Is “Rescuing Failed Drugs with AI” a Category Now?
Inside the growing bet that AI can find the patients pharma's failed trials missed...
In April, a Toronto-based startup called Biossil came out of stealth with a total of $70 million in funding, co-led by Peter Thiel’s Founders Fund and OpenAI.
Their thesis is a bit different from what most AI biopharma companies are doing. Instead of designing new molecules, Biossil uses AI to dig through late-stage clinical failures and figure out which patient subgroups those drugs should have actually been tested on. Ten molecules were acquired while in stealth mode over three years. Trials running in everything from glioblastoma to Alzheimer’s.
That’s not drug repurposing in the classic sense — taking an approved drug and finding it a new indication, like thalidomide going from its original (disastrous) use to multiple myeloma, or metformin being studied in cancer.
Biossil is doing something more subtle: same molecule, same disease, just a more precisely defined subset of patients. The argument is that many drugs “failed” trials only in the “aggregate”, averaged across a heterogeneous population where a real signal got buried.
And they’re not alone. A cluster of companies, each with different technical approaches and varying levels of clinical evidence, is converging on a shared conviction: the pharma industry’s 90%+ clinical failure rate isn’t just a scientific problem. It’s partly an analytical one. The tools to find the right patients simply weren’t good enough, until now.
This is a piece about that convergence. We’ll map who’s doing what, how the approaches differ, what’s actually been validated, and whether the thesis holds up under scrutiny.
In this issue: The Logic of Drug Rescue — The Landscape: Who’s Doing What — A Closer Look at the Frontrunners — The Roivant Precedent — What Doesn’t Work (Yet) — Looking Ahead
💊 The Logic of Drug Rescue
Before we profile the companies, it’s worth understanding why this thesis is surfacing now and why it’s distinct from what came before.
Drug repurposing has a long history. Sildenafil started as a cardiovascular drug before becoming Viagra. Thalidomide was rehabilitated remarkably, decades after its teratogenic disaster in the 1960s, as a treatment for multiple myeloma. These are cases where an approved (or previously studied) molecule found a genuinely new indication.
What companies like a newcover Biossil, as well as more established players like Lantern Pharma, Pathos AI, and BPGbio, are doing is different. They are not necessarily changing the target disease, but the target patient. The hypothesis: within a trial population that produced a negative aggregate result, there are subgroups of patients who responded, and whose response was masked by the statistical noise of everyone who didn’t.
This isn’t a new idea conceptually. Post-hoc subgroup analysis has been part of clinical trials for decades. What’s new is the scale and sophistication of the AI being applied: multimodal foundation models trained on hundreds of petabytes of data, causal inference engines, spatial transcriptomics paired with pathology imaging, and multi-agent systems reasoning across publications and biomarker data.
The question is whether the analytical tools have finally caught up to the biological complexity… or whether we’re just building fancier ways to p-hack.
🗺️ The Landscape: Who’s Doing What
The companies working in this space share a thesis but diverge significantly in their technical approaches, therapeutic focus, and maturity. Here’s how the landscape breaks down.
⭐ Biossil
The freshest entrant. Biossil emerged from stealth with $70M co-led by Founders Fund and OpenAI, and a portfolio of ten molecules acquired quietly over three years. Their approach centers on reanalyzing late-stage clinical failures to identify patient subgroups where a meaningful treatment signal was hidden by population heterogeneity. Trials are running across glioblastoma, Alzheimer’s, and other indications.
What makes Biossil notable is the breadth of their bet: ten molecules across multiple therapeutic areas, funded by investors who have not traditionally played in biopharma. The OpenAI connection signals a belief that general-purpose AI capabilities, not just domain-specific biostatistics, can crack the patient stratification problem. That’s an interesting bet, though one that remains unproven clinically.
Details on their technical platform are still limited. We’ll be watching for specifics on what data they’re training on, how their models identify subgroups, and, most critically, whether their approach produces prospectively validated biomarkers or just retrospective correlations.
⭐ Lantern Pharma (Nasdaq: LTRN)
Lantern has been working on a related playbook for years, making it one of the most useful reference points for whether the thesis actually holds up in the clinic. Their RADR AI platform identifies abandoned clinical-stage drugs and matches them to patient subgroups most likely to respond. The focus is oncology.
