Outer Bio Emerges from Stealth With Yuna, a Long-Duration Human Skin Platform Designed to Accelerate Bioactive Discovery
Outer Bio, a company focused on discovering new bioactive compounds for skin health, has emerged from stealth and introduced Yuna, an ex vivo human-skin research platform designed to keep full-thickness human skin alive and measurable for more than four weeks. The technology is intended to address a longstanding limitation in skin research: the difficulty of studying complex biological processes in living human tissue over a period long enough for meaningful changes to develop.
By maintaining native human skin architecture and immune activity for an extended period, Yuna aims to provide researchers with a more clinically relevant environment for evaluating potential skincare ingredients, therapeutic compounds and biological mechanisms. The platform is also at the centre of Outer Bio’s machine-learning-based discovery strategy, which combines experimental data with computational models to identify and validate new skin bioactives.The company believes the platform could help address a major gap between conventional laboratory models and the biological complexity of real human skin.
A New Approach to Skin Biology Research
The global skincare industry generates more than $100 billion annually, reflecting strong consumer demand for products that can improve skin health, appearance and signs of aging. Despite the size of the market, however, the development of genuinely new topical mechanisms has been relatively limited.
Outer Bio argues that the challenge is not a lack of commercial interest but a limitation in the scientific tools available to researchers.Many existing laboratory models use reconstructed skin, isolated cells or cell monolayers. These systems can provide valuable information about toxicity, cellular responses and certain biological pathways. However, they do not necessarily reproduce the complete architecture and interactions found in native human skin.
Human skin is a complex organ made up of multiple layers and cell types that interact with one another. Its biological behaviour is also influenced by immune activity, inflammation, aging, environmental exposure and other factors that develop over time.
Another major limitation is duration. Conventional ex vivo human skin samples typically remain viable for less than a week. That creates a problem for researchers attempting to investigate biological processes that require considerably longer to emerge.
Processes such as collagen remodeling, chronic inflammation and cellular senescence can develop over weeks rather than days. Consequently, researchers may be forced to make decisions about long-term biological activity using models that only remain functional for a short period.
Yuna has been developed to address this challenge.
Keeping Full-Thickness Human Skin Alive for More Than Four Weeks
Outer Bio says Yuna can maintain full-thickness human skin for four weeks or longer while retaining its native architecture and resident immune function.The platform incorporates the epidermis and dermis along with multiple cell populations found naturally in human skin. This allows researchers to investigate interactions that may not be captured by simplified laboratory systems.
Another important aspect of the platform is its use of human donor tissue representing a broad range of biological characteristics. Samples are obtained from donors across different ages, sexes and Fitzpatrick skin types ranging from I through VI.This diversity is intended to provide a more representative picture of how biological responses may differ across populations.
Instead of relying on a single endpoint, Yuna enables longitudinal measurements throughout the experimental period. Outer Bio uses multiple forms of analysis, including transcriptomics, histology and measurements of secreted proteins.According to the company, each compound-and-sample combination can generate more than 30,000 individual data points. The platform has so far produced more than 10 terabytes of proprietary data from over 300 donors and approximately 10,000 treatments.
The growing dataset is an important part of Outer Bio’s strategy because the company intends to use the information not only to evaluate individual compounds but also to improve its computational discovery models.
Building a Data Engine for Skin Discovery
Outer Bio describes Yuna as more than an experimental testing system. The company considers the platform a data-generation and validation engine that can continuously feed information into its machine-learning discovery process.The approach is based on an iterative cycle.
Machine-learning models analyse existing biological data and help identify compounds that may have desirable effects. Those compounds can then be tested in Yuna. The results provide additional information about how the compounds affect living human skin. That new information is subsequently incorporated into the models, helping improve future predictions.
This creates a feedback loop between computational prediction and biological experimentation.The strategy reflects a broader trend across biotechnology, where artificial intelligence is increasingly being used to identify potential drug candidates, predict biological activity and analyse complex datasets.However, Outer Bio believes that computational models are only as effective as the experimental data available to train and validate them.
Michael Polansky, co-founder and CEO of Outer Bio, said the skincare industry has significant demand and ambition but has historically lacked the ability to study compounds in living human skin over a biologically relevant period.According to Polansky, simplified models can provide useful information but do not fully reproduce the complexity of a living organ. The company’s objective with Yuna is to create a platform that can generate clinically relevant biological information over a longer period.
Expanding the Compound Discovery Process
Outer Bio is also developing a large virtual screening library to support its discovery activities.The company’s current library contains approximately 5.9 million compounds drawn from 11 databases. Outer Bio has outlined plans to expand this library toward billions of synthesizable structures.
The purpose of such a library is to provide computational models with a large universe of potential molecules to evaluate.Rather than testing every compound experimentally, machine-learning systems can help prioritise candidates that appear most promising based on existing biological knowledge and experimental results.
The most promising candidates can then move into experimental testing using Yuna.This combination could potentially reduce the amount of time and resources required to identify promising compounds. Instead of relying entirely on conventional trial-and-error approaches, researchers can use computational predictions to guide laboratory experiments and then use real biological results to refine the predictions.The quality and relevance of the experimental data therefore remain central to the overall strategy.
