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.

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