthrough Device status for a brain-stimulation visual prosthesis designed to restore functional vision in blindness beyond the retina, with planned surgical testing in October 2026.

🔹 Mind typing nears everyday speeds — Massachusetts General Hospital researchers enabled two people with paralysis to type on a standard digital keyboard using brain implants that decode imagined finger movements, reaching 22 words per minute with low error after training on just 30 sentences.

🔹 “No, we haven’t uploaded a fly yet”—scientists push back on viral claims of a digital fruit fly, arguing the demo stitched together existing brain and body models with pre-programmed behaviors, falling far short of true whole-brain emulation.

🏛️ Bioeconomy & Society

(News on centers, regulatory updates, and broader biotech ecosystem developments)

🔹 FDA pushes shift beyond animal testing, issuing draft guidance to validate non-animal methods like organoids, lab models, and simulations for drug safety testing, aiming to replace animal studies with more predictive human-relevant data and speed clinical development.

🔹 NIH committed $150M investment in human-based research to reduce reliance on animal models, framed around its Complement-ARIE program.

🔹 arXiv’s move out of Cornell—Science reports it will become an independent nonprofit on July 1, 2026.

🚀 A New Kid on the Block

(Emerging startups with a focus on technology)

Former GSK AI Leaders Launch Stealth Oncology Startup — Shane Lewin and Nick Peterson, who both recently left GSK after six years building the pharma giant’s AI/ML and research data infrastructure, have co-founded Clockwork Bio (Lewin as CEO, Peterson as CTO).

The stealth-mode startup is building an AI-native platform for preclinical oncology drug discovery, arguing that conventional pipelines designed around heritable genetic targets fail for cancer, where disease is driven by epigenetic regulation, cell signaling, and lineage switching.

Their approach combines active learning, AI-based phenotyping, and emerging modalities to force cells between healthy and diseased states in vitro while learning the underlying biology at scale.

No funding or team details disclosed, formal product launch expected later in 2026.

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Read also:

Three Big Ideas in Aging Research That Could Shift the Therapeutic Landscape

Cancer as a Data Problem: What AI Is Doing in Oncology

Caveat: the science of personalized oncogenomics isn't new, and the AI mostly helped a non-specialist navigate tools that already exist. This still required serious money and a lab willing to help, but the tooling is getting faster and more navigable even for non-specialists.

Meanwhile, Google’s diagnostic AI ran pre-appointment patient interviews at a real clinic—100 patients, no safety incidents, accuracy allegedly on par with primary care doctors. OpenFold Consortium now released its full training pipeline with datasets, weights, and training code.

On the money side: an AI-driven protein engineering startup Earendil is reportedly eyeing a Hong Kong IPO that could raise up to $500M. Breakout Ventures closed $114M for early-stage neuroscience and biomedical AI. Seemay Chou launched Radial, a $500M effort aimed at the scientific data infrastructure that AI tools depend on but nobody wants to fund. And Owkin spun out its diagnostics work as Waiv with $33M for oncology stratification.

Elsewhere: China cleared the first minimally invasive BCI for everyday home use. Researchers published a molecule-level simulation of a full bacterial cell cycle. Also—arXiv is separating from Cornell to go independent and is looking for a CEO.

Hi! This is BiopharmaTrend’s weekly newsletter, Where Tech Meets Bio, where we explore technologies, breakthroughs, and cutting-edge companies.

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🤖 AI x Bio

(AI applications in drug discovery, biotech, and healthcare)

🔹 A pet owner used AI to build his dog a custom cancer vaccine and it worked. After surgery and chemo failed, an Australian engineer used ChatGPT to map a treatment plan, AlphaFold to identify tumor targets, and eventually convinced a university lab to manufacture a personalized mRNA vaccine in under two months.

🔹 Microsoft launched Copilot Health, a dedicated health companion that pulls together wearable data, medical records from 50,000+ US providers, and lab results into one place.

🔹 An AI doctor’s assistant just passed its first real-world test. Google‘s diagnostic AI conducted pre-appointment patient interviews at a real clinic—100 patients, zero safety incidents, and its diagnostic accuracy matched that of primary care doctors.

🔹 The OpenFold Consortium released the complete OpenFold3 training pipeline—datasets, weights, training and inference code—making this biomolecular complex prediction system fully reproducible and extensible and not just inference-accessible. Antibody-antigen prediction remains an open frontier and a stated 2026 priority.

