RINAE.AI
Compressing rare disease drug discovery from years to weeks.
Identify the target. Score the odds. Design the drug. Ship findings in days, not quarters.
Argus ◉ pulling evidence from 15 bioinformatics sources — ClinVar, gnomAD, GTEx, OMIM…
The Problem
Ultra-rare disease isn't just hard. It's economically broken.
Conventional pharma is built around indications with a million patients and billion-dollar revenue ceilings. None of that math applies when fewer than a thousand children share the same diagnosis worldwide.
<1,000
Patients globally for ultra-rare
An eligible trial pool measured in dozens, not thousands.
5–7 yrs
Symptom onset to genetic diagnosis
8+ specialists consulted before the right answer arrives.
3–5 yrs
To enroll a 30-patient trial
KOL networks, conferences, and advocacy chains identify ~5 patients/month.
$50–150M
Per-program development cost
Down from $1–2B for common indications, but still unviable at this scale.
The brutal math. At 500 patients globally with a 50% diagnosis rate and 70% treatment eligibility, the addressable population for a single ultra-rare program is roughly 175 patients. Traditional development costs of $100M+ make that marginal at best — which is why most of these diseases never get a program.
The Approach
Why traditional approaches fail.
Each step that takes years for a common indication has to collapse to months — without losing the rigor an FDA reviewer expects. That's the bar.
Patient finding
20+ months → weeksTraditionalKOL networks, conferences, advocacy chains identify ~5 patients/month.
AI-enabledML over claims + genetic data identifies the same patients in weeks.
Natural history
3–5 years → monthsTraditionalProspective studies with limited sites and selection bias.
AI-enabledAI synthesis from sparse case reports with Bayesian inference.
Trial design
Inconclusive → rigorousTraditionalConventional designs assume large N — underpowered with N=30–50.
AI-enabledBayesian adaptive N-of-1 crossover designs increasingly embraced by FDA.
Commercialization
60% → 90% coverageTraditionalPost-approval HEOR and rebates — payers demand evidence we don't have.
AI-enabledPre-built value-based agreement infrastructure with outcomes tracking at launch.
The Pipeline
Three tools. One surface.
Each stage is a real service the team ships today. The platform wires them into a single pipeline with shared state, review gates, and an audit trail.
Stage 1Argus
Pull evidence.
Query 15+ bioinformatics databases for a gene target — expression, variants, constraint, pathways — and materialize one normalized evidence packet.
Stage 2Admiral
Assess feasibility.
Multi-agent amenability scoring: five specialist models deliberate on whether the target is druggable with an ASO and produce a structured report.
Stage 3Metamorph
Design ASOs.
Generate 20-nt antisense oligonucleotide candidates, fold and rank them, and screen off-target binding against the human transcriptome.
“My daughter Rose was diagnosed with HNRNPH2-NDD when she was three. We started RINAE to turn the science of treating rare diseases from a decade of committee work into weeks of focused engineering.”
Casey McPherson
Founder · AlphaRose Therapeutics
HNRNPH2 — Lead Program
Without AI-powered patient identification, enrolling a 30-patient trial could take 3–5 years and exhaust a program's runway before generating meaningful data.
Who built this
Genzyme, Alnylam, Krystal Biotech alumni.
The team that built Vyjuvek — the first FDA-approved in-vivo gene therapy — is operating the platform alongside rare-disease researchers and AI engineers.
Alan Walts
Executive Chairman
27 years at Genzyme · President, Genzyme Pharmaceuticals
Belinda Termeer
Co-founder
Termeer Foundation · Genzyme alumni network
John Garcia
CCO
VP at Alnylam · SVP at Krystal Biotech (launched Vyjuvek)
Reference corpus
188K ASO patents
ASO Atlas: 417 distinct chemical designs distilled into Admiral's reasoning.
Target per-program cost
$2–3M
vs. $100M+ for traditional rare disease programs.
Discovery-to-IND timeline
10 yr → 4 yr
40–70% compression across target validation, patient ID, natural history, and regulatory prep.