What are NAMs (new approach methodologies)?

NAMs are non-animal methods, systems, and strategies that can help answer key questions about safety, efficacy, and mechanisms of action. They include advanced in vitro models, ex vivo models that use intact human tissues outside the body, in silico models, and integrated testing strategies.

NAMs in Cancer Drug Discovery & Development

Q: Why are NAMs important in cancer drug discovery and development?

In oncology, many candidates that look promising in traditional models do not succeed in human trials. NAMs help address this gap by using human-relevant data, including human cells, tissues, and human-derived data, to evaluate how tumors respond to therapies earlier in the development process.

Q: What kinds of NAMs are used in cancer research?

Common NAMs in cancer research include advanced in vitro models such as spheroids and organoids, microfluidic and lab-on-a-chip systems, ex vivo tumor models that use intact human tumor tissue, 3D tissue-engineered models including 3D bioprinting, and in silico models including AI and machine learning approaches.

Q: What is the role of tissue engineering and ex vivo tumor models as NAMs in oncology?

Tissue engineering and ex vivo tumor models bring real human tumor biology into controlled, testable systems. Emerging ex vivo tumor models use intact pieces of patient tumor tissue outside the body, preserving key features such as 3D architecture and aspects of the tumor microenvironment, while tissue engineering creates structured 3D microtissues that recreate relevant elements of human tumors.

Q: How do in silico models and AI/ML relate to NAMs in oncology?

In silico models and AI or machine learning approaches play a complementary, predictive role by turning experimental and clinical data into simulations that anticipate tumor behavior and treatment response. Within a NAMs framework, these models are especially powerful when anchored in human-relevant data generated by advanced in vitro systems, tissue-engineered microtissues, and ex vivo tumor models.

Q: How does CerFlux participate in the NAMs ecosystem?

CerFlux focuses on human-relevant cancer models that use patient tumor tissue and tissue-engineered microtissues as the experimental backbone for ex vivo and in silico decision-making. Platforms such as the Bioprinted Ex vivo Slice Tissue (BEST) model are being designed to support drug response prediction tools like PEER and PROPHET by generating structured, quantitative response data from human-relevant systems rather than relying primarily on animal studies.

NAMs is a shared acronym used by regulators and research agencies to describe human-relevant, non-animal approaches that can answer important questions about safety, efficacy, and mechanisms of action of therapies.

Over time, however, the taxonomy around NAMs has evolved somewhat independently across different organizations. As a result, the same acronym has been defined slightly differently by different agencies at different points in time. For example, as “new approach methods” by the U.S. Food and Drug Administration (FDA), “novel alternative methods” by the U.S. National Institutes of Health (NIH), and “new approach methodologies” by the European Medicines Agency (EMA).

However, despite some differences in wording, all of these definitions point to the same core idea: using innovative, human-relevant, non-animal methods to generate better evidence for decision-making in oncology research, regulation, and drug development. On this page, and broadly at CerFlux, we use “NAMs” as an umbrella term that encompasses these related concepts and focus specifically on how NAMs apply to cancer drug discovery and development.

What are NAMs?

NAMs are methods, systems, and strategies that do not rely on traditional animal models to answer important scientific and regulatory questions. NAMs can include:

Advanced in vitro systems (such as complex 2D and 3D cell models)
Patient-specific and patient-derived ex vivo models that use intact human tissues (or close derivatives) outside the body
AI/ML and other in silico models built from human and experimental data
Integrated testing strategies that combine multiple non-animal methods

The goal of NAMs is not just to avoid animal use, but to generate data that is more relevant to human biology so that research and scientific findings can translate into improved outcomes for human patients.

Why are NAMs important in cancer drug discovery and development?

Cancer drug development is difficult, expensive, and slow. Many oncology candidates that look effective in early animal studies fail to show benefit in human trials. One reason is that traditional models, including animal models, do not capture the complexity of human tumors, their microenvironment, or patient-to-patient variability.

NAMs help address this gap by using human tumors and close derivatives as proxies to evaluate how patient tumors would respond to therapies. In oncology, where it is virtually impossible to mimic human tumor characteristics in animal models, this can make early decisions more predictive, reduce late-stage failures, and focus resources on treatments that are more likely to help patients.

How do traditional animal models fall short in oncology?

Animal models have been essential and fundamental to cancer research for the better part of a century, and they will continue to provide valuable insights. However, when it comes to evaluating treatments, they have limitations:

Tumors in animals cannot fully mimic key aspects of human tumors including growth kinetics and microenvironments.
Drug distribution, uptake, efficacy, and toxicity can differ significantly between animals and humans.
Some targeted therapies and immunotherapies depend on human-specific pathways that are hard to model in animals.

As a result, a treatment can appear promising in animals but fail to show benefit or safety in humans. NAMs are designed to complement and, where appropriate, replace animal models with more human-relevant systems.

What kinds of NAMs are used in cancer research?

