The Logic of
Rejection.
We analyzed nearly 300 FDA Complete Response Letters to map the hidden landscape of drug approval. Here is the blueprint of what separates success from failure.
The Hidden Patterns
Rejection isn't random. By decoding the regulatory language, we found distinct signatures that predict whether a drug will eventually reach patients or be abandoned.
Safety is rarely the dealbreaker.
While safety concerns appear in 31% of letters, they are surprisingly recoverable. The real killers are often bureaucratic and manufacturing hurdles that drain resources and time.
The Trial Trap.
The single strongest predictor of failure is the requirement for a new clinical trial. If the FDA asks for more data, the odds of approval drop precipitously.
The Language of Failure.
We trained models to "read" the tone of FDA letters. Unapproved drugs receive letters with significantly harsher, more directive regulatory language.
Predicting Outcomes.
Using just a few key features from the initial rejection letter, our Random Forest model can predict the ultimate fate of a drug with 72% accuracy.
VIEW MODEL PERFORMANCE →Dive Deeper
EXPLORE THE DATASETOverview
Comprehensive breakdown of approval rates, temporal trends, and application types.
Deficiencies
Deep dive into what actually goes wrong. Heatmaps of co-occurring issues.
Language
NLP analysis of regulatory tone, sentiment, and semantic embeddings.
Prediction
Machine learning models that forecast drug rescue probability.
Search the Database
Full-text search across all 297 Complete Response Letters with integrated PDF viewer and highlighting.