For decades, the industry has organized itself around a single question: how do we discover more, and better, drug candidates? Target identification keeps getting smarter, molecule generation keeps getting faster, and AI has now touched nearly every stage of early discovery. Yet a molecule is only ever as good as the patients it happens to be tested on, and that half of the equation has received a fraction of the innovation investment.

Consider what happened across non-small cell lung cancer last year. Seven new FDA approvals arrived in 2025, each tied to a specific genomic alteration rather than a broad diagnosis, spanning antibody-drug conjugates, bispecific antibodies, and targeted kinase inhibitors. That wave didn’t happen because the underlying biology of lung cancer changed. It happened because the field finally had the tools to see that “lung cancer” was never one disease to begin with, and to match each therapy to the subset of patients whose tumors were actually driven by the mechanism it targets.

Contrast that with a 2023 JAMA Internal Medicine analysis of drugs the FDA approved between 2018 and 2021, which found that roughly one in ten had failed to meet its primary efficacy endpoint in the pivotal trial that supported approval. Some of those drugs went on to help real patients anyway. But a meaningful share of what looks like scientific failure in this industry is actually a measurement failure: a real effect, diluted past the point of statistical detection by a population that was never properly stratified in the first place.

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