The FDA receives raw and cleaned datasets from sponsors, not just summary reports. Their internal teams conduct independent analyses, which can lead to findings or data presentations in the official drug label that differ from or expand upon what's in the published paper.
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The CREST trial showed benefit driven by patients with carcinoma in situ (CIS), while the Potomac trial showed a lack of benefit in the same subgroup. This stark inconsistency demonstrates that subgroup analyses, even for stratified factors, can be unreliable and are a weak basis for regulatory decisions or label restrictions.
Academics with novel research questions can collaborate with the FDA. However, due to the confidential nature of sponsor data, all analyses are performed internally by FDA statisticians. External partners provide clinical insight and interpretation on summarized, non-confidential outputs.
The FDA is abandoning rigid, fixed-length clinical trials for a "continuous" model. Using AI and Bayesian statistics, regulators can monitor data in real-time and approve a drug the moment efficacy is proven, rather than waiting for an arbitrary end date, accelerating access for patients.
When asked if they would investigate a safety concern for a specific drug at an external party's request, the FDA expressed reluctance. Such an analysis would raise questions of bias. Instead, they prefer to address these questions by pooling data from multiple drugs with a similar mechanism of action.
After reacquiring a "failed" ALS drug, Neuvivo's team re-analyzed the 200,000 pages of trial data. They discovered a programming error in the original analysis. Correcting this single mistake was a key step in reversing the trial's outcome from failure to success.
The FDA is requiring higher US patient enrollment in global trials to address concerns that results from predominantly non-US populations (e.g., Asia) may not be generalizable. This reflects worries about differences in prior standard-of-care treatments and potential pharmacogenomic variations affecting outcomes.
The FDA's current leadership appears to be raising the bar for approvals based on single-arm studies. Especially in slowly progressing diseases with variable endpoints, the agency now requires an effect so dramatic it's akin to a parachute's benefit—unmistakable and not subject to interpretation against historical data.
The CREST trial's positive primary endpoint, assessed by investigators in an open-label setting, was rendered negative upon review by a blinded independent committee. This highlights the critical risk of confirmation bias and the immense weight regulators place on blinded data to determine a drug's true efficacy, especially when endpoints are subjective.
While depth of response strongly predicts survival for an individual patient, the FDA analysis concludes it cannot yet be used as a surrogate endpoint to replace overall survival in pivotal clinical trials. It serves as a measure of drug activity, similar to response rate, but is not sufficient for drug approval on its own.
The FDA is eliminating mandatory animal testing because it's often misleading—90% of drugs passing animal studies fail in humans. The agency is embracing modern alternatives like computational modeling and organ-on-a-chip technology to get faster, more accurate safety data.