The most tangible proof point right now is LP-300, a candidate in development for never-smoker non-small cell lung cancer (NSCLC) — a molecularly distinct type of the disease with poor outcomes and no approved therapies focused on this specific population. Lantern has just announced that the FDA raised no objections to key proposed protocol amendments for the Phase 2 HARMONIC trial, a meaningful de-risking step.
Three protocol changes are worth noting:
Focused enrollment on the EGFR exon 21 L858R subgroup — the molecular subset, accounting for roughly 40% of EGFR-mutant NSCLC globally, where current therapies leave the largest unmet need, according to the company, and where LP-300’s preliminary data have been most differentiated.
Extended dosing from a maximum of 6 to 8 cycles.
Transition from a randomized to a single-arm design — intended to accelerate enrollment and sharpen the clinical signal in a genomically defined subgroup.
On the AI side, Lantern has been developing withZeta.ai, a multi-agentic system derived from RADR that extends the platform’s mechanistic modeling capabilities. For LP-300, withZeta has been used to interrogate the drug’s mechanistic potential in the L858R setting — reasoning across publications and biomarker observations to surface insights that informed the development strategy. It’s an interesting example of AI agents being used not just for patient selection but for mechanistic hypothesis generation.
⭐ Pathos AI
Pathos is arguably the company with the deepest data moat in this space, thanks to its roots in the Tempus ecosystem. Founded by executives from Tempus (Eric Lefkofsky’s healthcare AI company), Pathos claims to have access to over 200 petabytes of multimodal oncology data linked to patient outcomes, reportedly 50 times the size of The Cancer Genome Atlas, the largest public genomic dataset in oncology.
Their thesis mirrors Biossil’s: drugs fail because they were tested in the wrong patients, with the wrong assumptions, in trials that couldn’t answer the real question “who benefits, and why?” But Pathos is focused exclusively on oncology and is building what it describes as the largest foundation model in the field.
In April 2025, Pathos entered a major three-way collaboration with AstraZeneca and Tempus to build a multimodal oncology foundation model, with $200 million in data licensing and model development fees flowing to Tempus. The foundation model is being built on Tempus's repository, which includes 7.3 million de-identified patient records, including 1.4 million with imaging data, 1.3 million with genomic information, and 260,000 with full transcriptomics profiles.
Pathos is also running its own clinical programs. In March 2025, they dosed the first patient in a Phase 1b/2a trial of pocenbrodib (a CBP/p300 inhibitor) in metastatic castration-resistant prostate cancer. They also acquired Known Medicine, which builds patient-specific 3D cell cultures and uses AI to predict drug responses prospectively — an attempt to close the loop between computational prediction and wet-lab validation.
Funding: $365 million in a Series D (May 2025), at a $1.6 billion valuation.
⭐ BPGbio
BPGbio takes a different technical angle: Bayesian causal AI, built on their NAi Interrogative Biology platform. Rather than relying on pattern recognition across large datasets, their approach aims to infer causal relationships, not just correlations, between patient characteristics and treatment response.
The platform integrates one of the largest non-governmental biobanks (over 100,000 clinically annotated patient samples) with deep multi-omic and clinical data, running on the Frontier exascale supercomputer at Oak Ridge National Labs.
The clearest demonstration of the approach in the drug rescue context comes from a multi-arm Phase Ib oncology study involving 104 patients across multiple tumor types. NAi’s models, trained on tissue and blood-derived multi-omic data, identified biological signatures predicting response to BPM31510 — and BPGbio used those insights to prioritize glioblastoma multiforme (GBM) and pancreatic cancer as the most compelling indications. The causal framing is that if the model can distinguish “patients who happened to respond” from “patients who responded because of a specific biological mechanism,” the resulting biomarkers should be more robust in prospective validation.
BPGbio is further along clinically than many AI-native biotechs. The company has completed enrollment in a Phase 2b GBM trial, with topline results expected in Q3 2026, and sought FDA guidance in late 2025 for a potential expedited regulatory path in GBM. They have multiple Phase 2 clinical trials underway — making them, by their own account, one of the first companies worldwide to advance multiple Phase 2 programs developed using causal Bayesian AI.
