The Problem - Too Many Failed Transplants (Watch Video)

Behind every failed kidney transplant is a patient forced back to dialysis, a family restarting the waitlist process, and billions in avoidable healthcare costs each year.

In blood stem cell transplantation, graft-versus-host disease (GVHD)—a major cause of morbidity and mortality—affects 15-35% of patients, and blood cancer relapse rates remain high at 25-50% depending on disease risk.

Through optimal donor selection, Immunomatics aims to reduce the rates of rejection, GVHD, and cancer relapse.

The deeper issue is how donors are selected. Today’s practice counts HLA mismatches and treats them as equally risky, even though evidence shows that some mismatches are well tolerated while others drive rejection. Clinicians lack a reliable tool to tell the difference, so one of the most consequential decisions in medicine is made with a blunt instrument.

A note from our founder

“I am alive today because I received a blood stem cell transplant to treat relapsed lymphoma. But when my doctors evaluated potential donors, none was a perfect match, and there was no way to predict which donor would be safest.

That uncertainty stayed with me. I realized we need better tools to guide one of the most important decisions in transplantation. That is why I founded Immunomatics, to bring precision medicine to transplantation.”

— Lidio Meireles, PhD, Founder & CEO, Stem Cell Transplant Survivor

The Solution - Better Matches, Better Outcomes

Immunomatics is developing AI software that goes beyond counting mismatches, estimating the relative rejection risk of specific donor-recipient incompatibilities before transplantation. Our platform analyzes donor and patient genetic data to produce a rejection-risk score designed to help transplant teams select safer donors, personalize immunosuppression to each patient’s risk, and monitor for rejection more effectively.

Fewer mismatches aren't always safer.

Ranked by mismatch count, Donor B looks safest. Ranked by real rejection risk, it's the most dangerous.

A rejection-risk score clinicians can act on

The approach is built to fit the clinic, not replace it. Risk analysis uses existing transplant workflow data. No new tests needed. The score may be used to guide donor selection, immunosuppression, and rejection monitoring.

Traction & credibility

Beyond the grant, the team is advancing discussions with transplant centers and building the data partnerships needed to validate the model against real-world outcomes. The NIH award is a meaningful external signal: independent reviewers judged both the science and the team worth funding.

Market opportunity

Global TAM: ~$220M per year

About 220,000 solid-organ and blood stem cell transplants are performed worldwide each year. At about $1,000 of software value per transplant, that adds up to a $220M annual market.

Initial SAM: ~$116M per year (U.S.)

We’re starting in the U.S. In 2025, 251 hospitals performed more than 49,000 organ transplants, and 150 centers performed about 9,000 allogeneic blood stem cell transplants. At around $2,000 per transplant, that’s a $116M annual opportunity.

Forward-looking projections cannot be guaranteed.

Multiple paths to scale, without re-engineering the platform

1. Land and expand. Widen adoption across additional transplant departments within existing hospital networks, raising contract value per customer.
2. Geographic expansion. Scale sales and regulatory efforts into the high-volume European and Asia-Pacific markets.
3. Strategic out-licensing. Partner with multinational transplant-diagnostic companies to embed our AI models in their existing software.
4. Biopharma partnerships. Commercial agreements with cell-therapy companies that need advanced immune-compatibility modeling.

Reasons to invest

As with any early-stage healthcare company, this opportunity is subject to scientific, execution, regulatory, and commercialization risks.

1. Credibility at an early stage. A competitive NIH Phase I SBIT award provides third-party scientific validation and non-dilutive funding.
2. Founder-market fit. Led by a founder with both personal experience as a transplant recipient and professional training in computational biology and AI.
3. Large, unmet need. Rejection remains a major cause of failed grafts, complications, and high cost across organ and blood stem cell transplantation.
4. Impact, measured in lives. This is impact investing at its core. Better donor matches mean fewer rejections, less graft-versus-host disease, and more patients who survive and go home to their families. Your capital helps makes that real.

Roadmap

1. Foundation (Where we're at)
2. A $207,740 NIH Phase I SBIR non-dilutive, peer reviewed award.
3. Access to SRTR registry data
4. Collaborating with UC San Diego's Immunogenetics and Transplantation Laboratory.
5. Execution (Next 12-18 Months)
6. Development of Proof of Concept
7. Pilots at transplant centers
8. NIH Phase II ($2M)
9. Vision (Where we're headed)
10. Regulatory clearance
11. Expansion across U.S. & abroad

The ask & use of funds

Immunomatics intends to raise $500,000 in this offering as a Simple Agreement for Future Equity (SAFE), with a 20% discount and $8M post-money valuation cap. The proceeds will advance the platform through proof of concept and early clinical validation, allocated across four strategic areas:

1. Product development & proof of concept. Refine algorithms, benchmark performance, and execute early clinical validation.
2. Data acquisition & strategic partnerships. Expand access to transplant outcome datasets and build relationships with leading transplant centers.
3. Team expansion & specialist support. Add targeted talent and external expertise in technical, clinical, and regulatory areas.
4. Intellectual property protection. File and maintain patent applications in the U.S. and key markets abroad.

FAQ

Book an appointment with Lidio Meireles, CEO.

What problem does Immunomatics solve?

Immunomatics AI-based software estimates the relative risk of specific donor-recipient incompatibilities before transplantation, helping clinicians optimize donor selection for better transplant outcomes, less rejection, more survival.

Who would use the platform?

Transplant centers and histocompatibility labs evaluating donor-recipient compatibility across solid organ and blood stem cell transplantation.

What role does AI play?

Immunomatics designed neural network architectures that model how immune cells recognize foreign antigens. These models learn to correlate patterns of amino acid mismatches between donor and recipient HLA with the risk of rejection (time-to-event) using transplant registry data. The risk score is then adjusted for non-HLA factors, such as donor and recipient age and comorbidities.

Does it require new tests?

No. The approach is designed to work with data that already exists in current pre-transplant workflows, which could support adoption if validated.

How is the company funded today?

A $207,740 NIH Phase I SBIR award plus $120,000 in founder capital. This round is intended to fund development, data partnerships, and clinical validation.

How can I get involved?

Invest in this round, or connect directly with founder Lidio Meireles to learn more about the science and roadmap.