{"id":546619,"date":"2026-07-13T00:17:07","date_gmt":"2026-07-13T00:17:07","guid":{"rendered":"https:\/\/www.newsbeep.com\/ie\/546619\/"},"modified":"2026-07-13T00:17:07","modified_gmt":"2026-07-13T00:17:07","slug":"how-nams-are-transforming-drug-discovery","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/ie\/546619\/","title":{"rendered":"How NAMs Are Transforming Drug Discovery"},"content":{"rendered":"

Developing a new medicine is a long, costly, and complex process with significant risk. Despite decades of scientific advances and billions of dollars invested in research and development, around 90% of drugs entering clinical trials<\/a> ultimately fail. Often, this is because the preclinical models used to predict how a drug will behave in humans are not physiologically relevant enough to accurately predict efficacy and safety.
For years, animal models have played an important role in preclinical drug development, helping researchers understand disease mechanisms and evaluate potential safety risks. Although approaches have advanced considerably, traditional models still cannot fully capture the complexity of human physiology. As a result, researchers are increasingly shifting their focus away from refining animal models and towards developing more predictive human-relevant systems instead.<\/p>\n

This shift in thinking has given rise to New Approach Methodologies<\/a> (NAMs)\u2014a broad and evolving collection of technologies designed to improve how we study disease, predict the safety and efficacy of new medicines, and assess other regulated products.
Rather than relying on a single innovation, NAMs bring together advances in stem cell biology, organoids<\/a>, organ-on-a-chip<\/a> technologies, and artificial intelligence (AI), creating a more human-relevant approach to drug discovery.
According to Dr. Joseph C. Wu<\/a>, director of the Stanford Cardiovascular Institute and co-founder of Greenstone Biosciences, the promise of NAMs relates to their ability to work together.
\u201cThe emerging paradigm is not replacement, but integration,\u201d Wu explained. \u201cIn silico approaches enable efficient prioritization and design, while experimental NAMs provide mechanistic validation and translational grounding. Together, this complementary framework supports a more predictive, scalable, and human-centric drug discovery pipeline.\u201d<\/p>\n

NAMs: A growing toolkit for human-relevant research<\/p>\n

The term NAM is used to describe a range of technologies, each designed to answer different scientific questions.
Stem cell-derived models, including those generated from induced pluripotent stem cells (iPSCs), allow researchers to grow specific cell types (e.g., cardiac, brain, or liver cells) in the laboratory. Organoids take this a step further by enabling these cells to self-organize into miniature, three-dimensional tissues that recapitulate key aspects of organ structure and function. Meanwhile, organ-on-a-chip and microphysiological systems<\/a> can be used to recreate the physical and mechanical environment of living tissues, allowing scientists to study how the cells respond under more physiologically realistic conditions.
More and more, these experimental systems are being paired with AI-driven computational models capable of analyzing huge datasets, identifying patterns, and predicting how potential therapies may perform before they are physically tested in a lab.<\/p>\n

Importantly, these technologies are not competing to replace one another. They should be seen as an interconnected toolkit, where the strengths of one approach balance the limitations of another.
For example, AI is transforming the earliest stages of drug discovery. Computational models can rapidly search vast chemical libraries, identify promising drug targets<\/a>, and predict potential toxicities in a fraction of the time required by traditional laboratory approaches.
\u201cIn silico approaches are delivering the greatest value in early and intermediate stages of the drug discovery pipeline<\/a>, particularly in target identification<\/a>, molecular design, and safety prediction, where computational scalability accelerates timelines and reduces cost,\u201d said Wu. \u201cBy replacing physical throughput with computational exploration, these methods enable faster interrogation of disease biology and chemical space at a human-centric and population scale.\u201d
Wu emphasized that computational approaches also expand access to previously underexplored chemical spaces, for example, by enabling systematic mining of the hidden diversity within natural products.<\/p>\n

Beyond generating new experimental data, AI-driven approaches can also help researchers revisit existing clinical and real-world datasets. By analyzing these large datasets retrospectively, computational models may uncover population-specific differences in drug response that were previously overlooked or difficult to detect in clinical trials.<\/p>\n

However, computational modeling is only one piece of the puzzle. \u201cLater-stage translation and mechanistic validation remain more dependent on experimental systems,\u201d Wu noted.<\/p>\n

He continues: \u201cModel performance is constrained by data quality, representativeness, and standardization, and predictions often lack mechanistic resolution. Human-centric systems, such as iPSC-derived models, organoids, and organ-on-chip platforms, are therefore essential to validate biological effects and assess functional outcomes.\u201d<\/p>\n

Capturing the diversity of human biology with NAMs
\nOne of the most significant advantages of NAMs is their ability to better reflect the diversity found across human populations.
Traditional preclinical models often struggle to account for the genetic, biological, and environmental differences that influence how individual patients respond to medicines. For example, variants in
\ngenes involved in the metabolism<\/a>
\n or transport of a drug can alter an individual’s risk of adverse events or treatment failure, while sex-based differences can also affect treatment response. However,
\nthese biases are not confined to animal studies<\/a>
\n.<\/p>\n

As awareness of these limitations grows, researchers are making a conscious effort to ensure ex vivo and tissue slice models better reflect biological diversity (i.e., male and female sources).<\/p>\n