🔹 Others in the Neighborhood
The companies above aren’t the only ones in this territory. Several others touch the thesis from adjacent angles:
🔹 NOETIK trains AI models on massive datasets of paired pathology images and spatial transcriptomics to find hidden biological subtypes among trial participants and predict which patients will respond. The pairing of imaging and spatial transcriptomics is technically ambitious and could surface subgroups invisible to genomics-only approaches.
🔹 Ignota Labs (London) takes a complementary but distinct approach. As CEO Sam Windsor has noted, sometimes the drug just isn’t good enough and Ignota focuses on fixing fundamental safety issues in the chemistry itself to give failed drugs a second chance. Patient stratification and molecular optimization are different interventions for the same problem (the 90%+ failure rate), and both are needed.
🔹 Formation Bio (New York, valued at ~$1.7 billion) acquires stalled clinical-stage drugs and uses AI to run trials more efficiently, optimizing patient recruitment, protocol design, and site management. In November 2024, they launched Muse, an AI tool for clinical trial recruitment, in partnership with OpenAI and Sanofi. Formation’s overlap with the “drug rescue” thesis is real but less precise: they’re improving trial execution, not fundamentally reanalyzing who should be in the trial. Closer to operational arbitrage than to computational patient stratification.
🔹 Origent Data Sciences uses machine learning to build patient-level predictive models for disease progression, then identifies cohorts within failed trials where treatment effects can be demonstrated. Their ForecastOne platform is specifically designed for drug rescue in neurodegenerative diseases — a space where population heterogeneity is especially pronounced.
📜 The Roivant Precedent
The idea of finding value in pharma's abandoned assets isn't new.
Roivant Sciences, founded in 2014 by Vivek Ramaswamy, was built on the thesis that the pharmaceutical industry was full of abandoned assets that failed not because of efficacy problems but because of strategic deprioritization. They licensed shelved drugs, housed them in independent subsidiaries (”Vants”), and pushed them through development.
The model worked, sometimes. The most famous case, buying GSK’s Alzheimer’s candidate intepirdine for $5 million via Axovant, failed in Phase 3. But Roivant learned and evolved. The company has since grown to a ~$20 billion market cap by pivoting to a precision focus on immunology and inflammation, and its AI story now lives in VantAI (a spinout building the Neo-1 model for molecular glue design), not in clinical failure analysis.
Roivant’s original model was a financial and operational bet, spot undervalued assets, give them focused management, and move fast. It wasn’t a computational bet on finding hidden responder subgroups in trial data per se. The companies profiled above are making a different bet: that AI can extract signal from noise in ways that traditional biostatistics couldn’t.
Roivant CEO Matt Gline has been candid about his skepticism of AI drug discovery, arguing it faces a fundamental problem by solving only one or two of the roughly 150 hard problems in preclinical development. But it was aimed at de novo drug discovery, not at the more focused application of AI for patient stratification in existing clinical data.
⚠️ Still Open Questions
We need to be honest about the risks and limitations.
The p-hacking problem is real. Post-hoc subgroup analysis is one of the oldest and most dangerous tools in clinical research. If you slice a trial population enough ways, you will find a subgroup that responded, by chance. The key question for every company in this space is: can your AI-identified subgroups be validated prospectively? Retrospective signal discovery is table stakes. Prospective confirmation is where most of these approaches will succeed or fail.
Regulatory uncertainty. The FDA has frameworks for enrichment strategies and biomarker-driven trial designs, but there’s no well-trodden regulatory path for “we reanalyzed a failed trial with AI and found a responding subgroup, now we want to run a new trial in just those patients.” Each company is navigating this largely ad hoc. Lantern’s FDA interaction on the HARMONIC trial amendments is a positive signal, but one data point doesn’t make a precedent.
Small subgroups, small markets. Patient stratification is a precision medicine play. By definition, you’re narrowing the addressable population. Some subgroups will be commercially viable (Lantern’s L858R NSCLC population of 65,000–80,000 relapsed patients per year is meaningful). Others may be too small to justify the cost of a dedicated clinical program. The economics of drug rescue only work if the subgroup is big enough, or if the development cost is low enough, to justify the investment.