🔹 AI beats specialists at rare disease diagnosis. A Nature-published agentic system integrating 40+ clinical and genomic tools outperformed experienced physicians, with traceable, evidence-linked reasoning; over 500 institutions signed up since its July 2025 launch.

🔹 Structural confidence scores don’t predict cellular function. A 12,000-design CAR-T binder benchmark found only 5.9% of AI-generated designs worked (rising to 10.6% with basic sequence filters) while standard metrics (pLDDT, ipTM, Rosetta) had near-zero predictive power. The core failure: RFdiffusion’s helix-heavy backbones combined with ProteinMPNN’s bias toward lysine/glutamate produce sequences that stall ribosomes in cells. The one top-performing team folded designs in full CAR context rather than isolation.

🔹 Weeks of compute compressed into hours. Schrödinger says it uses Google Cloud GPUs to screen billions of compounds in a single weekend.

🔹 A living cell, simulated molecule by molecule. Researchers built a complete 4D whole-cell model of a minimal bacterium covering gene expression, DNA replication, metabolism, and division across a ~100-minute cell cycle. Each simulated cell is unique due to stochasticity, and the model correctly predicts doubling time, ribosome counts, and daughter-cell variability.

🔹 Asimov Press published a beginner-accessible walkthrough on computational antibody design using BoltzGen.

🔹 BigHat Biosciences benchmarked 30+ protein language models for antibody design with wet lab validation and found that model choice matters less than how one uses it, and that even a few hundred experimental datapoints outperforms any zero-shot approach.

💰 Money Flows

(Funding rounds, IPOs, and M&A for startups and smaller companies)

🔹 AI drug discovery startup Earendil (combining generative protein engineering with wet lab work to develop biologics) is reportedly eyeing a Hong Kong IPO that could raise up to $500M, following Insilico Medicine‘s successful listing there last year. Partnerships with Sanofi, Wuxi XDC, and Wuxi Biologics already in place.

🔹 Breakout Ventures raised $114M for its third fund, targeting early-stage neuroscience and biomedical AI: lab automation, computational chemistry, and AI-powered research tools. Early bets include a lab robotics startup; their track record includes a $300M Halozyme acquisition and the first FDA-cleared rapid sepsis diagnostic.

🔹 DeepMind and MIT alumni raised $13.5M to build a platform that connects ideas across scientific fields, proposes testable hypotheses, and runs simulations before experiments.

🔹 Using salmonella to fight cancer. Salspera is raising $91M to fund a phase 3 trial of an engineered salmonella strain that delivers an immune-boosting signal directly into tumors, previously showing reduced tumor burden and improved survival in metastatic pancreatic cancer. A two-person team, an orphan drug tag, and a Nasdaq listing on the horizon.

⚙️ Other Tech

(Innovations across quantum computing, BCIs, gene editing, and more)

🔹 First invasive BCI for paralysis cleared for everyday use in China—a minimally invasive implant that sits above the motor cortex has been cleared for everyday home use in paralyzed patients, translating imagined hand movements into a robotic glove.

🔹 Large gene insertion without the toxicity. Massachusetts General Hospital researchers developed a genome editing approach using circular single-stranded DNA donors to achieve kilobase-scale gene integration, avoiding the immune response triggered by the double-stranded DNA donors traditionally required for large insertions.

🏛️ Bioeconomy & Society

(News on centers, regulatory updates, and broader biotech ecosystem developments)

🔹 The internet’s preprint library is going independent. arXiv—the free platform hosting 2.7 million scientific papers that the entire AI and life sciences research world depends on—is separating from Cornell University to become an independent nonprofit, and is hiring its first CEO at ~$300K. The academic community’s main worry is that independence means paywalls eventually follow.

🔹 UK bets £500M on homegrown AI. The government’s new Sovereign AI fund launches April 16, with life sciences as a priority sector.

🔹 A Middle Eastern shipping lane could empty American pharmacy shelves. Nearly half of U.S. generic prescriptions come from India, which depends on the Strait of Hormuz for 40% of its crude oil—a key input for drug manufacturing. Current stockpiles give 30–60 days of buffer, but a prolonged closure would hit generics hardest, as thin margins leave little room to absorb rising fuel and petrochemical costs.

🚀 A New Kid on the Block

(Emerging startups with a focus on technology)

🔹 Owkin spun out its diagnostics arm as Waiv with $33M in fresh funding, focused on AI-powered patient stratification tests for oncology—both in clinics and clinical trials.

