The life sciences are in the midst of a crucial shift, driven by the emergence of organoid-based models and the power of automation. Organoids—three-dimensional cell cultures that mimic human tissue architecture and function—are enabling researchers to ask and answer questions that were once beyond reach. Paired with advances in automation, robotics, and artificial intelligence (AI), these models are transforming drug discovery and preclinical testing, offering a more human-relevant alternative to outdated 2D cell cultures and animal models. This revolution is reshaping the pharmaceutical industry, while also holding the potential to accelerate progress in personalized medicine.
Beyond 2D: The Rise of Organoids
For decades, preclinical research has relied on 2D cell cultures, single-cell-type 3D spheroid models, and animal models, despite their limitations in replicating human biology. Organoids, which are derived from stem cells, offer a more accurate representation of human tissues, recapitulating complex biological processes such as organ-specific functionality and cellular interactions. These miniature self-organizing biological systems are being used to model diseases, test drug efficacy and toxicity, and even explore regenerative medicine.
Traditional preclinical models often fail to predict how therapies will perform in humans. While 2D and simple spheroid cell cultures were originally derived from human tissues, they do not sufficiently mimic diverse cell interactions and tissue complexity. Animal studies, while highly complex and historically the gold standard, do not account for human-specific genetic, molecular, and physiological differences. The result? High failure rates in clinical trials—a trend exemplified by "Eroom’s Law" (the inverse of Moore’s Law), which illustrates the increasing inefficiency of drug development despite technological advances.1
Organoids are emerging as part of the solution to this problem, providing data that is more predictive of human responses, reducing the reliance on animal testing, and offering new opportunities to personalize treatments.
Regulatory Agencies Are Drivers of Change
Multiple forces are driving this shift toward human-relevant models. Regulatory changes, such as the US Food and Drug Administration (FDA) Modernization Act 2.0 and updated National Institutes of Health (NIH) guidelines, are encouraging the adoption of alternatives to animal testing. In 2025, the FDA announced plans to phase out a requirement for animal testing in the development of monoclonal antibody therapies and other drugs, to improve drug safety, accelerate the review process, and reduce research and development costs. In the UK, the Medicines and Healthcare products Regulatory Agency is now enabling an early review of non-animal data to give developers more confidence when making marketing applications based on evidence generated without animal testing. These changes, combined with societal pressures to improve ethical standards, are creating a fertile environment for the rapid adoption of New Approach Methodologies.
In addition, the pharmaceutical industry's growing focus on complex drug modalities, such as antibody-based therapeutics and cell and gene therapies, has highlighted the inadequacy of traditional models. These therapies often rely on human-specific mechanisms that cannot be adequately studied in animals. Organoids, with their human-derived biology, provide a robust platform for testing these next-generation treatments.
Automation: Moving From Variability to Control in Organoid Research
While the promise of organoids is undeniable, their complexity presents significant challenges. Culturing organoids can feel more like an art than a science—labor-intensive, dependent on highly experienced personnel able to troubleshoot and adjust on-the-fly, and often taking weeks (sometimes months) of meticulous work to achieve the desired result. The artisanal process is inherently variable, making scalability and reproducibility difficult to achieve. This is where automation and AI make a critical impact, by replicating expert skill and delivering consistent results across labs and experiments.
A midbrain organoid generated using the CellXpress.ai® Automated Cell Culture System and imaged on the ImageXpress® HCS.ai High-Content Screening System reveals complex neuronal networks. Calcium 6 fluorescent dye enables visualization of neuronal activity with spatial color coding that distinguishes 3D network structures within a 2D image.
Molecular Devices
Researchers at institutions like the University of California, Los Angeles and Emory University are pioneering the integration of automated cell culture platforms to scale organoid research. These systems streamline manual workflows, allowing researchers to grow and maintain organoids with greater consistency and throughput. Automation, together with AI-driven decision-making, not only reduces reliance on specialized personnel but also ensures that their expertise can be deployed across experiments—and between different geographical sites—to improve model reproducibility and data consistency. For example, brain organoids, which require up to 90 days to mature, can now be cultured more reliably at scale, enabling researchers to generate the large datasets needed for translational research.
Advances in precision automation, robotics, and data analytics are enabling labs to overcome historical bottlenecks. These technologies ensure that organoids are cultured under standardized conditions, preserving their quality and functionality. AI-powered platforms can also analyze data in real-time, enabling researchers to make appropriate decisions efficiently on the next steps to take in the culture process.
Real-World Impact of Organoid Models
The adoption of organoid models, supported by automation, is already yielding promising results. Early studies have shown that organoids can predict patient responses to therapies more effectively than animal models. For example, researchers have used patient-derived tumor organoids to test cancer treatments, identifying therapies that are most likely to succeed in clinical trials.2 Such breakthroughs not only save time and resources but also bring us closer to the promise of personalized medicine.
Plus, the scalability, standardization, and transferability enabled by automation is opening new avenues for collaboration. Dedicated organoid research centers and consortia, such as the NIH’s organoid standardization initiative, are fostering knowledge-sharing and the development of best practices. These efforts are critical to ensuring that organoid-based approaches are adopted widely and effectively.
When Organoids, AI, and Automation Intersect
Looking ahead, the combination of organoid technology, automation, and AI promises to revolutionize drug discovery and clinical trials. Over the next decade, we can expect to see an expansion of organoid applications, from modeling rare diseases to testing novel drug modalities. Advances in AI-driven analytics will further enhance the predictive power of these models, enabling researchers to identify biomarkers, optimize drug dosing, and design more effective clinical trials.
Perhaps most importantly, these technologies are paving the way for a future where animal testing is no longer the default option. As regulatory agencies and pharmaceutical companies continue to embrace human-relevant models, the reliance on animal studies is likely to decline significantly. This shift will not only lead to better outcomes for patients, but also improve the ethical standards of research.
The Evolution of Translational Science
The integration of organoid models and automation represents a turning point in translational science. By addressing the inefficiencies of traditional drug discovery methods, these technologies are unlocking new possibilities for precision medicine, reducing the time and cost associated with bringing therapies to market. As the field continues to evolve, the promise of organoids and automation is clear: a more efficient, ethical, and human-centered approach to advancing healthcare.
- Scannell JW, et al. Diagnosing the decline in pharmaceutical R&D efficiency. Nat Rev Drug Discov. 2012;11:191-200.
- Smabers LP et al. Organoids as a biomarker for personalized treatment in metastatic colorectal cancer: drug screen optimization and correlation with patient response. J Exp Clin Cancer Res. 2024;43:61.