<\/p>\n

Human-derived models help to address this challenge by incorporating cells from diverse donors and combining them with computational approaches capable of analyzing variation at scale.<\/p>\n

Wu believes this could fundamentally change how researchers evaluate the safety and efficacy of drugs. \u201cThe integration of
\nin silico
\n models with stem cell- and organoid-based NAMs creates a powerful opportunity to systematically capture human diversity across factors such as genetic background, age, sex, and environmental exposures,\u201d he said. \u201cThis combined framework enables both retrospective and prospective evaluation of drug efficacy and safety in ways that were not previously feasible with traditional models.\u201d \n<\/p>\n

For example, in drug-induced cardiotoxicity research, combinatorial NAM platforms can be designed to assess specific safety risks, helping researchers understand different mechanisms of toxicity, including arrhythmia, fibrosis, and mitochondrial dysfunction, while accounting for differences between individuals. \n<\/p>\n

Large-scale iPSC biobanks<\/a>
\n and emerging concepts such as \u201ccell villages\u201d and \u201corganoid villages\u201d\u2014collections of different human cell types or cell models grown and studied together in a coordinated system\u2014may allow researchers to simulate how entire patient populations respond to therapies before clinical trials even begin, helping identify individuals who are most likely to benefit while reducing the risk of unexpected adverse events. \n<\/p>\n

Building confidence in new approach methodologies<\/p>\n

Despite their potential and growing popularity, NAMs are not \u201cflawless\u201d. <\/p>\n

Many experimental systems still require improvements in maturity and reproducibility, while AI models remain heavily dependent on the quality and diversity of the data used to train them. At the same time, regulators need robust evidence demonstrating that these approaches can consistently deliver reliable and clinically meaningful results.
According to Wu, collaboration is key to helping overcome these challenges.<\/p>\n

\u201cThe most pressing bottlenecks to translational adoption fall into three main categories: biological fidelity, data quality, and regulatory confidence,\u201d he explained. \u201cStrengthening trust will depend on demonstrating robustness and transparency across academia, industry, clinicians, the public, regulatory agencies, and patient advocacy groups, without slowing innovation.\u201d<\/p>\n

Rather than replacing established approaches overnight, Wu believes the field should focus on developing standardized validation frameworks and integrating complementary technologies that can collectively provide stronger evidence for decision making.
Realizing the potential of NAMs will require closer integration of AI-driven predictions with cellular and organoid models, supported by standardized methods, FAIR data sharing<\/a>, and clear regulatory pathways. He adds that automation, scalability, and global collaboration will be key to moving NAMs beyond specialist settings and towards broader adoption.<\/p>\n

\u00a0A new generation of drug discovery<\/p>\n

As NAMs continue to advance, their impact is likely to extend far beyond the laboratory. Realizing their full potential will require new approaches to training researchers and closer collaboration between academia, industry, and regulatory agencies.
\u201cCurrent academic and industry training models have laid a strong foundation, but they are not yet fully aligned with a future where AI, organoids, and regulatory science converge,\u201d said Wu. \u201cRather than replacing existing models, we probably need to evolve toward a new kind of drug discovery science.\u201d<\/p>\n

NAMs help researchers build more biologically relevant models that better reflect the complexity and diversity of human biology, and they also offer a unique opportunity to democratize drug discovery if they are deliberately designed with accessibility, standardization, and global collaboration in mind.<\/p>\n

However, Wu cautioned that these technologies \u201care likely to carry the risk of becoming concentrated in well-resourced institutions, particularly given the technical complexity, infrastructure demands, and data requirements involved.\u201d<\/p>\n

\u201cUltimately, the goal is not just to train more scientists, but to build a multidisciplinary workforce embedded in collaborative ecosystems. By aligning technical innovation with regulatory insight and societal responsibility through public\u2013private partnership-driven frameworks, we can empower a new generation of scientists capable of translating NAMs-driven advances into practical, trustworthy, and globally accessible solutions,\u201d concluded Wu.<\/p>\n

Further reading<\/p>\n

For readers interested in the role of new approach methodologies in drug discovery, the following articles provide additional perspectives:<\/p>\n

Wu X, Wu MA, Zou J, Kleinstreuer N, Wu JC. Reimagining human-centric drug development with new approach methodologies. Science. 2026;392(6796):371-378. doi: 10.1126\/science.aeb0045<\/a>
Liu W, Pang PD, Wu CA, Tagle D, Wu JC. New approach methodologies for drug discovery. Cell. 2026;189(7):1877-1903. doi: 10.1016\/j.cell.2026.02.012<\/a>
Herron TJ, Br\u00fcning-Richardson A, Gough JE, et al. Alternatives to animal testing are the future \u2014 it\u2019s time that journals, funders and scientists embrace them. Nature. 2025;646(8086):799-801. doi: 10.1038\/d41586-025-03344-6<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"Developing a new medicine is a long, costly, and complex process with significant risk. Despite decades of scientific…\n","protected":false},"author":2,"featured_media":546620,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[7],"tags":[61,60,82],"class_list":["post-546619","post","type-post","status-publish","format-standard","has-post-thumbnail","category-science","tag-ie","tag-ireland","tag-science"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/posts\/546619","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/comments?post=546619"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/posts\/546619\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/media\/546620"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/media?parent=546619"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/categories?post=546619"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/tags?post=546619"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}