The science sets the shape of that distribution. The economics reads it. We model both together, because in life sciences one determines the other.
The mean sits well above the median — a handful of large outcomes drag it upward.
Scientists, physicians, economists and financial engineers in the same room. A biologist and an economist reading the same dataset reach different conclusions — and the useful answer usually sits where they disagree.
Molecular biology, pharmaceutical sciences, epidemiology and genomics. Whether the mechanism holds, and what the data actually supports.
PhD-level economists and market modellers, including World Bank and ILO consulting experience. What a market will bear, not what a report says it will.
Valuation and deal-structuring experience from industrial-scale negotiation, applied to probabilistic valuation and real options.
Each stands alone. Run in sequence, each one narrows the question the next has to answer.
Who else is developing against your target, mechanism or indication — and which of them will still be there when you reach the market.
The addressable population is a scientific question before it is a commercial one. Diagnosis rates, genotyping coverage and line of therapy decide how much of an epidemiological number you can reach.
Where analysis becomes decision: which indication runs first, which to hold, which partner is worth giving economics away to. Sequencing is usually worth more than selection.
Founders and executive teams deciding which indication leads, what a partner is worth, and how to frame an asset so diligence moves quickly.
Funds and family offices who need the biology behind a pitch assessed independently, and the numbers rebuilt from the ground up before capital moves.
Six people across the three axes — molecular biology, pharmaceutical sciences, economics and finance — supported by advisors from Harvard Medical School, the Joint Genome Institute, Mammoth Biosciences and the BioMed X Institute.
Not a deck of industry benchmarks. A model you can interrogate, with every assumption named, sourced and adjustable — so when someone challenges the conclusion, you can show them which input they disagree with.
Engagements are scoped as fixed-fee phases with defined deliverables. You know the cost and the output before work starts.
Markov decision trees and real options rather than a single discounted cash flow. Development is a sequence of conditional decisions, and the model should say so.
Built from diagnosis rates, testing coverage and treatment lines — not a headline prevalence figure that assumes every patient is found.
We identify the two or three assumptions that actually move the answer, then concentrate evidence-gathering there.
Scientific, economic and financial training on the same team, by design rather than by hand-off.
A 30-minute call, no charge and no deck. Tell us the question; we'll tell you honestly whether we can help and roughly what it would take.
Typical first engagement: a scoped landscape or market assessment, 3–6 weeks, fixed fee.