🔹 AI in science is only as good as the infrastructure beneath it. Researcher Seemay Chou launched Radial, a $500M organization focused on modernizing how scientific data is generated, shared, and built upon—the “unglamorous” infreastucture and tools AI needs to deliver real value. Early projects include better methods for capturing protein dynamics.

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Three Big Ideas in Aging Research That Could Shift the Therapeutic Landscape

AI Protein Design: Platforms, Specialists, Modular Tools

The ‘Holy Grail’ of Digital Biology

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🤖 AI x Bio

(AI applications in drug discovery, biotech, and healthcare)

🔹 AI revives stalled drug program — Takeda is embedding AI across drug discovery after Boston AI partner Nabla helped rescue a failing preclinical antibody.

🔹 ByteDance challenges AlphaFold with new protein folding model — The TikTok maker’s SeedFold builds on AlphaFold 3 and sets new benchmarks on FoldBench, inheriting an AlphaFold3-style architecture while widening the Pairformer and introducing linear triangular attention.

🔹 Tahoe Therapeutics, Arc Institute, and Chan/Zuckerberg’s Biohub have partnered to create the largest open dataset for virtual cell modeling, combining 120M single-cell profiles and 225K drug-patient perturbations for AI models of cellular behavior and treatment response.

🔹 Recursive Language Models reframe AI reasoning limits — Bo Wang (Xaira Therapeutics, UHN) highlights how Recursive Language Models (RLMs) challenge the belief that long-context AI reasoning is just a memory problem, instead enabling scalable, structured thinking by programmatically decomposing tasks rather than ingesting more data (an approach his team is testing in clinical trial applications).

🔹 Industrializing tissue replacement — Cambridge-based Cellino and Polyphron partner to combine AI-driven iPSC manufacturing with autonomous tissue engineering to scale personalized regenerative therapies through end-to-end, reproducible production systems.

🔹 Natera partners with NVIDIA to train large-scale foundation models on longitudinal oncology datasets for improved diagnostics, immunotherapy prediction, and biomarker discovery with high-performance AI infrastructure.

🔹 Leadership moves in AI-Driven biotech — Novo Nordisk appointed Anja Leth Zimmer as its first Chief AI Officer; BioMarin named Arpit Davé as Chief Digital and Information Officer, and Rune Labs tapped former Tempus executive Amy Gordon Franzen as CEO to lead expansion of its AI neurology platform.

🔹 CytoReason launches LINA, an NVIDIA-powered AI agent built on computational disease models to generate reproducible analyses and insights.

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🚜 Market Movers

(News from established pharma and tech giants)

🔹 Google expands medical AI suite with imaging and speech models — releasing MedGemma 1.5 for high-dimensional medical imaging and MedASR for speech recognition, alongside a $100K Kaggle hackathon centered on open-source clinical AI development.

🔹 Pfizer taps SF-based Gordian Bio’s in vivo and ML platform for obesity targets — to screen gene targets in visceral fat using its in vivo mosaic platform and Pythia machine learning engine to identify therapeutic interventions for obesity with tissue-specific precision.

🔹 BMS taps Immunai’s AI to decode immune responses — Bristol Myers Squibb partners with US-based Immunai to analyze clinical trial data using its AI-powered immune atlas; Immunai now provides free single-cell multi-omic profiling to Mount Sinai, Weill Cornell, and Massachusetts General Hospital.

🔹 Illumina launches Billion Cell Atlas for AI drug discovery — launching the first phase of a 5-billion-cell CRISPR perturbation dataset, partnering with AstraZeneca, Merck, and Eli Lilly to train AI models and validate genetic drug targets across hard-to-treat diseases.

🔹 Insilico Medicine brings AI drug discovery model to Microsoft platform — integrating its Nach01 foundation model with Microsoft Discovery to enable scalable, AI-native workflows for molecular design and predictive analysis in drug discovery.

🔹 JPM 2026: CEOs of Novo Nordisk, BMS, and Pfizer lay out their bets — Novo Nordisk’s Mike Doustdar called 2026 a “year of price pressure,” citing the Trump administration’s GLP-1 pricing deal and growing competition. Bristol Myers Squibb’s Chris Boerner previewed 10 potential product launches by 2030, with 11 key readouts expected this year. Pfizer’s Albert Bourla reaffirmed the company is “all in on obesity,” with 10 late-stage trials planned for Metsera assets and strong expectations from the growing cash-pay market.

