Animal testing has long served as the safety foundation for new drug development. However, regulatory shifts are quietly changing this paradigm. On April 10, 2025, the FDA released its "Roadmap for Reducing Animal Testing in Preclinical Safety Studies," outlining a gradual phase-out of animal testing for monoclonal antibodies and other drugs in favor of alternative technologies like organoids. Nearly one year post-policy, substantive progress from this "shockwave" is becoming evident.
The changes are visible in two key dimensions. First, regulatory acceptance of new technologies has seen concrete action. In January of this year, the FDA accepted a Letter of Intent for the liver organ-on-a-chip ISTAND program, led by the 3Rs Collaborative, with participants including Axiom/LifeNet Health, BioIVT, CN Bio, and Xellar. Furthermore, just two months later, the FDA issued a draft guidance titled "General Considerations for Using Novel Methodologies in Drug Development," encouraging submissions of experimental data from new approaches like organ-chips and organoids to enhance clinical trial safety and reduce reliance on animal testing. This signifies that non-animal testing alternatives have taken a crucial first step within the official regulatory framework, moving beyond theoretical concepts.
Second, industry competition is intensifying, with Chinese companies beginning to emerge. The list of participants in the FDA's liver organ-on-ahip ISTAND program includes Axiom/LifeNet Health, BioIVT, CN Bio, DefiniGEN, InSphero, Lena Biosciences, Inc., PredictCan, TissUse, and Xellar. Xellar is the sole Chinese organ-on-a-chip company selected for this initiative.
Capital is now accelerating its bets on this sector. Recently, Xellar completed a Series A financing round exceeding 200 million yuan, exclusively led by China Life Private Equity, with continued investment from existing shareholders including XtalPi, Yayi Capital, and Legend Capital. The funds will primarily be used to build a next-generation bio-intelligent infrastructure centered on humanized models and mechanism research, including expanding its organ-on-a-chip disease model systems, establishing a scalable mechanism research platform, and continuing high-throughput, standardized collection and accumulation of real biological data.
However, the industry still faces a lengthy validation period before truly entering the "AI-powered drug discovery" era. A stark reality is that no drug fully developed by AI has yet achieved global market approval. Highly anticipated pioneers also carry significant risks. For instance, Exscientia's "DSP-1181," the world's first AI-designed molecule, ultimately failed after not meeting targets in Phase 1 clinical trials. This gap between ideal and reality stems from the inherent complexity of drug development and regulatory policies.
Despite the finish line for "AI-originated new drugs" remaining uncrossed, breakthroughs by domestic companies are underway. In February 2025, Signet Therapeutics announced its collaboration with XtalPi on the first-in-class drug candidate SIGX1094, which officially received FDA Fast Track designation. Progress is now visible. On April 2, Signet CEO Zhang Haisheng stated that SIGX1094's initial indication is gastric cancer, a rare disease in the US. With both FDA Orphan Drug and Fast Track designations, the drug may be eligible for market application upon completion of Phase II trials. "We look forward to potentially becoming the first AI-designed and developed drug to gain approval," Zhang said.
From building foundational organ-on-a-chip model infrastructure to advancing innovative pipelines into later clinical stages, AI is rapidly transforming the industry. In this paradigm shift, regulatory alignment and industrial pace are key variables reshaping pharmaceutical R&D. As the only Chinese organ-on-a-chip company on the FDA's Letter of Intent list, Xellar is not just an observer but a keen participant sensing industry shifts. Regarding the pace of Chinese regulatory advancement in organ-on-a-chip and the real competitive moat of Chinese innovators in foundational infrastructure, a recent dialogue was held with Xellar CEO Xie Xin.
