Six decisions, made with the evidence in hand.

Each of these started with a high-stakes question a strong team could not finish on its own: a large opportunity space, evidence scattered across disciplines, and a decision waiting at the end of it.

Every number below comes from the engagement itself, and we name the team wherever they let us.

SharkTooth Bio, asset strategy

Repurposing off-patent compounds for CMT1A

19compounds selected for in vivo testing

Urchin integrated SharkTooth's proprietary RNA-seq data from CMT1A mouse models with a broad array of public evidence, and surfaced a drug class their scientific advisors had not originally prioritized.

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Vrata Therapeutics, biological strategy

Four hypotheses for an unexplained delivery mechanism

4novel mechanistic hypotheses, none previously considered

Rather than narrowing to a single explanation, Urchin explored the space of mechanistic possibilities and returned four hypotheses, each with a rationale and suggested experiments. Reagents were ordered within a week.

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Jonathan Thon, translational strategy

A regulatory and clinical roadmap for a first-in-class program

3iterative analyses into one roadmap

Regulatory precedent, potency assay strategy and Phase 1-3 design, connected into a single evidence-based framework the advisor could share directly with the board and with KOLs.

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A gene editing company, indication selection

Which indications best fit a new therapeutic modality

200+candidate opportunities evaluated, 6 shortlisted

A team that had spent years on the opportunity space wanted to know what was still hiding outside it. Several of the highest-priority results were candidates the scientific lead said he would not have identified independently.

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Absco Therapeutics, translational strategy

Parameterizing a PBPK model for a novel route of administration

27PBPK parameters estimated, each with rationale and confidence

Urchin synthesized evidence scattered across pharmacology, anatomy, physiology and comparative biology into biologically grounded estimates. It clarified which preclinical species were viable translational models, and resolved a mechanistic question that would otherwise have needed an experiment.

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Probably Genetic, portfolio strategy

Scale or depth: how to grow a genomic data asset

30kpatient cohort at the center of a Series B decision

Urchin mapped where biological value comes from cohort scale versus per-patient depth, connected that to partner demand and market comparables, and turned an open-ended strategic debate into a concrete, depth-first investment decision.

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More on the system behind them: how Urchin works.

Interested in exploring a question together?

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Grace Tiao, Founder & CEO
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