💰 Money Flows

(Funding rounds, IPOs, and M&A for startups and smaller companies)

🔹 Nearly $1B in new biotech VC funds launch globally — Andreessen Horowitz commits $700M to U.S. biotech innovation with a focus on AI applications, while Germany’s BioNTech and Penn back a $50M fund for Pennsylvania startups, and France’s Servier launches a €200M fund targeting European oncology and neurology firms.

🔹 Denmark’s Novo Nordisk Foundation will invest ~$856M over nine years into BioInnovation Institute to expand life sciences and AI-driven startups across Europe, scaling its support beyond Denmark and into quantum and reproductive health.

🔹 AstraZeneca acquires Boston’s Modella AI to embed foundation models and AI agents across its global cancer pipeline for biomarker discovery, pathology analysis, and trial design through multimodal data integration.

🔹 OpenAI acquires health records startup Torch for reported $100M — Torch built a “medical memory” that unifies fragmented health data for AI use. Its four-person team joins OpenAI to support ChatGPT Health, a new initiative aimed at helping users analyze and manage personal health through the chatbot.

🔹 Nuclera extends Series C to $87M for antibody engineering platform — UK- and US-based company raises an additional $12M to expand its eProtein Discovery system with full-format antibody expression and validation, aiming to generate high-quality datasets for AI-driven biologics discovery.

🔹 $80M for AI-driven protein interaction drugs — New York-based Proxima raises $80M to develop proximity-based medicines using generative AI and structural proteomics to design small molecules that modulate protein-protein interactions; backed by DCVC, Nvidia, and Roivant.

🔹 Boston/Tel Aviv-based Converge Bio raises $25M to expand generative AI systems trained on DNA, RNA, and protein sequences, enabling pharma clients to design antibodies and optimize protein yield with integrated predictive and physics-based validation.

🔹 Juvena Therapeutics secures $33.5M Series B to progress its tissue-restorative biologics pipeline and AI discovery platform, backed by Bison Ventures, Eli Lilly, and others, amid ongoing Phase 1 trials for muscle regeneration.

🔹 $50M to scale human cell programming — UK-based bit.bio secures $50M Series C to expand its ioCells platform for drug discovery and toxicology, generating AI-ready human cell data to reduce animal testing and improve preclinical model accuracy.

🔹 Lux Capital raises $1.5B for frontier tech and AI — NY-based Lux Capital closes its largest fund to date amid a weak VC market, doubling down on AI, defense, and deep tech after early wins with Anduril, Hugging Face, and Recursion, and Auris Health, which sold to J&J for up to $6B.

🔹 UK-based Allos secures $5M to apply Causal AI to the redesign of hard-to-genericize small-molecule drugs.

🔹 Avenue Biosciences secures $5.7M to scale its AI-enabled platform that modulates the secretory pathway using engineered signal peptides, improving yield and manufacturability of complex therapeutic proteins.

⚙️ Other Tech

(Innovations across quantum computing, BCIs, gene editing, and more)

🔹 MIT spotlights biotech’s leap into personalized and regenerative medicine — In its Ten Breakthrough Technologies (2026), MIT Technology Review highlights advances like personalized base editing in infants, embryo genetic scoring, and gene “resurrection,” signaling a shift toward bespoke gene therapies, AI-integrated biology, and deeper manipulation of human development and disease.

🔹 Vascularized retinal organoids achieve full light-signal transmission. Researchers at University Hospital Bonn and partners integrated endothelial cells into retinal organoids, enabling nutrient flow, boosting ganglion cell survival, and—for the first time—establishing functional light pathways from photoreceptors to optic nerve cells in vitro.

🔹 CurifyLabs debuts 3D printer for scalable drug compounding that enables faster, modular preparation of personalized medicines in pharmacies

🏛️ Bioeconomy & Society

(News on centers, regulatory updates, and broader biotech ecosystem developments)

🔹 China’s biotech VC diverges on exits, incentives, and scale — a new BridgeCross Bio piece dissects how China’s life science venture ecosystem differs from the West, drawing on perspectives from firms like Sofinnova Partners, Sunstone Life Science Ventures, Apricot Capital, and a top Chinese VC; key themes include RMB fund dominance, founder resistance to M&A, and why Western VCs remain cautious despite China’s speed and scale.

🔹 Big Pharma eyes M&A surge as patents expire. With nearly $180B in drug revenues set to go off patent by 2028, major pharma companies are ramping up dealmaking, fueled by regulatory clarity, surging biotech stocks, and pressure to replenish pipelines, setting the stage for a wave of large-scale acquisitions in 2026.

