Genotype-to-referral OS for AATD gene-editing trials that finds untreated PiZZ patients and qualifies centers before launch.

1. Capital has reached the level where sponsors can and must fund dedicated AATD enrollment infrastructure rather than rely only on ad hoc coordinator work.
2. A defined pool of severe PiZZ patients without underlying treatment creates urgency to surface missed diagnoses before competitors do.
3. Root-cause editing raises the value of precise genotype confirmation, making disease-specific referral software more necessary than broad respiratory outreach.
4. Cross-border asset licensing shortens the time between scientific access and clinical execution, so sponsors cannot wait to build patient-finding workflows later.

Catalyst. Serapha's $230M launch around a root-cause PiZZ editor turns AATD from a future science story into an immediate race to diagnose, qualify, and route patients into trials and early center networks.

The company sells a disease-specific operating layer to sponsors and specialist centers launching AATD gene-editing programs. It connects pulmonary and hepatology referral sources, identifies charts that match likely AATD patterns, triggers genotype-confirmation workflows, and packages prior imaging, lung function, liver markers, and family history into a sponsor-ready screening dossier. For active sites, the product standardizes baseline collection and tracks where patients stall between suspicion, testing, confirmation, and enrollment. Over time, the startup builds the highest-value dataset in the category: which referral patterns yield true PiZZ patients, what baseline evidence predicts trial fit, and which centers convert screened patients into enrolled participants fastest.

What's different. Generic trial-tech vendors help manage forms after a patient is already found, while rare-disease recruiters usually lack deep genotype and site-readiness workflows. This company starts earlier in the funnel by linking suspicion patterns, confirmatory testing, baseline evidence, and sponsor-site handoff inside one AATD-specific workflow. Its defensibility comes from a proprietary graph of referral sources, genotype-yield patterns, family cascade conversions, and site performance that becomes more valuable as more one-time editing programs enter adjacent diseases.

Jobs to be done

flowchart LR
  Buyer[Clinical Ops Lead] --> Pain[Hidden PiZZ patients and unprepared sites]
  Pain --> Product[AATD referral readiness OS]
  Product --> Outcome[Faster enrollment and stronger site conversion]

Executive takeaways

• The wedge is real because the operational bottleneck is genotype-confirmed patient flow and baseline-packet completeness, not generic respiratory awareness.
• The first beachhead is commercially meaningful but narrow, so the venture case depends on proving AATD and then expanding into adjacent genotype-defined liver and lung diseases.
• Horizontal recruitment vendors exist, but none obviously own the combination of AATD-specific testing rules, family cascade logic, and sponsor-ready dual-organ packet assembly.

Market definition

The beachhead is sponsor- and site-facing workflow software plus services that move suspected AATD patients to genotype-confirmed, protocol-ready screening packets for disease-modifying studies and early treatment-center networks. The need exists because AATD remains underdiagnosed, diagnosis is delayed, family testing is still underused, and the therapeutic pipeline has shifted from augmentation toward genotype-directed RNA and base-editing programs [4][6][9][11][13][16][17][18][19][21][22].

Customer and buyer

Primary buyer is a VP Clinical Operations or CMO at a sponsor running an AATD study; day-to-day champions also include site directors, genetic counselors, and study-startup leads that must identify PiZZ patients, collect liver-plus-lung baselines, and refer consent-ready patients into protocol screening [13][14][25][29].

Buying triggers

• A sponsor moves into site activation and realizes a genotype-specific protocol needs fewer but far better qualified referrals than a generic respiratory funnel can deliver. [18][19][21][22][25]
• Guideline-driven testing and family-cascade expectations create extra work at centers that already have fragmented pulmonology, hepatology, and genetics workflows. [4][9][14]
• Patient interest in curative or durable gene/RNA therapies rises faster than site capacity to educate, test, and route them compliantly. [17][23][46]

Willingness to pay

Sponsors already buy expensive horizontal recruitment, data-enrichment, and trial-ops support for low-prevalence studies; if an AATD-specific layer materially improves genotype yield, reduces site-level screen failure, and shortens time to first qualified patient, six-figure annual program spend is easy to justify even in a small market. [25][26][27][31][33][35]

Category dynamics

Growth signal Not credibly reducible to a clean market CAGR; active AATD clinical activity is broadening from augmentation into RNAi, RNA editing, base editing, and other precision modalities.

