Remember, the clinic is where the bill comes due — not where the mistakes are made. Go break some eggs.
👀 The Read
This week's feature is up on LinkedIn: the five nonclinical mistakes that cost first-time founders the most. Not clinical failures — the quiet calls made months earlier, in nonclinical, that decide whether your program shows up on time and defensible or late and full of holes. Pharmacology with no PK. Punting metabolite ID. Sprinting to tox before the data that's supposed to inform it. No line of sight to the clinic. Treating CMC as an afterthought instead of the gate it is. Fifteen years, 8+ INDs, 40+ biotechs — same five, every time.
❓ Recognize one of these in your own program? Reply — I read every one.
💭 The Decision
Deliverables, or a program designed backwards?
Here's the decision underneath all five mistakes. You can treat nonclinical as a checklist — run each study, tick the box, move on — or you can design the whole package backward from the clinical questions you'll have to answer. Same studies, opposite outcomes.
The checklist approach feels efficient and produces a program full of holes: endpoints that don't translate, exposures you can't anchor a human dose to, a species rationale with a gap FDA is very good at finding. You only discover what's missing in the clinic — where generating it costs trial time and trial money. Designing backward costs more upfront thinking and almost nothing later: every study earns its place by answering a question a reviewer, or your own Phase 1, will actually ask. The decision is which you're building — and you make it before the first study, because backward-design can't be retrofitted onto studies already run.
🔧 The Move
This week, take your lead program and do one thing: next to every nonclinical study — planned or done — write the clinical question it's supposed to answer. The human dose it informs. The safety signal it lets you watch for. The margin it establishes.
Any study without a clear clinical question attached is either mis-scoped or missing something — and that's your gap, found now instead of in the clinic.
📕 Did you know I wrote a book?
It's true. It's called Data Is Not Strategy,
and it's about how more data doesn't always equal better outcomes.
Many founders and teams think that if they do more —
run more studies, write more briefing books, make more molecules
that they will have better outcomes because more looks good, right?
❌ Wrong.
More means:
→ more justification needed
→ more probability of conflicting data
→ more things to weave into your regulatory narrative
This book teaches you how to recognize when more is not always better,
and how to be able to sit comfortably with uncertainty.
It may just save your program.