Capabilities
One engine. Every gate of the drug lifecycle.
The same discipline — trace, break, pre-register — answers the question that matters at each stage, from the bench to the capital table. Point it anywhere.
R&D
Does the science actually hold?
The stake if it is wrong: a dead program, funded.
Translation
Is the target product profile credible and sourced?
The stake if it is wrong: fundable, or not.
Clinical
Will this readout separate?
The stake if it is wrong: the call, in millions.
Regulatory
Approvable across FDA and EMA — and will the agency accept an AI-assembled submission?
The stake if it is wrong: years of runway.
Commercial
Is the market — and the pricing — real?
The stake if it is wrong: TAM, share, launch.
Capital
Wire, or pass?
The stake if it is wrong: the whole check.
The specialization
Will the FDA accept an AI-built regulatory submission?
Whether the agency will accept an AI-assembled submission is a question being decided right now — in draft guidance, in promotional-review queues, in the first filings built with machine assistance. The useful preparation is not speculation about the answer; it is having already moved regulated work through the agency's review at scale, and having certified machine-learning systems for clinical use under the regimes that already exist.
FDA promotional review, operated at scale
A Form 2253 pipeline running 7,000+ assets per week through FDA OPDP review — at a 100% first-pass approval rate (Shire).
Machine learning, certified for the clinic
CLIA- and CAP-certified ML diagnostics stood up in precision oncology — machine learning applied to clinical decision-making (Metamark / ProMark).
The regulatory science, studied formally
MS Biotechnology, Enterprise concentration, Johns Hopkins — the regulatory, technical, and commercial layers of biotech in one degree.
The breadth is proven, not claimed — twenty-five years standing at every one of these gates as the operator, the founder, and the diligence reviewer.
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