In cancer research and drug development, NAMs span several categories:

in vitro models: These typically involve cancer cell and tissue cultures, including 2D monolayers, co-cultures, and advanced systems such as spheroids and organoids. These can be developed using patient tumor cells for personalized medicine applications or using well-characterized cell-lines for enhanced rigor and reproducibility for evaluating mechanisms of action.
Microfluidics and lab-chips: These are specialized in vitro platforms with dynamic elements to model perfusion. Think of these as computer chip and electronic circuits except that electric wiring is replaced with tiny channels for fluid streams.
ex vivo models: These involve intact pieces of human tumor tissue, such as fresh biopsies or surgical specimens, maintained and tested outside the body to characterize or preserve architecture and cell–cell interactions. Some approaches use close derivatives of patient tumor tissue for higher throughput applications.
3D tissue-engineered models: These are specialized in vitro and ex vivo platforms that use fluid handling, bioprinting, and other tissue engineering techniques to generate microtissues that recreate aspects of tumors and their microenvironment.
AI/ML and in silico models: Computational models, including simulations and AI/ML trained on experimental and clinical data.
Cancer supermodels: While each platform offers unique capabilities, combining best-in-class model platforms is poised to offer the most comprehensive and rigorous understanding of the disease itself and in designing new, more effective therapies.

Do NAMs completely replace animal studies?

In most current workflows, NAMs complement rather than instantly replace animal studies. NAMs can:

Provide earlier, human-relevant data about how tumors respond to treatments
Reduce the number of animal studies needed
Help refine which animal studies are still necessary and how they are designed

Over time, as NAMs are validated and accepted by regulators and the scientific community, they can take on a larger share of the evidence needed to move a cancer therapy forward, especially when they are directly anchored in human tumor biology.

How can NAMs make oncology drug development more predictive and efficient?

NAMs help make cancer drug development more predictive and efficient by:

Bringing human-relevant data earlier into the pipeline
Allowing side-by-side comparison of multiple therapies or combinations in controlled, reproducible systems
Generating rich datasets that can be used with AI/ML to identify patterns and refine hypotheses
Helping prioritize which candidates move into costly and time-consuming clinical trials

When NAMs are used strategically, they can reduce late-stage failures, focus resources on the most promising therapies, and support more translation-driven development decisions.

What is the role of tissue engineering and ex vivo models as NAMs in oncology?

Tissue engineering and ex vivo tumor models play a central role as NAMs in oncology because they bring real human tumor biology into controlled, testable systems. Emerging ex vivo tumor models use intact pieces of patient tumor tissue outside the body, preserving key features such as 3D architecture, cell–cell interactions, and key aspects of the tumor microenvironment that act as gatekeepers to therapy. Tissue engineering builds on this to compensate for limited amount of patient tissue by creating structured 3D microtissues using a variety of methods including 3D bioprinting that can recreate relevant elements of human tumors.

Together, these approaches make it possible to expose human-relevant tumor tissues to multiple therapies or combinations, measure direct responses, and generate rich data without relying on animal studies. In fact, many of the capabilities afforded by these tissue-engineered models are simply not possible to achieve in animal models such as tuning the extracellular matrix of the tumor microenvironment. As NAMs, tissue-engineered and ex vivo tumor models help bridge the gap between simplified in vitro monolayer assays and complex clinical trials, improving the relevance of preclinical decisions in oncology.

How do in silico models and AI/ML relate to NAMs in oncology?

The broader class of in silico models and AI/ML play a complementary, predictive role in oncology by turning experimental and clinical data into simulations that anticipate tumor behavior and treatment response. As NAMs, these emerging models can be especially powerful when anchored in human-relevant data generated specifically for AI/ML drug response prediction rather than animal models - or even real world data - that is disproportionately skewed toward failed trials and therapies.

Thus, from a NAMs perspective, the greatest potential of in silico models lies in using data generated by advanced in vitro systems, tissue-engineered human-relevant microtissues, and ex vivo tumor models to train, test, and refine computational models. Together, this approach makes it possible to “rehearse” drug development decisions in silico: prioritizing which candidates or combinations to test, exploring dosing strategies, and identifying which questions truly require new experiments or clinical studies. In the NAMs framework, human-relevant experimental models would generate richer data to enable in silico models to provide the predictive layer on top of that data that is patient-informed and translatable to improved patient outcomes.

How does CerFlux participate in the NAMs ecosystem?

CerFlux engaged in NAMs years before “NAMs” became a widely used term. From the beginning, the principal driver for CerFlux was the inadequacy of human-relevant drug response prediction tools in oncology. We started at a time when mouse models were still mandatory for most oncology clinical trials and, as such, the de facto gateway for new cancer drugs. Our focus has always been on developing approaches that are closer to human tumors so that we can match treatments to tumors for a more personalized, precision approach to treating cancer.

Today, working alongside our partners and collaborators, CerFlux continues to play a pioneering role in the NAMs ecosystem by developing human-relevant cancer models that use patient tumor tissue and tissue-engineered microtissues as the experimental backbone for ex vivo and in silico decision-making. Platforms such as our Bioprinted ex vivo Slice Tissue (BEST) model are being designed to support drug response prediction tools like PEER® and PROPHET™ by generating structured, quantitative response data from human-relevant systems rather than animal studies.

In practice, this means we use NAMs to bridge the serious gaps along molecule-to-medicine journey for promising cancer therapies. We do this by evaluating how individual tumors respond to multiple therapies outside the body, creating AI/ML-ready datasets, and working with partners to make preclinical decisions more predictive and patient-informed. Our goal is not to position NAMs as a slogan or buzzword, but to demonstrate, through our models, publications, and collaborations, that human-relevant, non-animal methods can make cancer drug discovery and development more adaptive, efficient, and aligned with real patient needs.