Rare diseases demand sharper early decisions, and induced pluripotent stem cell (iPSC)-derived models are helping teams get there faster. Here, iXCells Biotechnologies' Steve Smith explains how these systems support better target validation, scalability, and precision strategies.
How are custom iPSC-derived models changing the way drug development is approached, particularly compared with traditional drug screening?
Custom iPSC-derived models are changing drug development by shifting discovery earlier toward human biology rather than downstream translation. Traditional screening approaches are often optimized for speed and throughput, but they can miss whether a target or mechanism is truly relevant in patients. iPSC-based models allow that question to be asked much earlier.
iPSCs are a powerful driver of preclinical insight, particularly in early target validation and mechanism-of-action studies. Interrogation of disease phenotypes in cells derived directly from patients enables researchers to gain confidence much earlier about whether a therapeutic hypothesis is grounded in human biology. This results in fewer false positives advancing and clearer “no-go” decisions when biology doesn’t support the program. Whilst this represents an important direction for discovery, some questions are still best addressed with primary cells or complementary systems, and those approaches continue to play a valuable role alongside iPSC-based models. The goal is to apply the most appropriate biology to the question at hand, rather than relying on a single system.
A key area of progress has been the development of more standardized, end-to-end approaches that helps teams generate consistent, decision-ready data from iPSC-based models. Ultimately, it’s about starting with biology that more closely reflects the patients a therapy is intended to serve.
In what ways do iPSC-based models differ from traditional cellular models, and where do they offer the greatest value?
The key difference between iPSC-based models and traditional cellular systems is context. iPSCs preserve patient-specific genetics and can be differentiated into disease-relevant cell types that are otherwise difficult to access. That allows researchers to study disease biology as it exists in humans, rather than relying on heavily engineered proxies.
iPSCs offer their greatest value in early discovery, where understanding if a target, pathway, or phenotype is disease-driving can materially change development decisions. They are particularly powerful for complex and genetically influenced diseases, where traditional models often fall short. That said, science is not dogmatic. iPSC-based models are not a universal solution and primary cells and established cellular models remain important tools, especially when addressing specific functional questions or validating findings across systems. Model selection should always be driven by the biology and the question being asked.
Across all of these approaches, the emphasis increasingly needs to be on scalability, continuity, and confidence in the data. Whether data is generated in iPSCs, primary cells, or later-stage 3D systems, they need to be comparable and trustworthy. That’s where integrated workflows and standardized characterization become as important as the model itself.
What are the key considerations when developing disease-specific iPSC models, and how can standardized platforms support this process?
The most important consideration in developing disease-specific iPSC models is clarity of purpose. Scientists need to define which phenotypes matter and how those phenotypes will inform decisions. Without that alignment, even well-executed models can struggle to deliver actionable insight.
From a technical standpoint, consistency matters. Genetic stability, differentiation fidelity, functional maturity, and reproducibility all become increasingly important as models scale or support multiple programs. This is where standardized platforms can play a meaningful role.
Standardized platforms can help provide structure across donor sourcing, reprogramming, differentiation, and characterization, allowing iPSC-derived data to be generated in a consistent and comparable way. In practice, these approaches focus standardization on what matters most, supporting better science without constraining it.
Importantly, standardization should not override scientific judgment. Many disease programs benefit from combining iPSCs with primary cells or other model systems. While iPSC-based strategies are increasingly viewed as foundational, platforms and workflows need to be designed to support flexibility rather than enforce a single approach.
Rare diseases present unique scientific and logistical challenges. How can iPSC-based models help address some of these challenges in rare disease research?
Rare diseases are inherently difficult to study. Many patients are spread across wide geographies, and the biology of these conditions often varies from one person to the next. This variability makes it hard to develop models that accurately reflect the diverse mechanisms driving disease, and it is one reason why traditional models have struggled to deliver therapies that translate into clinical benefit.
iPSC-based models help address these challenges by providing consistent, human‑relevant systems that capture patient‑specific biology. When cells are reprogrammed from individual patients and differentiated into disease‑relevant cell types, researchers can observe disease phenotypes in a context that more closely mirrors the human condition. This scalability and fidelity help teams generate deeper insight into both shared and unique aspects of rare diseases.
