A New Era in Drug Discovery Begins as FDA Embraces Human Models
Discover how FDA and NIH reforms are ushering in a new era of organoids, AI and ethical drug development.
In April 2025, the US Food and Drug Administration (FDA) announced a pivotal shift in drug development policy. The agency has set out to reduce, refine and potentially replace its long-standing requirement for animal testing in the development of monoclonal antibodies and other pharmaceuticals. As the FDA points out in its recently released roadmap for the transition to non-animal models, over 90% of drugs that appear safe and effective in animal models have safety and/or efficacy issues in humans and fail to gain approval. The goal? To promote more predictive, human-relevant science while enhancing safety, speeding up approval timelines and cutting research and development costs.
This landmark initiative leverages a growing arsenal of new approach methodologies (NAMs), including artificial intelligence (AI)-based computational models, advanced cell lines and organoid systems. Effective immediately, investigational new drug applications are encouraged to include data generated through these alternatives. Additionally, the FDA will begin accepting real-world safety data from international sources with comparable regulatory oversight to support drug evaluations.
“For too long, drug manufacturers have performed additional animal testing of drugs that have data in broad human use internationally,” said FDA commissioner Dr. Martin Makary. “This initiative marks a paradigm shift in drug evaluation and holds promise to accelerate cures and meaningful treatments for Americans while reducing animal use.”
What does this mean for scientists?
“What felt like a distant possibility just a few years ago is now the new normal,” said Dr. Pradipta Ghosh, a professor in the Departments of Medicine and Cellular & Molecular Medicine at the University of California, San Diego.
“The National Institutes of Health’s (NIH) April 2025 policy marks a definitive cultural and scientific turning point,” Ghosh said. “By requiring that all biomedical research shift away from an overreliance on animal models – unless strongly justified – and embrace human-relevant platforms like organoids, organ-on-chip and computational modeling, the NIH has joined the FDA in declaring that relevance and translational potential must trump convenience or convention.”
This shift, Ghosh noted, has been building for more than a decade. “In 2013, former NIH director Dr. Elias Zerhouni stated plainly: ‘We have moved away from studying human disease in humans... The problem is that it hasn’t worked, and it’s time we stopped dancing around the problem.’”
The challenge lies in the limitations of animal models, which have consistently failed to capture the complexity of human disease. “While they will always retain a place in some areas of science, the era of using them as a default is over,” Ghosh added.
“We now have an unprecedented opportunity – and responsibility – to build models that better reflect human biology and diversity. The rise of human-derived 3D systems like organoids is not just a technological advance – it is a moral, scientific and strategic imperative for the future of medicine.”
The FDA’s proposed roadmap further accelerates this shift by encouraging scientists to adopt next-generation tools earlier in drug development. AI-powered simulation platforms now enable researchers to model drug absorption, distribution and toxicity in silico, reducing reliance on early-stage animal studies. These platforms can predict how monoclonal antibodies behave in tissues and anticipate adverse effects using molecular structure and clinical data.
Equally transformative are human-relevant biological models – such as organoids and organ-on-chip systems – that mimic the structure and function of real human organs. These systems provide powerful new ways to test safety and efficacy in physiologically relevant environments. “For instance, researchers can now detect organ-specific toxicities in systems that reflect human heart, liver or immune function more accurately than traditional animal models,” Ghosh explained.
“The demand for organoid-compatible technologies is set to skyrocket. And the signs are already here. Study sections are no longer just encouraging the inclusion of 3D models – they are critiquing their absence,” she continued.
Yet, Ghosh cautioned that the rapid growth of organoid research comes with risks. “Organoids have a lot of limitations. For one, they’re incomplete and can vary a lot because people and their habits are different. They are made in many ways (i.e., different laboratory protocols), using different materials, and often grown in different brews, embedded in artificial gels that lack the stiffness of the scaffold in the real human tissues. Sometimes, only a small number of organoids – ones that don’t reflect the diversity of real patients – are used to make big claims, mainly to cut costs. If these models are not carefully checked, quality checked through rigorous phenotypic and functional measurements or don’t match real-life conditions, they could lead to another reproducibility crisis, maybe even worse than what we saw with animal models.”
“What we need is a smart strategy – one rooted in the art of abstraction,” she explained. “And when it comes to abstraction, few examples are more powerful than Picasso’s The Bull. In that series, he strips away detail through each sketch, reducing the form to a few bold strokes that still capture the essence of the animal. We apply the same principle to disease modeling at UC San Diego’s HUMANOID™ Center, through our BioDESIGN program. Our HUMANOIDs are those bold strokes – simplified, yet essential representations. By leveraging large, diverse patient datasets – hundreds to thousands per study – we identify traits that are biologically universal across disease states. Aligning our models to these conserved features allows us to build organoid platforms that are predictive, scalable and clinically reliable.”
“Just as inbred, caged mice failed to capture human diversity, leading to poor clinical translation, we must ensure that organoid models reflect the breadth of human variation. Throughput should not come at the cost of diversity. To truly mirror clinical trial populations in a dish, we need technologies that can rigorously measure the essentials, as well as minimize the tradeoffs between scale, scope and biological richness,” said Ghosh.
“This will also require investments in tools for high-throughput analysis, imaging, AI-based phenotyping and standardized protocols. But if we get it right, we won’t just be replacing animal models – we will be building something better,” she added.
Upcoming regulatory updates from the FDA will soon formalize how these NAMs can meet preclinical testing requirements. Scientists who adopt these methods early may benefit from streamlined approval processes – a powerful incentive to shift toward innovative, ethical and cost-effective drug discovery strategies.