**Sino-US Industry Resonance** "If regulatory policies hadn't changed, I believe drug development would still follow the animal-to-human testing sequence even 100 years from now. Fortunately, regulators are now allowing attempts with new technologies," noted a Shanghai-based innovative drug investor. This sentiment reflects regulatory acceptance of AI-driven changes in drug development. The US FDA's "Roadmap" explicitly outlines a gradual shift from animal testing to alternative technologies like organoids. This is not an isolated trend. Just over ten days later, China's NMPA and other departments jointly issued the "Implementation Plan for Digital and Intelligent Transformation of the Pharmaceutical Industry (2025-2030)," highlighting 41 technological innovation scenarios, including "animal model data mining and virtual animal experiments" to address issues like high demand for animal alternatives and discrepancies with human results.
Against this backdrop, companies like Xellar joined the industry's practical test. In June 2025, the FDA initiated a joint validation for drug-induced liver injury involving nine global organ-on-a-chip platforms, including Xellar. As the sole Chinese company on the list, Xellar's core business relies on its "organ-on-a-chip + AI" platform, conducting platform-driven collaborative R&D with global pharmaceutical companies focused on specific disease models or R&D challenges.
According to Xie Xin, the stability, reproducibility, and ability to provide complete, standardized, and traceable raw data and technical documentation were key reasons for Xellar's selection in the FDA validation project.
**Q: What are the current differences in the pace and focus of organoid standards advancement between China and the US? What are the primary concerns and core risk assessment points for regulators in both regions?** **Xie Xin:** Overall, China and the US show "similar paths but different paces and focuses." The US FDA's approach is more systematic and clear, centered on mechanisms like iSTAND, gradually building a complete framework from technical validation to qualification. The focus is on promoting the application of new methodologies in specific drug development contexts through multi-center validation, standardized data requirements, and clear "Context of Use." In other words, the US emphasizes whether the technology is reliable enough for specific use cases to be integrated into decision-making systems.
In contrast, China's CDE is currently in a phase more focused on "consensus building," emphasizing broad input from academia and industry to gradually refine standards through guideline discussions and seminars. China places greater emphasis on the applicability and industrial feasibility of technological pathways, exploring implementation methods suitable for the local R&D system while ensuring scientific rigor.
Currently, regulators in both regions share two core concerns: first, reproducibility and consistency—whether stable and comparable results can be obtained across different batches, experimental conditions, and even laboratories; second, correlation with human outcomes—whether the model genuinely improves predictive capability for clinical results, beyond just appearing more complex.
Building on this, the US FDA is more concerned with "whether it can be trusted in specific use cases," thus focusing on data integrity, standardization, and performance in multi-center settings. Chinese regulators are relatively more focused on "how to implement within the existing system," including practical factors like technical operability, cost, and industrial support capabilities.
**Q: What impact has the FDA's "Roadmap" had on your performance and financing?** **Xie Xin:** Since 2023, we have collaborated with the FDA and 3Rs-related organizations, gradually entering multi-center validation and the iSTAND review system. This has driven our platform capabilities towards becoming a "regulatorily acceptable tool." As policies explicitly encourage alternative technologies and we are within the regulatory validation pathway, clients' perception of our technology has shifted from "cutting-edge exploration" to "a solution with前瞻 regulatory value."
From a commercial perspective, collaboration depth and payment structures have evolved. Initially, pharmaceutical companies engaged mostly through exploratory, small-scale projects to validate feasibility. However, over the past year, with clearer regulatory signals and continuous validation of our delivered data, collaboration models have deepened. For example, some global pharma companies, after initial projects, no longer treat organ-on-a-chip as merely a "supplementary experiment" but integrate it into routine evaluation systems for specific R&D stages, engaging in sustained collaboration around specific models or application scenarios. These collaborations are typically not one-off validation projects but focus on phased, multi-batch data production and mechanism research, reflecting stronger long-term investment willingness. Essentially, clients are no longer paying just for "technology experimentation" but for "reducing R&D uncertainty and failure risk."
**Q: Why was your company selected as an FDA partner? Does this help build a competitive barrier?** **Xie Xin:** The core reasons are twofold: first, our platform demonstrates stability and reproducibility in predicting drug responses, delivering consistent results across different batches and conditions; second, we can provide complete, standardized, and traceable raw data and technical documentation, which is particularly critical for regulators. The regulatory pathway itself does create a barrier through time and trust accumulation. Even if latecomers possess similar technical capabilities, they would require considerable time to complete the same validation and alignment processes.