One example of this approach is Project Mosaic, a patient‑led initiative focused on sporadic amyotrophic lateral sclerosis (sALS). As part of an initial pilot, patient‑specific iPSC models were generated better reflecting the heterogeneity seen in ALS patients. The goal of Project Mosaic is to generate consistent, transcriptomic signatures from ALS patient lines and use those models to inform precision medicine strategies that could better match patients with therapies.
This work demonstrates how patient‑derived iPSC models can do more than reproduce disease biology. They can be used systematically to identify disease subtypes, support precision drug matching, and help transform how rare disease programs prioritize targets and design studies. When coupled with standardization, deep characterization, and collaboration across the research community, iPSC‑based models can shorten development timelines and bring us closer to effective therapies for conditions that have long lacked them.
How do more physiologically relevant models translate into better decision-making for therapies ultimately intended for patients with rare diseases?
Physiologically relevant models improve decision-making by reducing uncertainty early. In rare diseases, where patient populations are small and development paths are narrow, early confidence in biology is especially important.
iPSC-derived models allow teams to evaluate therapeutic hypotheses in a human context before significant resources are committed. When combined with more complex systems such as organoids, they can reveal disease behaviors that simpler models miss. We believe this layered approach represents the future of preclinical decision-making.
However, relevance doesn’t come from complexity alone. The most effective programs integrate insights across multiple systems, supporting iPSC-forward strategies while enabling cross-platform validation, rather than forcing artificial choices between models.
Ultimately, better models lead to clearer decisions. Scientists, companies, and personalized medicine works best when they know which programs to advance, which to pause, and which to stop. For patients with rare diseases, that clarity can translate directly into faster progress toward therapies that truly have a chance to succeed.
Beyond rare diseases, which therapeutic areas could benefit from iPSC technology?
We expect iPSC technology to continue expanding across a wide range of therapeutic areas. Oncology and neurodegenerative diseases have already demonstrated strong adoption, while metabolic, inflammatory, and multisystem conditions are increasingly benefiting from human-relevant models. Rare diseases remain an important focus, where iPSC-derived systems help researchers better understand disease biology and identify potential therapeutic strategies.
Across these areas, the common theme is the need for biology that reflects human disease early in development. iPSC models allow researchers to study disease-relevant cell types and patient diversity at scale, enabling deeper insights into complex conditions and accelerating the discovery of effective therapies. In oncology, iPSC-based models can help evaluate tumor biology, drug responses, and potential toxicities more predictively. In neurodegenerative diseases, they support the study of disease progression, cellular vulnerabilities, and mechanism-driven target validation.
Adoption will vary by indication and research context, but the trajectory is clear. The regulatory bodies are also clear that human-relevant models are becoming essential across multiple therapeutic areas. As a result, there is growing focus on developing scalable and reproducible systems that allow researchers to explore thousands of patient-derived lines, generate actionable insights, and advance therapeutic programs more efficiently.
By focusing on disease biology, iPSC technology is helping drive meaningful scientific breakthroughs, improve early decision-making, and bring new therapies closer to patients who need them most.
What innovative advances do you expect to see in the 3D cell modeling and organoid spaces over the coming years.
Organoid models are poised to play an increasingly central role in preclinical research. By capturing the three-dimensional architecture and cell-cell interactions of human tissues, they provide a level of biological fidelity that allows researchers to observe disease processes more accurately. This higher relevance translates into efficiency, enabling teams to prioritize the most promising therapeutic candidates early and reduce resources spent on approaches unlikely to succeed. Midbrain organoids, for example, are proving valuable in neurodegenerative disease research. They replicate the cellular diversity and network connectivity of human midbrain tissue, offering insights into disease mechanisms and potential therapeutic effects that traditional 2D neuronal cultures cannot. Using these organoids, researchers can evaluate efficacy and safety earlier in the development process, improving the prediction of clinical outcomes.
Safety profiling also benefits from organoid models. Their physiological complexity allows for more predictive assessments of toxicity, helping to identify potential risks before candidates reach animal studies or clinical trials. This can shorten timelines, reduce costs, and ultimately increase the likelihood of success in human studies.
One example of this trend is our recent partnership with Rosebud Biosciences, which brings together iPSC-derived organoid models with standardized workflows. The aim of this partnership is to improve reproducibility and consistency in how organoid systems are generated and applied, particularly in the context of efficacy and safety evaluation. Collaborations like this reflect how the field is working to make complex 3D models more practical and reliable for translational research, supporting acceleration from discovery to therapy.
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