🔹 JPM shifts from megadeals to meetings — Despite no major announcements, the 2026 J.P. Morgan Healthcare Conference remained packed with biopharma networking, as companies increasingly drop news before the event or pivot toward smaller conferences, possibly reflecting a broader evolution in how and where the industry communicates.

🚀 A New Kid on the Block

(Emerging startups with a focus on technology)

🔹 Merge Labs emerges with $252M to develop non-invasive brain-computer interfaces — Backed by OpenAI, Bain Capital, and Gabe Newell, the startup is building ultrasound-based neurotechnology as an alternative to implanted BCIs. A spinoff from Forest Neurotech, Merge aims to interpret and modulate brain activity via blood flow dynamics, with AI foundation models aiding in signal interpretation.

🔹 Tahoe Therapeutics and Alloy Therapeutics form a new company to develop antibody-drug conjugates targeting novel tumor antigens discovered via Tahoe’s AI-powered single-cell platform, combining target discovery with Alloy’s ADC engineering and venture creation infrastructure (namely, 82VS).

Reality Check on AI Antibody Design

We recently read a head-to-head analysis comparing several of the current de novo antibody design models: Nabla Bio’s JAM-2, Chai Discovery’s Chai-2, Absci’s Origin-1 (built on Boltz), and the Baker Lab’s RFAntibody.

Traditional antibody discovery still largely means immunizing mice, waiting for an immune response, generating hybridomas, and screening thousands of candidates. That process typically takes 12–24 months, and many programs never produce a viable lead.

In short, JAM-2 and Chai-2 come out ahead overall, with relatively small differences between them. JAM-2 looks stronger on reported hit rates and affinities across many targets, while Chai-2 stands out on structural validation and target breadth. But the more interesting point is how much better all of these systems look compared with what was considered state of the art roughly a year ago, both in speed and in the likelihood of getting binders at all.

There is particular emphasis on GPCRs, which account for about 34% of approved drugs but are almost exclusively targeted by small molecules rather than antibodies. JAM-2 reports low-nanomolar binding to CXCR4, but Chai-2 reports a CXCR4 antibody with measurable agonist activity based on downstream signaling assays—“to design an antibody that not only finds its target but also induces a specific functional response represents capability that simply didn’t exist before.”

Highlights

• Hit rates at 39%

• Weeks-long timelines vs 12-24 months traditionally

• AI-designed antibody with GPCR-activating activity (a first)

• KRAS mutant discrimination at the single-amino acid level

That said, the analysis is explicit about the limits of what’s been shown so far:

• No in vivo data. None of the platforms report mouse PK, xenograft studies, or other in vivo proof of concept.

• Immunogenicity remains untested. The designed CDRs are novel, and anti-drug antibody risk is unknown. Clinical data is still years away.

• Manufacturing is unvalidated. There is no public evidence yet of large-scale CHO production for these designs.

• Access is limited. JAM-2, Chai-2, and Origin-1 are proprietary, while RFAntibody is open but requires thousands of designs per target.

• Hit rate does not equal a drug. Affinity maturation, epitope tuning, and format engineering remain largely downstream work.

The expectation is that the next meaningful milestones are in vivo data and IND-enabling work, rather than further gains in raw hit rates. At least one group signals that those steps may be approaching, but for now that’s still prospective.

How AI Brings Drug Testing Closer to Human Biology

In her latest column on rare diseases at BiopharmaTrend, our regular contributor Louise von Stechow, PhD, examines how drug testing is gradually shifting away from animal models toward more human-relevant, technology-driven approaches, with AI playing an increasingly visible role. Drawing on examples from toxicology, ADME, and disease modeling, she outlines why animal studies often fall short in predicting human outcomes, particularly in rare and multisystem disorders where suitable models are limited or absent.

Advanced human cell systems, organoids, and organ- and patient-on-chip platforms can capture patient-specific biology, but remain labor-intensive and reductionist. AI models are increasingly layered on top of these systems and historical clinical data to scale predictions, flag toxicity risks such as liver or cardiac injury, and support ADME and dose modeling in ways that are difficult to achieve with animal studies alone.

Von Stechow situates these technical developments in a changing regulatory and industrial context, where agencies in the US and EU are beginning to accept non-animal methods in defined settings, and both public initiatives and biotech companies are investing in computational and hybrid alternatives to traditional preclinical testing. The overall picture is one of incremental change, with AI and human-based models used to focus animal studies where they are most informative.

