Swiss BioTech startup ALP Bio raises €1.9 million to advance immune organoid and AI platform
Schlieren-based ALP Bio AG today announced it has raised €1.9 million in pre-Seed financing to accelerate their platform which combines human immune organoid biology with generative AI to help antibody developers identify, understand, and reduce immunogenicity risk earlier in drug development.
The round was led by Munich-based VC 42CAP, with participation from Venture Kick and a group of strategic angel investors.
“Immunogenicity is one of the largest hidden costs in biologics, and the industry has accepted late-stage surprises as the norm for too long. We believe this risk should be measured and reduced years earlier than it is today. This financing lets us scale the experimental and computational foundation of our platform and partner with teams who want to make antibody development more predictable from the start,” says Dr Christian Vahlensieck, CEO of ALP Bio AG.
“Our scientific conviction is that immunogenicity cannot be solved by computation alone. By combining human immune organoid readouts with AI, we can generate the type of biological feedback needed to make antibody design more informed, iterative, and ultimately more useful for discovery and optimisation teams,” adds Dr Lucas Schaus, CSO of ALP Bio AG.
Founded in 2025, ALP Bio is developing an immune organoid and AI platform for immunogenicity intelligence in biologics development. The company combines human immune organoid readouts with generative AI to help drug developers predict and reduce anti-drug antibody risk earlier in antibody discovery and development.
According to the company, immunogenicity, including anti-drug antibody (ADA) responses, remains one of the most difficult risks in biologics development. These immune responses can reduce therapeutic efficacy, create safety concerns, and force teams to abandon or rework programmes late in development, after they have already consumed significant time and capital.
Today, most immunogenicity signals only emerge in clinical trials, when the cost of course correction is highest.
ALP Bio is building a hybrid platform designed to bring more clinically relevant signal into earlier antibody decision-making. The company combines experimentally measured human immune organoid readouts with machine learning models that support risk assessment and antibody redesign.
By integrating wet-lab immune biology with scalable computational design, ALP Bio aims to move developers from late-stage failure toward earlier, more actionable immunogenicity intelligence.
The platform builds on human tonsil organoid technology, which models relevant immune activity in vitro, and AI models designed to learn from increasingly rich biological datasets. ALP Bio is developing the platform to support lead candidate screening, ADA risk stratification, and sequence optimisation while preserving therapeutic function.
“ALP Bio is doing for biologics what high-throughput screening did for small molecules: collapsing a years-long bottleneck into a tractable design loop. Immunogenicity has held back the field for decades, and the team’s combination of immune organoid biology and sequence-level AI is the most credible attempt we have seen to address it at the source.
“This is exactly the kind of frontier BioIT bet, deep science with a clear commercial wedge, where we back founders with real conviction. Christian, Lucas, and the team bring the scientific depth and commercial discipline a problem of this size demands,” says Thomas Wilke, Partner at 42CAP
The pre-Seed round will be deployed to:
- Expand ALP Bio’s experimental immune organoid capabilities and increase automation and throughput.
- Launch early partner projects focused on immunogenicity risk in antibody lead candidates.
- Build out scientific and commercial capacity in Switzerland and the US.
ALP Bio is now engaging pharmaceutical and BioTech partners interested in early-access collaborations on immunogenicity. The company is particularly focused on antibody programmes where earlier de-risking could improve candidate selection, reduce downstream uncertainty, and support better development decisions.
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