Median Refuse-to-File delay
182 days
The median time to resubmit after a Refuse-to-File; only ~72% of resubmitted applications had been approved at the time of analysis.
Chahal et al., JAMA Internal Medicine, 2021Clinical Brief · For the Chief Medical Officer
Defensible by construction.
You are strictly liable for every assertion in the dossier. Evidence infrastructure, powered by the Provenance Neural Engine (PNE), makes each AI-drafted line trace-verifiable to its source, so the read-out comes forward and the submission survives the audit.
Clinical development · AI in regulatory submissions
You're putting generative AI into CSRs, RWE, and submissions to pull the read-out forward, and you should. But a single hallucinated citation or fabricated statistic is grounds for a Refuse-to-File, and the sponsor owns every word. The winning move isn't a more accurate model; it's provenance: an evidence chain from every AI-generated assertion back to verified source data. That is what evidence infrastructure is built to produce.
Three pains a more accurate model won't solve.
Regulatory-writing hallucination
The pain
AI drafts the CSR faster, but a fabricated citation or statistic is a filing deficiency, and the FDA already refuses to file over missing or deficient evidence. You are strictly liable for all of it.
How evidence infrastructure answers
At its heart, the PNE binds every AI-drafted assertion to an inspectable chain back to source data at a specific version. A hallucination can't reach the dossier un-cited. Defensible by construction, not by after-the-fact review.
RWE trapped in dark data
The pain
Much of your clinical data sits in formats AI can't process without hallucinating, so the real-world evidence that powers HEOR and label expansion stalls in pilot.
How evidence infrastructure answers
Evidence infrastructure puts a unified, provenance-preserving layer over the data you already have: the ontology and lineage that make historical data AI-ready instead of AI-hazardous.
ICH M15 credibility
The pain
ICH M15 asks regulators to judge a model by its context of use and the consequence of a wrong decision, with the data, assumptions, and validation documented. A sophisticated model is useless for a submission without that record.
How evidence infrastructure answers
Evidence infrastructure produces the provenance and reproducibility evidence an ICH M15 credibility assessment asks for, with every record attributable and traceable to source in line with ALCOA+ principles. Your team still makes the case; the evidence is already there when it does.
The fear budget: what one un-provenanced line costs.
Cost of that delay
~$500k/day
Lost sales for an average drug for each day of delay.
Tufts CSDD, Ther. Innov. Regul. Sci., 2024Provenance Neural Engine™ is a trademark of Scientix.ai Inc. Figures: FDA Refuse-to-File median time to resubmission and resubmission approval rate: Chahal et al., JAMA Internal Medicine, 2021; value of a delay-day: Smith, DiMasi, and Getz (Tufts CSDD), Ther. Innov. Regul. Sci., 2024, lost sales, not out-of-pocket cost; credibility framework: ICH M15 (Step 4, Jan 2026).
Accuracy is not defensibility.
A 99%-accurate model is no defense for the one assertion that fails the audit. A global metric can't defend an individual claim. And a runtime guardrail only logs what the AI did; it can't reconstruct why the assertion is true. The PNE traces the evidence, not just the transaction: the difference between passing an inspection and surviving one.
The next step.
Bring us one submission. We'll show you every AI-touched assertion traced to source: the dossier your regulator can't send back, and the read-out you stop waiting on.