The full column, with detailed case studies and examples, is available on our website here.

2026 Biotech & Clinical AI Reports

Two reads that came across our desk recently: one is Benchling’s 2026 Biotech AI Report, which is based on data from 100 biotech and biopharma organizations already using AI in R&D and is trying to separate “daily driver” use cases from the stuff that still stalls out on data and workflow friction. The other is ARISE’s State of Clinical AI Report 2026, positioned as an annual synthesis of what is holding up in real clinical settings and what is not, with an emphasis on evaluation quality.

Benchling: 2026 Biotech AI Report

Benchling posits this as a November 2025 survey of about 100 biotech and pharma orgs actively using AI in R&D, and explicitly says it is not meant to represent “general industry sentiment,” but a view into AI front-runners in the U.S. and Europe.

A few takeaways:

• The earliest “wins” are workflow-native use cases with data that is comparatively clean and easy to sanity-check. They call out literature review (76% adoption), protein structure prediction (71%), scientific reporting (66%), and target identification (58%).

• Adoption drops in more complex, regulated, or harder-to-validate domains like generative design, biomarker analysis, and ADME. The report points to data being scattered, incomplete, and hard to validate as a core limiter.

• Their “planned growth” list leans toward more integrated systems, including workflow orchestration, manufacturing optimization, multimodal models, and “co-scientists.”

• They claim AI is becoming a default interface for scientists, reporting 89% using copilots or reasoning tools as a first stop for interrogating and synthesizing data.

• On org design, they report internal upskilling as the top talent source (67%), versus hiring from tech (21%).

• On impact, they report that 50% “report faster time-to-target today” and 56% “expect cost reductions within two years,” explicitly tying the latter to scaling automation and agentic workflows.

• On what breaks pilots, they state the number one reason AI pilots fail is challenges with data quality.

ARISE: State of Clinical AI Report 2026

ARISE describes itself as an interdisciplinary research network, founded in 2024 as a collaboration between clinicians and researchers across multiple academic medical centers, focused on evaluating clinical AI through real-world studies, open benchmarks, and reproducible methods. In their positioning, the State of Clinical AI Report 2026 is framed as an inaugural annual synthesis that pulls together the current deployment landscape, the state of evaluation quality, and what kinds of evidence they think should come next.

Context

According to ARISE, AI is already embedded across healthcare, with 1,200+ FDA-cleared tools and 350,000+ consumer apps contributing to a $70B market, while only a minority of these systems have undergone peer-reviewed evaluation.

Among 691 FDA-cleared AI/ML medical devices cleared between 1995 and 2023, more than 95% went through the 510(k) pathway, which relies on equivalence to existing devices, including devices the report characterizes as sometimes being cleared on suboptimal evidence.

ARISE adds that FDA device summaries frequently lack key study details, reporting that about 50% omitted study design, 53% did not include sample size, and fewer than 1% reported patient outcomes. They also report that 95% of summaries did not include demographic data and 91% did not include bias assessments, and they frame these gaps as relevant to safety and equity in real-world use.

The report’s six key themes are:

• Models made major leaps in actionable prediction and autonomous clinical reasoning, but the “jagged frontier” persists, including brittleness around uncertainty.

• Real-world task evaluation is positioned as a prerequisite for trustworthy clinical AI, with a push beyond traditional QA-style benchmarks.

• A shift from better models to better systems, including methods like multi-agent orchestration, multimodal models, and reasoning fine-tuning, with trade-offs.

• Workflow design matters as much as model capability, with explicit attention to failure modes, automation bias, and deskilling risk.

• Patient-facing AI is treated as its own category, where outcome measurement and safeguards are central and patients are not assumed to provide oversight.

• The report argues the time for context-specific prospective trials is now, noting that randomized prospective trials have already started and framing 2026 as the next wave of evidence.

JPM Takeaways: Less Deals, More Assets

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As we close out the year, thank you for reading us in 2025. Wishing you a good holiday season and a strong year ahead!

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If this newsletter is in your inbox, it’s because you subscribed, or someone thought you might enjoy it. In either case, you can subscribe directly by clicking this button:

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If this newsletter is in your inbox, it’s because you subscribed, or someone thought you might enjoy it. In either case, you can subscribe directly by clicking this button:

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If this newsletter is in your inbox, it’s because you subscribed, or someone thought you might enjoy it. In either case, you can subscribe directly by clicking this button:

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