The past year has signaled a pivotal shift in the adoption of New Approach Methodologies (NAM) in preclinical drug development. These human-relevant approaches are transitioning from being experimental curiosities to becoming mainstream practices, driven by a growing body of evidence and significant regulatory reforms. There is increasing pressure on regulators to assist drug developers in moving beyond animal testing. Consequently, the global embrace of NAM is recognizing that these advanced, animal-free approaches can lead to new therapeutic options for patients more swiftly and with enhanced predictive accuracy.
The Status Quo in Drug Discovery and Development
Animal research has traditionally been integral to drug discovery, allowing researchers to assess drug behavior within a living system prior to human trials. However, with only one in ten drugs progressing to Phase I trials receiving regulatory approval, the gap between preclinical laboratories and clinical settings is stark. These drug failures are not only costly but can also lead to unnecessary animal use, spurred by inherent interspecies differences that diminish the predictive value of animal models.
Now more than ever, NAM must be seen as a critical aspect of modern drug development. Scientists, policymakers, regulators, and governments must adapt to this evolving landscape to ensure they can deliver the next generation of therapies.
Regulatory Changes Accelerate Adoption of NAM
The year 2025 witnessed unprecedented regulatory changes that have highlighted the NAM sector. Announcements from the U.S. FDA and the UK government’s roadmap for animal testing have markedly accelerated market momentum, reminiscent of the early growth of organ-on-a-chip (OOC) technology, which experienced growth rates of up to 40%. These developments have sparked increased interest among academic and industry scientists in these technologies. Researchers who previously overlooked NAM are starting to explore its potential, while those who were already considering it have gathered the data and confidence needed for deeper investments.
However, there remains significant work to be done, especially in raising awareness among researchers regarding where NAM adds the most value. Many organizations aim to integrate these methods as alternatives to animal models but are uncertain about how to deploy them effectively. NAM providers have a crucial role in education and outreach: demonstrating what is possible, the applications of NAM, and the types of data they can generate will foster wider adoption of these technologies.
New initiatives, such as the FDA STATUT Program (Innovative Science and Technology Approaches for New Drugs), are extremely valuable in raising awareness and understanding these bottlenecks. The program accepts submissions for the qualification of drug development tools (DDT) that fall outside existing DDT qualification programs, allowing NAM developers to build the necessary evidence base to strengthen regulatory confidence in emerging technologies.
It is also important to note that discussions continue between pharmaceutical companies and regulators regarding the types of NAM data that will be accepted in IND submissions. Until clarity is achieved, adoption will proceed slowly, despite evidence showing superior performance compared to traditional approaches. Moreover, much of the guidance available remains just that—guidance—and without stronger reasons to change, many are content to maintain the status quo. The upcoming years will be critical as we begin to see more INDs emerging. Once stakeholders observe the types of datasets utilized in regulatory submissions for OOC technologies and other NAM, it will become easier for others to follow suit.
Integrating AI and OOC Technologies for More Precise Preclinical Insights
AI-based approaches are rapidly becoming one of the hottest topics in drug discovery, with those in the field likely considering how to incorporate AI into their discovery pipelines. Technologies like OOC offer significant advantages here, as their data naturally feed into machine learning algorithms. These algorithms can then be leveraged to refine models, produce better outcomes, generate new potential leads, and facilitate validation.
For NAM providers, the next steps involve exploring how to maximize the use of AI: how to extract data from microphysiological systems (MPS) or advanced in vitro models, and subsequently apply computational modeling to translate these datasets more broadly to predict outcomes for larger patient populations. Some progress is already evident, but further advancements are likely in the coming years.
Accelerating the Search for New Modalities
NAM provides invaluable insights into the emerging modalities space; from gene and cell therapies to ADCs and PROTACs. One of the main challenges in developing these therapeutic products is that each class of molecules requires new, highly specific testing, largely due to validation difficulties stemming from a lack of existing tests. Ultimately, many of these modalities are stalled because they are more challenging to develop, with relatively few in clinical development and even fewer receiving regulatory approval. These challenges create substantial uncertainty in advanced therapies: many developers hesitate to progress therapeutic candidates without extensive preclinical data validating safety.
In many cases, animal models are ill-suited for these modalities, as they often target human-specific pathways or targets. Unsurprisingly, this is also where NAM is increasingly being utilized in regulatory submissions, likely to be the first area to witness widespread acceptance of NAM.
NAM Providers Excel in a Shifting Biotech Market
Amid market turbulence, biotechnology companies continue to struggle, highlighting the challenges of selling a startup asset without a solid, validated evidence base supporting its potential success. Coupled with limited funding available for many small biotech firms, there is increasing pressure on these companies to find new, more affordable ways to generate early-stage data that clearly demonstrate asset or enterprise value. This is where NAM is poised to play a significant role, often proving more cost-effective for many testing applications. OOC, in particular, has already demonstrated its value in several patent submissions and early regulatory applications—now it remains for more researchers to take the leap and leverage it as a springboard to propel their pipelines towards commercial success. NAM is already showing its worth in bridging the in vitro to in vivo translation gap, fostering informed decision-making earlier in the process, and ultimately reducing our reliance on animal studies when their use is not appropriate. As the life sciences sector faces economic and scientific pressures, NAM providers are uniquely positioned to collaborate with regulators, industry, and academia to accelerate their integration. If this momentum continues, the coming years could herald a fundamental shift in the manner in which preclinical research is conducted—creating a new drug development landscape that emphasizes more predictive, human-relevant science.
Photo: Petmal, Getty Images