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“In disease areas where animal models have been particularly poor predictors – such as inflammatory bowel disease, neurodegenerative diseases and even most cancers – the expectation is shifting toward human-derived, patient-specific systems. It’s entirely plausible that in the coming years, animal models will be restricted – or even banned – for preclinical research in these domains. In fact, the latest NIH announcement promises that it will do so.”
A boost for instrument makers
The FDA’s updated roadmap is driving innovation in tools and technologies that support non-animal testing methods. In response to this shift, Agilent Technologies launched the Seahorse XF Flex Analyzer at the American Association of Cancer Research (AACR) Annual Meeting 2025 – a cutting-edge platform designed to analyze metabolism in three-dimensional (3D) cell cultures, organoids and co-cultures in real time.
“Organoid and 3D tissue models present several challenges for metabolic analysis due to their structural complexity and biological variability,” said Dr. Mark Garner, director of the translational research market at Agilent Technologies. “Unlike two-dimensional (2D) cultures, these models exhibit gradients in oxygen, nutrients and waste, which can obscure metabolic signals and complicate data interpretation. Their heterogeneity in size and composition also makes normalization difficult, while their dense architecture can limit reagent penetration and reduce assay sensitivity.”
According to Garner, the XF Flex Analyzer addresses these challenges by combining precision engineering with workflow flexibility. “The XF Flex Analyzer uses a specialized geometric 3D capture microplate and optimized assay kits to ensure consistent positioning and reagent exposure across complex 3D structures,” he detailed. “Its high sensitivity enables detection of subtle metabolic changes, while automated normalization tools help account for variability in organoid size and composition. Together, these features allow researchers to generate reproducible, high-resolution metabolic data from physiologically relevant 3D models.”
Metabolism is a critical driver of disease progression and drug responses and is recognized as a hallmark of cancer. 3D biological models provide more physiologically accurate insights than traditional 2D cultures. The Seahorse XF Flex Analyzer delivers precise, real-time bioenergetic measurements in 24-well plates, helping researchers detect subtle metabolic changes that may indicate toxicity or therapeutic potential.
“Agilent had been anticipating this since the FDA Modernization Act 2.0 was signed in December 2022,” Garner explained. “In preparation, Agilent has been actively evolving its product portfolio to meet the growing demand for non-animal preclinical testing tools, particularly in organoid-based research. A centerpiece of this strategy was the launch of the Agilent Seahorse XF Flex Analyzer.”
To further support these workflows, Agilent has introduced complementary tools such as the XF 3D Capture Microplate and XF 3D Mito Stress Test Kit, designed specifically for mitochondrial function analysis in 3D tissues. These advancements simplify the adoption and scaling of NAMs in alignment with the FDA’s vision for modernized testing strategies.
“To be validated as alternatives to animal testing, metabolic analysis platforms like Agilent’s XF Flex Analyzer need to meet several key regulatory expectations,” Garner noted. “They must demonstrate scientific validity by reliably replicating human physiological responses, particularly in advanced 3D models such as organoids or tissue slices. They must ensure reproducibility and robustness, meaning the data generated should be consistent across different laboratories and experimental conditions. Finally, these platforms must align with applicable regulatory standards and guidance documents, which are often monitored through formal regulatory intelligence systems to ensure ongoing compliance.”
The road ahead
With this significant policy shift, the FDA is positioning itself at the forefront of modern regulatory science. The move reflects growing pressure from lawmakers and the scientific community to align drug testing practices with 21st-century technologies and ethical standards.
“Transitioning from animal models to organoids is as exciting as it is daunting. The challenges are real, but they’re surmountable – and the rewards are worth it,” Ghosh said.
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There are, however, critical hurdles to address, from ensuring scalability and cost-effectiveness to meeting regulatory standards, before these technologies can be fully integrated into practice.
“Culturing organoids is more expensive up front than maintaining an animal colony. Media components, Matrigel, cytokines and bioreactors all add up. But costs are falling with scale and automation – and unlike animals, organoids can be frozen, revived and shared,” Ghosh explained.
Expertise is another barrier. “Culturing organoids requires a different skill set than traditional cell or animal work. Success demands training in 3D culture techniques, tissue engineering and real-time phenotyping – fields that haven’t traditionally overlapped. But the rise of shared cores and training centers is helping bridge this gap,” she said.
Infrastructure presents perhaps the biggest challenge. “Not every lab has the necessary facilities or proximity to a clinic. Building human organoids requires patient tissue, clinical coordination, Institutional Review Board (IRB)-approved consent and biobanking – all of which require institutional commitment and logistical sophistication,” noted Ghosh.
“With patient-derived models come new responsibilities. Genomic data must be handled with care, particularly when samples are linked to individuals – even through honest broker systems. HIPAA (Health Insurance Portability and Accountability Act) compliance, secure data sharing and reproducible pipelines are essential.”
Standardization is another area where the field must advance. “Unlike animal models, organoids lack widely accepted norms for ‘n’ size, quality control and functional relevance. What counts as a reproducible phenotype? How much variation is acceptable? This is where the field must coalesce around shared benchmarks and quality assurance protocols.”
Despite these hurdles, Ghosh emphasized that the organoid revolution is already underway. “The labs that succeed will be those that build collaborative bridges – with clinicians, data scientists and biologists – and embrace the complexity of the human system not as a problem, but as the point.”
For scientists, this transition offers an opportunity to rethink how safety and efficacy are evaluated, shifting away from legacy models toward faster, more reliable and human-focused strategies. For companies developing laboratory instrumentation, it presents a chance to lead in providing the tools needed to meet the growing demand for animal-free testing.
The end of mandatory animal testing in key areas of drug development is more than a regulatory update – it marks the beginning of a new scientific era.