Validation signals

• New sponsor capital and public-market launch activity make AATD operational infrastructure a current, not theoretical, buyer need.
• Multiple active recruiting or active-not-recruiting precision-therapy studies confirm sustained sponsor demand for PiZZ patient identification.
• Guidelines explicitly recommend broader testing and family cascade work, which creates exactly the workflow burden the product addresses.
• Patients show meaningful willingness to consider gene therapy and clinical trials when educational gaps are addressed.

Regulatory & technical constraints

• Electronic workflows visible to sponsors must satisfy FDA expectations for trustworthy electronic records and signatures under Part 11.
• If provider EHR data is used for screening or baseline packets, provenance and source-data-originator controls must be explicit.
• Remote pre-screening, telehealth, or decentralized steps still sit inside FDA guidance on decentralized clinical-trial elements.
• Integration quality depends on whether target sites can expose USCDI/FHIR-aligned data rather than only unstructured documents.

Competition comes from horizontal cohort-discovery and recruitment vendors rather than AATD-native software. TriNetX, IQVIA, Antidote, and data-enrichment networks can all sell “find patients faster,” but their default unit of work is a broad study funnel. The proposed startup narrows to mutation-aware AATD identification, family cascade routing, and sponsor-ready packet assembly, which is more specific than current horizontal offerings but also much narrower at launch [25][27][29][31][32][33][35].

Why incumbents do not win by default

• Horizontal real-world-data platforms. Platforms like TriNetX help with cohorting and site discovery, but they do not win by default because AATD enrollment still needs disease-specific genotype confirmation, family outreach logic, and packet assembly outside a generic no-code cohort builder.
• Full-service CRO recruitment engines. IQVIA-style recruitment can scale globally, but it is optimized for breadth and execution capacity, not for owning an enduring AATD referral graph that improves true-PiZZ yield over time.
• Genetic counseling specialists. InformedDNA-like services are strong at consent-ready referrals and counseling, yet they do not appear to offer a full sponsor-site operating system for dual-organ baseline capture and site performance benchmarking.
• Patient community networks. Rare Patient Voice and similar networks can surface motivated patients, but they start later in the funnel and do not replace clinic-side case finding, genotyping workup, or structured handoff into regulated site workflows.

AATD Referral Readiness OS sells sponsor-facing workflow software and services to gene-editing companies and expert centers that need genotype-confirmed PiZZ patients, complete dual-organ baseline packets, and trial-ready referrals. The first customer is a VP Clinical Operations or CMO at an AATD precision therapy sponsor activating 3-8 U.S. sites and discovering that generic rare disease recruitment does not reliably produce protocol-ready PiZZ candidates. The wedge is narrow by design: identify likely AATD patients in pulmonology and hepatology workflows, route confirmatory testing, and assemble sponsor screening packets before site capacity is wasted. This beachhead is attractive because value can be measured quickly through days to genotype confirmation, packet completeness, and time to first qualified patient rather than long-tail brand or awareness metrics. Research supports a plausible year-three SOM of about $2.0M, but the immediate buyer universe is small and the venture case depends on expansion into adjacent genotype-defined liver and lung diseases. The main evidence-backed advantage over horizontal vendors is tighter AATD workflow fit around genotype confirmation, family cascade routing, and dual-organ baseline assembly. The biggest open questions are whether target centers hold enough structured genotype and lab data for implementation-light onboarding, and whether first-wave sponsors prefer program fees, site fees, or throughput-based pricing. This should be viewed as a credible pre-seed infrastructure play with a real operational bottleneck, but not yet a high-conviction platform unless one anchor sponsor proves repeatable time to qualified-patient improvement.

Problem

• Likely PiZZ patients remain buried across COPD, emphysema, liver disease, and family-screening workflows, so sponsors and sites do not control a reliable pipeline of genotype-confirmed candidates.
• Confirmatory testing, counseling, packet assembly, and baseline evidence collection are still manual across pulmonology, hepatology, and genetics teams, which slows enrollment and raises screen-failure risk.
• One-time editing programs make each missed patient and each underperforming site disproportionately expensive because first-wave studies have small footprints and concentrated timelines.

Solution

• Flag likely AATD cases inside specialty-center workflows, trigger confirmatory genotype and AAT testing, and track each case from suspicion to sponsor-ready referral.
• Assemble protocol-specific lung and liver baseline packets, family history, and consent state into an auditable screening dossier for sponsor and site teams.
• Benchmark which referral sources, family cascade paths, and centers produce true PiZZ patients and faster enrollment so sponsors can focus capacity where conversion is highest.