**Q: Compared to other organ-on-a-chip companies domestically and internationally, what is your most irreplaceable competitive differentiation?** **Xie Xin:** Primarily, we have built a dry-wet closed-loop bio-intelligence system centered on 3D Bio Intelligence, demonstrating competitiveness across three dimensions: regulatory acceptance, industrial validation, and engineering capability.
Regarding the regulatory pathway, our platform was designed from the outset around new drug R&D and regulatory submission systems, with deep involvement in advancing FDA-related standards and Novel Alternative Method frameworks. Regulatory alignment capability fundamentally determines whether a technology can transition from "usable" to "adopted," which is one of the most critical and difficult hurdles in the industry.
In terms of industrial validation, we have established deep collaborations with several global leading pharmaceutical companies, consistently delivering high-quality data and research results in real R&D scenarios. Through continuous accumulation of high-quality human data and application scenarios, we are building a long-term competitive advantage driven by real-world data.
In engineering and system capabilities, we have advanced the organ-on-a-chip platform from research-grade to industrially stable operation. For instance, achieving a CV below 10% and a Z' factor above 0.5 in high-throughput experimental systems indicates the platform meets standards for consistency, reproducibility, and signal discrimination required for scalable application. This is the foundation for stable large-scale data production and model training. Currently, by deeply integrating AI with human biological models, we have formed a positive feedback system of "data generation - mechanism analysis - model training - experimental validation," enabling the platform to continuously self-evolve.
Long-term, our goal is not merely to provide tools or services but to build a data and computational infrastructure centered on human biology, driving drug R&D from experience-driven to mechanism-driven.
**Q: AI algorithms and data quality are inseparable. What are the main sources of your company's data, and is it exclusive?** **Xie Xin:** The primary source is the 3D wet-lab data generated by our proprietary organ-on-a-chip platform, which is our most important and exclusive asset. Unlike data reliant on public databases or single in vitro models, our data is continuously generated under conditions closer to real human physiology, through standardized, reproducible, and quantifiable experimental systems, with clear origins, complete process records, and traceability. This data includes multiple cell types, dynamic fluid environments, and multi-dimensional functional readouts, encompassing not only traditional indicators but also high-dimensional dynamic physiological information like metabolic activity, barrier function, and mechanical signals. The value lies not just in "result data" but in "process data" closely linked to biological mechanisms, supporting deeper mechanistic analysis and model training.
Additionally, we selectively integrate and clean public database data as a supplementary source to enhance model generalization. However, the core competitive advantage truly lies in the high-quality human data continuously generated via our proprietary platform. This data is inherently non-replicable due to the complex engineering systems and experimental capabilities required, and it accumulates over time with project progression, creating a compounded advantage of scale and duration.
**Capital Operations as a Means, Not an End** As regulatory policies demonstrate openness towards organ-on-a-chip technology, domestic pharmaceutical companies are gradually increasing their acceptance. According to Xie Xin, domestic companies typically start with single project collaborations, transitioning to long-term framework agreements after validating the technology's value. Xellar has already signed long-term cooperation frameworks with several leading domestic and international biotech companies.
Alongside accelerated commercialization, Xellar's considerations regarding a potential listing have attracted attention. Regarding this, Xie Xin views capital operations as a means rather than an end, with any specific timeline dependent on the company's development stage and market conditions.
**Q: Focusing on the domestic market, what is the current acceptance level of organoid technology among Chinese pharmaceutical companies? What common pain points and concerns do they face when adopting this technology? In terms of commercial implementation, what are the characteristics of local pharma's willingness to pay and procurement decision cycles for this cutting-edge technology?** **Xie Xin:** Acceptance is indeed increasing, especially among innovative drug companies with urgent needs for IND application support. Common pain points include concerns about the credibility of data from new technologies and the complexity of internal validation processes. From our observations, the payment willingness of local companies shows a "pilot first, platform later" characteristic, usually starting with single project collaborations and moving to long-term frameworks after value validation. We have already signed multi-year collaborations with several leading domestic biotech companies, and procurement decision cycles have shortened significantly.