Public Validation of the Yuna Platform
Outer Bio has now made some of its validation work publicly available through a preprint published on bioRxiv.In the research, the company reported that Yuna was able to reproduce three distinct examples of skin biology over a four-week period.The first involved a psoriasis-like inflammatory response. According to Outer Bio, the inflammatory condition was sustained for approximately three weeks and subsequently reversed using a clinically used JAK inhibitor.
The second example focused on cellular senescence in aged donor tissue. Researchers reported that senescence-associated signatures could be reduced using a combination of senolytic compounds.The third involved ultraviolet-induced photoaging. In this experiment, UV-related skin injury was mitigated using a topical sunscreen.These examples are significant to the company’s platform strategy because they demonstrate that Yuna can maintain biological processes over a period longer than conventional short-duration models.
The ability to reproduce known biological responses is also important for a discovery platform. Before researchers can confidently use a system to search for new compounds, they need evidence that it can reproduce the effects of compounds and treatments whose activity is already understood.Outer Bio says it has also tested Yuna against a broader collection of benchmark bioactives and therapeutic agents spanning areas such as anti-aging, antioxidant activity, inflammation, senescence and skin protection.
These experiments have contributed to an internal reference dataset that the company expects to expand as additional compounds and donor samples are evaluated.
Potential Applications Across Skin Health
Although Outer Bio’s primary objective is internal discovery, the company says Yuna can also be made available through selected partnerships.Potential users include biopharmaceutical companies, ingredient suppliers and consumer skincare brands.For biopharmaceutical companies, the platform could provide a way to study potential therapies in living human skin before progressing to later stages of development.
Ingredient suppliers could potentially use the system to evaluate the efficacy or biological activity of new compounds and formulations.Consumer brands may also be able to use the platform when investigating ingredients intended for applications such as skin aging, inflammation, antioxidant protection or environmental damage.
The ability to conduct longer-duration studies could be particularly useful when evaluating biological mechanisms that cannot be adequately assessed during short experiments.Outer Bio says external partners can access Yuna for long-duration efficacy testing, skin toxicity assessment and collaborative discovery programmes focused on specific areas of skin biology.
Addressing the Limitations of Conventional Models
One of the central arguments behind Yuna is that no single laboratory model perfectly reproduces human biology.Cell cultures are relatively straightforward to work with and can be valuable for understanding specific mechanisms. Reconstructed skin models provide additional structural complexity. However, these systems may not capture the full range of interactions present in native tissue.
Ex vivo human skin offers greater biological relevance, but its usefulness has historically been limited by the short period during which samples remain viable and functional.Yuna is designed to combine the biological relevance of native human tissue with an extended experimental window.
By preserving multiple skin layers, different cell populations and immune activity, the platform aims to allow researchers to study biological changes as they develop rather than relying solely on immediate responses.This could be particularly relevant for skin aging, where many important changes occur gradually.
The Role of AI in Future Bioactive Discovery
Artificial intelligence and machine learning are becoming increasingly important in life sciences research, but the availability of high-quality biological data remains a major challenge.Outer Bio’s approach attempts to address this issue by placing experimental biology at the centre of its computational strategy.
Polansky said AI models are advancing rapidly, but the experiments used to train and validate those systems need to keep pace. In the company’s view, biological data cannot simply be generated through computation; it must be produced through experiments using relevant biological systems.
Yuna is therefore positioned as the experimental foundation supporting Outer Bio’s AI-driven discovery engine.As more donor samples, treatments and biological measurements are added, the company expects its dataset to become increasingly valuable for identifying relationships between compounds and skin biology.
The long-term objective is to create a discovery system capable of moving from millions or potentially billions of candidate structures toward a smaller group of experimentally validated bioactives.
A Potential Shift in Skin Research
Outer Bio’s emergence from stealth marks an important step in the company’s development and introduces a technology aimed at addressing a longstanding challenge in skin science.The company’s strategy combines three major components: long-duration living human skin, large-scale biological data generation and machine-learning-guided compound discovery.
If the Yuna platform continues to demonstrate reproducible results across different skin conditions, donor populations and compounds, it could become a useful tool for researchers seeking more clinically relevant models for skin research.The technology could also support a broader shift toward human-relevant testing methods, particularly in areas where traditional cell-based models struggle to reproduce the complexity and time-dependent nature of human biology.
For the skincare and life sciences industries, the potential impact extends beyond discovering another individual ingredient. A platform capable of generating detailed biological information from living human tissue could help researchers understand why certain compounds work, why others fail and how biological responses change over time.
Outer Bio is now making Yuna available through selected partnerships while continuing to use the platform for its own discovery programmes. As its proprietary dataset grows, the company aims to strengthen the connection between experimental human biology and artificial intelligence.
Ultimately, the goal is to improve the ability to discover and validate new bioactives that can be translated into skincare products and potentially therapeutic applications. By extending the experimental window from days to more than four weeks, Outer Bio believes Yuna can provide researchers with a clearer view of the biological processes that shape human skin health, aging and disease.
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