Why This Exists
In AI-oncology, calibration failure takes two forms — and both are expensive.
The obvious failure is overclaiming. A program reaches the pre-IND or first-in-human stage carrying an endpoint the agency does not regard as a development endpoint, a trial design that cannot enroll its intended population, or a model whose provenance cannot be audited to regulatory standards. The story is ahead of the evidence.
The subtler — and often costlier — failure is premature conformity. A program forces itself into established precedent when the science, the mechanism, the biomarker, or the patient population may warrant a reasoned departure. Some of the most consequential oncology approvals — accelerated approvals on early response data, tumor-agnostic labels, novel surrogate endpoints validated under unmet need — came from sponsors who engaged the agency in territory the agency itself acknowledged was less well understood, and who built the evidentiary package that made the new path defensible.
The failure is not choosing one or the other — it is not knowing which case you are in, and not having the evidentiary package that would make the chosen path defensible. Both failures are failures of calibration between what the model or platform produces and what regulators have historically required — or would entertain. Most companies discover the misalignment late — inside a pre-IND meeting, or in front of a board — after capital and calendar time are already committed. The Calibration Sprint is designed to surface it before that.