Weekly Tech+Bio #86: OpenAI Backs a Bet on “Rescuing“ Failed Drugs
ALSO: Enveda, Anew Labs and Varda raise over $850M between them; Genentech signs a $1.5B collaboration with Earendil; Neuralink pretrains on 50,000 hours of brain data and reports a BCI record, and mo
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In this issue: OpenAI's fund leads Biossil to unicorn status on a bet that failed drugs failed the wrong patients — Anthropic says it won't take its own programs into the clinic — Enveda, Anew Labs, Varda and Precision each raise $250M+ — Roche lays out a path to autonomous labs — optogenetics wins the Nobel — Xaira finally shows its antibody pipeline — a microbe built to live on Mars, and more…
Main Story
In April, Toronto-based Biossil emerged from stealth with $70M and a plan that sounded a bit contrarian for an AI company: instead of designing new molecules, buy or license the ones that failed in their own clinical trials.
About five months later, Biossil raised another $153M (C$216M) at a $1B valuation, this time led by the OpenAI Startup Fund, with Founders Fund, Quiet Capital, Modern Capital, Golden Ventures, Panache Ventures, Duke University’s endowment and the Abu Dhabi Investment Council participating. The money goes to expanding clinical development capacity and acquiring more drug candidates, with programs spanning sickle cell disease, idiopathic pulmonary fibrosis, glioblastoma, breast cancer and Alzheimer’s disease.
The idea is not classic drug repurposing. Repurposing looks for a new disease for an old molecule. Biossil’s approach is closer to asking whether the drug actually worked — just not for everyone in the trial. If an efficacy signal existed in a more precisely defined patient group, it may have been diluted across a broad trial population.
That reframes a familiar number. Pharma’s 90%+ clinical failure rate is usually read as a story about how hard biology is. Part of it, though, may be a story about the purely matchmaking side of things, and that is exactly the kind of problem where AI on rich clinical and molecular data could make a difference.
CEO Anthony Mouchantaf argues that AI-generated biomedical insights still have to be translated into the physical world: “That requires dedicated infrastructure capable of integrating AI and medicine together from discovery through development and into the clinic,” adding, “We’re building that infrastructure in the United States and Canada.”
The round also fits OpenAI’s broader push into life sciences. In April, it launched GPT-Rosalind, a reasoning model for biology, drug discovery and translational medicine, released as a research preview through its trusted access program with Amgen, Moderna, the Allen Institute and Thermo Fisher Scientific named as partners, on top of an earlier-announced drug discovery partnership with Novo Nordisk.
Biossil is not alone, though. We mapped the “drug rescue” category in our members’ deep dive — who else is working this angle (Lantern Pharma, Pathos AI, BPGbio, etc), what the Roivant precedent teaches, and the approaches that didn’t work.
Nature’s chemistry, AI-ranked — Enveda raised a $311M Series E at a $2B valuation, led by Catalio Capital Management (total raised: $845M+). Proceeds go to three clinical programs, including one for severe skin conditions and one for maintaining weight loss after stopping GLP-1s.
ByteDance’s AI biotech prices in — Anew Labs raised $290M in its first external round at a reported $1.5B valuation, led by HSG, IDG Capital, GL-C Ventures/Hillhouse and 5Y Capital; ByteDance reportedly keeps 56%. Its work spans protein structure prediction, molecular design and antibodies, and its research includes small-molecule IL-17 inhibitors.
Drug manufacturing in orbit — Varda raised a $251M Series D co-led by Lux Capital and Natural Capital at a $1.6B valuation. It has completed six capsule reentries and booked 28 launches for 2027–2029.
BCI money keeps concentrating — Precision Neuroscience closed a $250M Series D co-led by Pershing Square and the Ackman Oxman Institute ($430M total). Its Layer 7 interface has been used in 100+ procedures across 18 institutions.
From models to medicines — Basecamp Research raised $140M from investors including S32, NVIDIA and Menlo’s Anthology Fund to launch a pipeline of AI-designed therapeutics, starting with medicines that “reprogram the body to repair itself.”
An AI-designed ADC enters the clinic — BigHat Biosciences raised a $75M Series C co-led by DFJ Growth and Premji Invest, mainly for BHB810, a CDH17-directed ADC for GI cancers that dosed its first Phase 1 patient in September.
Paying for delivery, not the drug — Novartis paid $125M for Sironax’s platform for moving large molecules across the blood-brain barrier, its latest BBB deal after BioArctic and SciNeuro.
Roche plans for lab autonomy — At Pharma Day, Roche laid out a path to autonomous labs, naming Medra and Anthropic as collaborators. It says 40% of recent pipeline decisions had a tracked AI contribution, and one program went from lead ID to clinical candidate in 18 months.
AbbVie, argenx, Lundbeck and Takeda joined Ginkgo Datapoints and Apheris’s developability consortium: ~10,000 antibodies tested to one standard train a shared model that each member fine-tunes privately. First data due early 2027.
AbbVie partnered with Valkai, starting in clinical sciences, as it sees pharma’s bottlenecks shifting from discovery to trials.
Boston-based Tremont AI emerged from stealth with Mass General Brigham-licensed foundation models and agents for drug safety pathology; funding not disclosed.
Antibodies “drug-ready from the start” — Xaira unveiled its X-Design platform and first programs in a company blog post: XA-1 (oncology) reached a progressable binder in seven weeks, and XA-4 hit a GPCR where conventional screens and llama immunization had failed.
Measurement before models — Sculpta Bio says its new RNA barcoding and sequencing approach improves isoform capture at scale across bulk, single-cell and spatial applications.
💁♀️ People 💁
Richard Bonneau left his role as Genentech’s global head of AI for drug discovery, Endpoints’ Andrew Dunn reported, the latest in a string of AI departures there including KyungHyun Cho, Stephen Ra and Nathan Frey; two sources cited tension with Aviv Regev, which Genentech denies.
According to Andrew Dunn’s report, Anthropic will not take its pipeline programs into human trials — and will not compete with its pharma customers
The quote, from Dunn’s report: “Anthropic does not plan to start human testing or Phase 1 trials for its pipeline programs, with Eric Kauderer-Abrams saying they will not compete with its pharma customers.”
Well, it is a big claim, and it changes a lot of assumptions about Anthropic’s current push into pharma, doesn’t it?
Time will tell, but it reads like a deliberate choice to stay a supplier rather than become a drug developer. And the customer list is growing fast: Bristol Myers Squibb put Claude in front of more than 30,000 employees earlier this year, Novo Nordisk signed its own deal (both covered in our last issue), and Roche named Anthropic as a collaborator on its path to autonomous labs at last week’s Pharma Day. If you are selling the reasoning layer into every big pharma’s R&D, owning clinical assets that compete with those same customers is an obvious conflict.
The open question is what “pipeline programs” Anthropic has at all, and what happens to them if they don’t go into humans — partnering or licensing would be the natural route, but the report, as quoted, doesn’t say.
A model retirement can be a regulatory event
Yujan Shrestha, MD, describes a problem few generative-AI device makers seem to plan for: Claude Sonnet 4 was released in May 2025 and retired in June 2026, about 13 months, shorter than most De Novo reviews, and one of his team’s API keys sent 10,236 failed requests in a single day afterwards.
Anthropic gives at least 60 days’ notice, less than the FDA’s 90-day goal for a Traditional 510(k), and swapping the model string can be a design change needing verification and validation. His advice: write model replacement into a PCCP (agreed with FDA in a Pre-Sub), keep a sequestered, labeled test set that can requalify a new model in days, and pin dated model snapshots rather than aliases that move on their own.
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