Why we win

• Horizontal cohorting and recruitment vendors do not appear to own the specific workflow where AATD suspicion, genotype confirmation, family routing, and sponsor packet assembly intersect.
• The company compounds a proprietary graph of referral-source yield, baseline-data gaps, and site conversion performance that gets stronger with each sponsor program and center onboarded.
• Launching as an overlay on existing site and lab workflows is more realistic than asking centers to adopt a broad new trial stack before the first AATD program is proven.

Milestones

flowchart LR
  Wedge[AATD sponsor wedge] --> MVP[Referral-readiness MVP]
  MVP --> Proof[Faster PiZZ qualification and packet completeness]
  Proof --> Expansion[Adjacent genotype-defined disease expansion]

Founding team

Experiment roadmap

Risk assessment

What must be true

• Sponsors will pay low-to-mid six figures annually for an AATD-specific workflow rather than rely on coordinators, CRO services, or horizontal recruitment vendors.
• At least 40% of target-center suspected cases can be routed through a lightweight data-ingestion model without requiring deep EHR integration first.
• Family cascade screening contributes enough incremental qualified referrals to matter commercially within the first 12 months.
• One anchor sponsor can show at least 30% faster time to first qualified patient and materially lower packet defects versus prior process.
• The same data and workflow model can expand into adjacent genotype-defined liver or lung diseases within 24 months.

Open diligence questions

• How many first-wave sponsors are realistic buyers in the next 24 months, and how concentrated is that revenue base?
• What percentage of PiZZ case finding at anchor centers depends on structured data versus manual chart review and PDFs?
• Which metric matters most to the economic buyer: days to first qualified patient, lower screen-failure rate, or site activation efficiency?
• Why would a sponsor buy this overlay instead of extending IQVIA, TriNetX, InformedDNA, or internal clinical-ops workflows?
• What evidence shows family cascade workflows create meaningful referral volume rather than operational complexity?

Model sanity

• Revenue engine. Base revenue comes from turning 2 Y1 paid pilots into 5 active sponsor programs by Q4Y3 while program value rises from pilot pricing to roughly $500K annualized production value plus expansion fees.
• Must go right. Onboarding has to stay within the BP's 6-8 week target so gross margin can climb toward 72% without adding too much implementation headcount.
• Model breaks if. A 12-month sales cycle or sub-68% gross margin pushes the downside case through zero cash before the adjacent-disease expansion starts to help.
• Next-round proof. A seed-ready story is 3 production sponsor programs, about 8 active sites, benchmark reporting, and one adjacent-disease paid partner by late Y2.

Scenarios

Sensitivity

flowchart LR
  SponsorLeads --> PaidPilots
  PaidPilots --> ProductionPrograms
  ProductionPrograms --> ProgramFees
  ProductionPrograms --> SiteExpansion
  ProductionPrograms --> ThroughputFees
  ProgramFees --> Revenue
  SiteExpansion --> Revenue
  ThroughputFees --> Revenue
  Revenue --> GrossProfit
  GrossProfit --> Hiring
  GrossProfit --> Opex
  GrossProfit --> Cash

Flags: The company is still not fully EBITDA positive in Y3, so the seed case depends on convincing investors that adjacent-disease expansion is repeatable before absolute profitability. · Revenue concentration remains high because 5 active programs still sit inside a small AATD buyer universe and one delayed sponsor can move the model materially. · The model assumes lightweight ingestion really works; if target centers require deep EHR integration, gross margin and pilot velocity both deteriorate quickly. · Cash stays positive in the base case, but the downside shows that a slower sales cycle plus lower structured-data availability likely forces a bridge before the seed milestone is fully proven.

• Narrow first sponsor market. Only a small number of AATD gene-editing programs may be active in the first wave. Mitigation: Start with AATD for focus, but design data models and workflows to expand quickly into adjacent genotype-defined liver and lung diseases.
• Integration friction at specialty sites. Pulmonary and hepatology centers may resist another workflow unless setup is light and immediate value is obvious. Mitigation: Launch with minimal data-ingestion paths, lab-partner integrations, and white-glove onboarding that proves faster referral conversion within one study cycle.
• Regulatory caution around screening workflows. Sponsors may worry that software-driven qualification logic could complicate protocol compliance or site oversight. Mitigation: Position the product as an operational decision-support layer with sponsor-configurable rules, auditable handoffs, and validation against existing screening SOPs.