**Q: What is your company's primary business model currently? Is it more akin to a technology service CRO model? Who are your main clients - pharmaceutical companies or CXOs? Can you break down the current revenue structure?** **Xie Xin:** Essentially, Xellar is not a traditional CRO company but a bio-intelligence platform company centered on 3D Bio Intelligence. Organ-on-a-chip and AI are our technological foundations. Our goal is not to provide one-off services but to build platform capabilities that continuously generate data, analyze mechanisms, and drive discovery.
Currently, our business model is primarily reflected in platform-driven collaborations. Specifically, we conduct joint projects with pharmaceutical companies focused on specific disease models, organ systems, or R&D questions, providing high-quality human data and mechanism research support through our organ-on-a-chip and AI capabilities. While these collaborations may appear as project revenue, their essence is closer to technology cooperation based on platform capabilities, rather than a traditional fee-for-service model per experiment.
Regarding client structure, our core clients are primarily from the industry side, including global pharmaceutical and biotech companies, extending also to cosmetics, food, and nutrition health sectors. These clients share the common goal of seeking evaluation and mechanism research tools closer to real human responses. We also maintain collaborations with regulatory and academic institutions to jointly advance technical standards and methodologies.
From a revenue structure perspective, it currently主要包括 includes three categories: first, platform collaboration revenue围绕 specific R&D challenges, i.e., joint research projects based on organ-on-a-chip and AI; second, ongoing data and model service revenue formed as collaborations deepen, such as multi-batch research and data accumulation around specific models; third, gradually explored platform licensing and longer-term R&D collaboration models, such as deeper cooperation on disease models or mechanism research.
Short-term, the focus is on platform collaborations to quickly enter real R&D scenarios and accumulate data; medium-term, we aim to expand towards scalable output of model platforms and data capabilities; long-term, the goal is to evolve towards mechanism research, new target discovery, and proprietary pipeline development based on accumulated human data and AI capabilities.
Superficially, our business model might resemble a CRO's, but the underlying logic is entirely different. We are not delivering one-time experimental results but building a bio-intelligence system that continuously generates value and evolves.
**Q: Beyond pharmaceutical R&D, we see your technology extending to areas like cosmetic screening. What is the core logic behind these cross-sector expansions?** **Xie Xin:** Fundamentally, organ-on-a-chip and AI address the same core problem: how to more accurately replicate human physiological responses in vitro and convert them into computable, analyzable data. This capability is inherently cross-sector. Whether in drug R&D, cosmetic evaluation, or food and nutrition health, the essential question is "the mechanism of action and safety of a substance in the human body."
These cross-sector applications, in turn, strengthen our core capabilities. Different fields provide diverse types of biological stimuli and phenotypic data, allowing us to accumulate human data under broader conditions and enrich our AI models' understanding of human biology. This process of "multi-scenario data input - unified model learning" continuously enhances the platform's generalization ability, ultimately benefiting the core scenario of drug R&D.
Commercially, this allows for rapid revenue and data accumulation in relatively shorter-cycle application scenarios while reducing dependence on a single industry cycle, enhancing the company's stability across different market environments. Overall, we don't view this as "crossing sectors" but as the natural extension of the same underlying capability into different scenarios.
**Q: Is there a specific timeline plan for an IPO?** **Xie Xin:** I have always believed that capital operations are a means, not an end. At this stage, our core task remains to continuously strengthen our technological moat, deepen collaborations with global clients, and accumulate high-quality human data assets, enabling the company to create long-term, stable value. When the company reaches an infrastructure-level maturity in technology, business, and regulatory dimensions, the capital market will provide a natural answer. Of course, we are actively making relevant preparations and plans. Under suitable timing and market conditions, we hope to advance the company into the capital market, becoming a benchmark listed entity in the 3D Bio AI field, injecting more confidence and certainty into the industry. However, any specific timeline must be considered in conjunction with the company's development stage and market environment.
Comments