Data access and quality. These approaches are only as good as the data they’re trained on. Pathos has an enormous advantage through the Tempus relationship (200+ petabytes), but most failed trials sit in corporate vaults, and the patient-level data needed for subgroup reanalysis is rarely publicly available. Companies that can’t access high-quality, multimodal, longitudinal patient data are building on thin foundations.
The “drug just isn’t good enough” problem. As Ignota’s CEO, Sam Windsor pointed out, sometimes patient stratification isn’t the answer; the molecule itself has fundamental issues. A drug with genuine safety problems or an insufficient therapeutic window won’t be rescued by finding better patients for it. The companies building these AI platforms need to be disciplined about walking away from molecules that don’t warrant rescue.
Self-reported early data. Most of the clinical results we’ve seen so far — including Lantern’s LP-300 PFS data — are company-reported, from early-stage trials, in small patient numbers. This is expected at this stage of maturity, but we shouldn’t confuse preliminary signals with validated outcomes. The real test comes in registrational trials with pre-specified subgroups and independently adjudicated endpoints.
🔭 Looking Ahead
So is “rescuing failed drugs with AI” a category now?
The capital providers certainly believe so. Between Biossil ($70M), Pathos ($365M Series D, $1.6B valuation), Formation Bio ($600M+, $1.7B valuation), and Lantern (public, running clinical trials), there’s real money behind the thesis, from investors ranging from traditional life science VCs to Founders Fund and OpenAI.
But a category needs more than capital. It needs clinical proof points. Here’s what to watch:
Near-term (2026–2027):
Lantern’s HARMONIC trial data in the narrowed L858R NSCLC subgroup. This is one of the most concrete tests of the thesis: a drug that was broadly tested, AI-identified a specific molecular subgroup, and a redesigned trial is now running in just that population. If LP-300 produces confirmatory data, it’s a powerful proof of concept for the entire space.
Pathos’s pocenbrodib Phase 1b/2a data in mCRPC — particularly whether their biomarker-defined subgroups show differential response.
Biossil’s first clinical readouts from any of its ten programs.
Medium-term (up to 2028 and beyond):
Whether any AI-identified subgroup leads to a regulatory filing. This would be the true inflection point — a drug that failed in a broad population, succeeded in an AI-defined subgroup, and got approved for that subgroup.
The maturation of multimodal foundation models in oncology (the AstraZeneca-Tempus-Pathos collaboration). If these models can reliably predict responders across tumor types, the implications extend far beyond drug rescue.
Whether pharma companies begin systematically reanalyzing their own shelved assets with these tools, either internally or through partnerships. The volume of failed late-stage programs sitting in corporate vaults is enormous.
The open questions:
Can retrospective AI-driven subgroup discovery produce biomarkers robust enough for prospective trial enrichment? This is the central scientific question.
Will regulators create clearer frameworks for AI-informed trial redesign, or will each program remain a bespoke negotiation with the FDA?
Is there a sustainable business model here, or will drug rescue remain a niche strategy for specific asset classes? The economics depend heavily on how cheaply you can acquire failed assets, how efficiently AI can identify the right subgroup, and how large that subgroup turns out to be.
These are early days. The tools are getting dramatically more powerful, including multimodal models, massive patient datasets, causal inference frameworks, and agentic AI systems. But the clinical validation is thin, with only a handful of companies running trials. None has yet produced the definitive proof point: a failed drug, rescued by AI-driven patient stratification, approved by regulators.
We think this is a space worth watching closely, because the underlying logic is sound, the unmet need is enormous (90%+ failure rates, billions in sunk R&D), and the technical capabilities are likely approaching what the problem demands. The next 18–24 months of clinical data will tell us whether this is a genuine new paradigm or an expensive lesson in the limits of computational biology.
As always, if you’re working in this space or watching it from the inside, we’d love to hear what you’re seeing. Leave a comment!
AI-driven drug rescue could become a major biotech advantage by finding new uses and patient targets for previously abandoned compounds, reducing both cost and development time.